伍德里奇计量经济学英文版各章总结

第一篇：伍德里奇计量经济学英文版各章总结

CHAPTER 1 TEACHING NOTES You have substantial latitude about what to emphasize in Chapter 1.I find it useful to talk about the economics of crime example(Example 1.1)and the wage example(Example 1.2)so that students see, at the outset, that econometrics is linked to economic reasoning, even if the economics is not complicated theory.I like to familiarize students with the important data structures that empirical economists use, focusing primarily on cross-sectional and time series data sets, as these are what I cover in a first-semester course.It is probably a good idea to mention the growing importance of data sets that have both a cross-sectional and time dimension.I spend almost an entire lecture talking about the problems inherent in drawing causal inferences in the social sciences.I do this mostly through the agricultural yield, return to education, and crime examples.These examples also contrast experimental and nonexperimental(observational)data.Students studying business and finance tend to find the term structure of interest rates example more relevant, although the issue there is testing the implication of a simple theory, as opposed to inferring causality.I have found that spending time talking about these examples, in place of a formal review of probability and statistics, is more successful(and more enjoyable for the students and me).CHAPTER 2 TEACHING NOTES This is the chapter where I expect students to follow most, if not all, of the algebraic derivations.In class I like to derive at least the unbiasedness of the OLS slope coefficient, and usually I

derive the variance.At a minimum, I talk about the factors affecting the variance.To simplify the notation, after I emphasize the assumptions in the population model, and assume random sampling, I just condition on the values of the explanatory variables in the sample.Technically, this is justified by random sampling because, for example, E(ui|x1,x2,…,xn)= E(ui|xi)by independent sampling.I find that students are able to focus on the key assumption SLR.4 and subsequently take my word about how conditioning on the independent variables in the sample is harmless.(If you prefer, the appendix to Chapter 3 does the conditioning argument carefully.)Because statistical inference is no more difficult in multiple regression than in simple regression, I postpone inference until Chapter 4.(This reduces redundancy and allows you to focus on the interpretive differences between simple and multiple regression.)You might notice how, compared with most other texts, I use relatively few assumptions to derive the unbiasedness of the OLS slope estimator, followed by the formula for its variance.This is because I do not introduce redundant or unnecessary assumptions.For example, once SLR.4 is assumed, nothing further about the relationship between u and x is needed to obtain the unbiasedness of OLS under random sampling.CHAPTER 3 1 TEACHING NOTES For undergraduates, I do not work through most of the derivations in this chapter, at least not in detail.Rather, I focus on interpreting the assumptions, which mostly concern the population.Other than random sampling, the only assumption that involves more than population considerations is the assumption about no perfect collinearity, where the possibility of perfect collinearity in the sample(even if it does not occur in the population)should be touched on.The more important issue is perfect collinearity in the population, but this is fairly easy to dispense with via examples.These come from my experiences with the kinds of model specification issues that beginners have trouble with.The comparison of simple and multiple regression estimates – based on the particular sample at hand, as opposed to their statistical properties – usually makes a strong impression.Sometimes I do not bother with the “partialling out” interpretation of multiple regression.As far as statistical properties, notice how I treat the problem of including an irrelevant variable: no separate derivation is needed, as the result follows form Theorem 3.1.I do like to derive the omitted variable bias in the simple case.This is not much more difficult than showing unbiasedness of OLS in the simple regression case under the first four Gauss-Markov assumptions.It is important to get the students thinking about this problem early on, and before too many additional(unnecessary)assumptions have been introduced.I have intentionally kept the discussion of multicollinearity to a minimum.This partly indicates my bias, but it also reflects reality.It is, of course, very important for students to understand the potential consequences of having highly correlated independent variables.But this is often beyond our control, except that we can ask less of our multiple regression analysis.If two or more explanatory variables are highly correlated in the sample, we should not expect to precisely estimate their ceteris paribus effects in the population.I find extensive treatments of multicollinearity, where one “tests” or somehow “solves” the multicollinearity problem, to be misleading, at best.Even the organization of some texts gives the impression that imperfect multicollinearity is somehow a violation of the Gauss-Markov assumptions: they include multicollinearity in a chapter or part of the book devoted to “violation of the basic assumptions,” or something like that.I have noticed that master’s students who have had some undergraduate econometrics are often confused on the multicollinearity issue.It is very important that students not confuse multicollinearity among the included explanatory variables in a regression model with the bias caused by omitting an important variable.I do not prove the Gauss-Markov theorem.Instead, I emphasize its implications.Sometimes, and certainly for advanced beginners, I put a special case of Problem 3.12 on a midterm exam, where I make a particular choice for the function g(x).Rather than have the students directly compare the variances, they should 2 appeal to the Gauss-Markov theorem for the superiority of OLS over any other linear, unbiased estimator.CHAPTER 4 TEACHING NOTES

At the start of this chapter is good time to remind students that a specific error distribution played no role in the results of Chapter 3.That is because only the first two moments were derived under the full set of Gauss-Markov assumptions.Nevertheless, normality is needed to obtain exact normal sampling distributions(conditional on the explanatory variables).I emphasize that the full set of CLM assumptions are used in this chapter, but that in Chapter 5 we relax the normality assumption and still perform approximately valid inference.One could argue that the classical linear model results could be skipped entirely, and that only large-sample analysis is needed.But, from a practical perspective, students still need to know where the t distribution comes from because virtually all regression packages report t statistics and obtain p-values off of the t distribution.I then find it very easy to cover Chapter 5 quickly, by just saying we can drop normality and still use t statistics and the associated p-values as being approximately valid.Besides, occasionally students will have to analyze smaller data sets, especially if they do their own small surveys for a term project.It is crucial to emphasize that we test hypotheses about unknown population parameters.I tell my students that they will be punished if they write something like ˆ = 0 on an exam or, even worse, H0:.632 = 0.H0:1One useful feature of Chapter 4 is its illustration of how to rewrite a population model so that it contains the parameter of interest in testing a single restriction.I find this is easier, both theoretically and practically, than computing variances that can, in some cases, depend on numerous covariance terms.The example of testing equality of the return to two-and four-year colleges illustrates the basic method, and shows that the respecified model can have a useful interpretation.Of course, some statistical packages now provide a standard error for linear combinations of estimates with a simple command, and that should be taught, too.One can use an F test for single linear restrictions on multiple parameters, but this is less transparent than a t test and does not immediately produce the standard error needed for a confidence interval or for testing a one-sided alternative.The trick of rewriting the population model is useful in several instances, including obtaining confidence intervals for predictions in Chapter 6, as well as for obtaining confidence intervals for marginal effects in models with interactions(also in Chapter 6).The major league baseball player salary example illustrates the difference between individual and joint significance when explanatory variables(rbisyr and hrunsyr in this case)are highly correlated.I tend to emphasize the R-squared form of the F statistic because, in practice, it is applicable a large percentage of the time, and it is much more readily computed.I do regret that this example is biased toward students in countries where baseball is played.Still, it is one of the better examples of multicollinearity that I have come across, and students of all backgrounds seem to get the point.CHAPTER 5 TEACHING NOTES Chapter 5 is short, but it is conceptually more difficult than the earlier chapters, primarily because it requires some knowledge of asymptotic properties of estimators.In class, I give a brief, heuristic description of consistency and asymptotic normality before stating the consistency and asymptotic normality of OLS.(Conveniently, the same assumptions that work for finite sample analysis work for asymptotic analysis.)More advanced students can follow the proof of consistency of the slope coefficient in the bivariate regression case.Section E.4 contains a full matrix treatment of asymptotic analysis appropriate for a master’s level course.An explicit illustration of what happens to standard errors as the sample size grows emphasizes the importance of having a larger sample.I do not usually cover the LM statistic in a first-semester course, and I only briefly mention the asymptotic efficiency result.Without full use of matrix algebra combined with limit theorems for vectors and matrices, it is very difficult to prove asymptotic efficiency of OLS.I think the conclusions of this chapter are important for students to know, even though they may not fully grasp the details.On exams I usually include true-false type questions, with explanation, to test the students’ understanding of asymptotics.[For example: “In large samples we do not have to worry about omitted variable bias.”(False).Or “Even if the error term is not normally distributed, in large samples we can still compute approximately valid confidence intervals under the Gauss-Markov assumptions.”(True).]

CHAPTER 6 TEACHING NOTES I cover most of Chapter 6, but not all of the material in great detail.I use the example in Table 6.1 to quickly run through the effects of data scaling on the important OLS statistics.(Students should already have a feel for the effects of data scaling on the coefficients, fitting values, and R-squared because it is covered in Chapter 2.)At most, I briefly mention beta coefficients;if students have a need for them, they can read this subsection.The functional form material is important, and I spend some time on more complicated models involving logarithms, quadratics, and interactions.An important point for models with quadratics, and especially interactions, is that we need to evaluate the partial effect at interesting values of the explanatory variables.Often, zero is not an interesting value for an explanatory variable and is well outside the range in the sample.Using the methods from Chapter 4, it is easy to obtain confidence intervals for the effects at interesting x values.As far as goodness-of-fit, I only introduce the adjusted R-squared, as I think using a slew of goodness-of-fit measures to choose a model can be confusing to novices(and does not reflect empirical practice).It is important to discuss how, if we fixate on a high R-squared, we may wind up with a model that has no interesting ceteris paribus interpretation.I often have students and colleagues ask if there is a simple way to predict y when log(y)has been used as the dependent variable, and to obtain a goodness-of-fit measure for the log(y)model that can be compared with the usual R-squared obtained when y is the dependent variable.The methods described in Section 6.4 are easy to implement and, unlike other approaches, do not require normality.The section on prediction and residual analysis contains several important topics, including constructing prediction intervals.It is useful to see how much wider the prediction intervals are than the confidence interval for the conditional mean.I usually discuss some of the residual-analysis examples, as they have real-world applicability.CHAPTER 7 TEACHING NOTES

This is a fairly standard chapter on using qualitative information in regression analysis, although I try to emphasize examples with policy relevance(and only cross-sectional applications are included.).In allowing for different slopes, it is important, as in Chapter 6, to appropriately interpret the parameters and to decide whether they are of direct interest.For example, in the wage equation where the return to education is allowed to depend on gender, the coefficient on the female dummy variable is the wage differential between women and men at zero years of education.It is not surprising that we cannot estimate this very well, nor should we want to.In this particular example we would drop the interaction term because it is insignificant, but the issue of interpreting the parameters can arise in models where the interaction term is significant.In discussing the Chow test, I think it is important to discuss testing for differences in slope coefficients after allowing for an intercept difference.In many applications, a significant Chow statistic simply indicates intercept differences.(See the example in Section 7.4 on student-athlete GPAs in the text.)From a practical perspective, it is important to know whether the partial effects differ across groups or whether a constant differential is sufficient.I admit that an unconventional feature of this chapter is its introduction of the linear probability model.I cover the LPM here for several reasons.First, the LPM is being used more and more because it is easier to interpret than probit or logit models.Plus, once the proper parameter scalings are done for probit and logit, the estimated effects are often similar to the LPM partial effects near the mean or median values of the explanatory variables.The theoretical drawbacks of the LPM are often of secondary importance in practice.Computer Exercise C7.9 is a good one to illustrate that, even with over 9,000 observations, the LPM can deliver fitted values strictly between zero and one for all observations.If the LPM is not covered, many students will never know about using econometrics to explain qualitative outcomes.This would be especially unfortunate for students who might need to read an article where an LPM is used, or who might want to estimate an LPM for a term paper or senior thesis.Once they are introduced to purpose and interpretation of the LPM, along with its shortcomings, they can tackle nonlinear models on their own or in a subsequent course.A useful modification of the LPM estimated in equation(7.29)is to drop kidsge6(because it is not significant)and then define two dummy variables, one for kidslt6 equal to one and the other for kidslt6 at least two.These can be included in place of kidslt6(with no young children being the base group).This allows a diminishing marginal effect in an LPM.I was a bit surprised when a diminishing effect did not materialize.CHAPTER 8 TEACHING NOTES

This is a good place to remind students that homoskedasticity played no role in showing that OLS is unbiased for the parameters in the regression equation.In addition, you probably should mention that there is nothing wrong with the R-squared or adjusted R-squared as goodness-of-fit measures.The key is that these are estimates of the population R-squared, 1 – [Var(u)/Var(y)], where the variances are the unconditional variances in the population.The usual R-squared, and the adjusted version, consistently estimate the population R-squared whether or not Var(u|x)= Var(y|x)depends on x.Of course, heteroskedasticity causes the usual standard errors, t statistics, and F statistics to be invalid, even in large samples, with or without normality.By explicitly stating the homoskedasticity assumption as conditional on the explanatory variables that appear in the conditional mean, it is clear that only heteroskedasticity that depends on the explanatory variables in the model affects the validity of standard errors and test statistics.The version of the Breusch-Pagan test in the text, and the White test, are ideally suited for detecting forms of heteroskedasticity that invalidate inference obtained under homoskedasticity.If heteroskedasticity depends on an exogenous variable that does not also appear in the mean equation, this can be exploited in weighted least squares for efficiency, but only rarely is such a variable available.One case where such a variable is available is when an individual-level equation has been aggregated.I discuss this case in the text but I rarely have time to teach it.As I mention in the text, other traditional tests for heteroskedasticity, such as the Park and Glejser tests, do not directly test what we want, or add too many assumptions under the null.The Goldfeld-Quandt test only works when there is a natural way to order the data based on one independent variable.This is rare in practice, especially for cross-sectional applications.Some argue that weighted least squares estimation is a relic, and is no longer necessary given the availability of heteroskedasticity-robust standard errors and test statistics.While I am sympathetic to this argument, it presumes that we do not care much about efficiency.Even in large samples, the OLS estimates may not be precise enough to learn much about the population parameters.With substantial heteroskedasticity we might do better with weighted least squares, even if the weighting function is misspecified.As discussed in the text on pages 288-289, one can, and probably should, compute robust standard errors after weighted least squares.For asymptotic efficiency comparisons, these would be directly comparable to the heteroskedasiticity-robust standard errors for OLS.Weighted least squares estimation of the LPM is a nice example of feasible GLS, at least when all fitted values are in the unit interval.Interestingly, in the LPM examples in the text and the LPM computer exercises, the heteroskedasticity-robust standard errors often differ by only small amounts from the usual standard errors.However, in a couple of cases the differences are notable, as in Computer Exercise C8.7.CHAPTER 9 TEACHING NOTES

The coverage of RESET in this chapter recognizes that it is a test for neglected nonlinearities, and it should not be expected to be more than that.(Formally, it can be shown that if an omitted variable has a conditional mean that is linear in the included explanatory variables, RESET has no ability to detect the omitted variable.Interested readers may consult my chapter in Companion to Theoretical Econometrics, 2001, edited by Badi Baltagi.)I just teach students the F statistic version of the test.The Davidson-MacKinnon test can be useful for detecting functional form misspecification, especially when one has in mind a specific alternative, nonnested model.It has the advantage of always being a one degree of freedom test.I think the proxy variable material is important, but the main points can be made with Examples 9.3 and 9.4.The first shows that controlling for IQ can substantially change the estimated return to education, and the omitted ability bias is in the expected direction.Interestingly, education and ability do not appear to have an interactive effect.Example 9.4 is a nice example of how controlling for a previous value of the dependent variable – something that is often possible with survey and nonsurvey data – can greatly affect a policy conclusion.Computer Exercise 9.3 is also a good illustration of this method.I rarely get to teach the measurement error material, although the attenuation bias result for classical errors-in-variables is worth mentioning.The result on exogenous sample selection is easy to discuss, with more details given in Chapter 17.The effects of outliers can be illustrated using the examples.I think the infant mortality example, Example 9.10, is useful for illustrating how a single influential observation can have a large effect on the OLS estimates.With the growing importance of least absolute deviations, it makes sense to at least discuss the merits of LAD, at least in more advanced courses.Computer Exercise 9.9 is a good example to show how mean and median effects can be very different, even though there may not be “outliers” in the usual sense.CHAPTER 10 TEACHING NOTES

Because of its realism and its care in stating assumptions, this chapter puts a somewhat heavier burden on the instructor and student than traditional treatments of time series regression.Nevertheless, I think it is worth it.It is important that students learn that there are potential pitfalls inherent in using regression with time series data that are not present for cross-sectional applications.Trends, seasonality, and high persistence are ubiquitous in time series data.By this time, students should have a firm grasp of multiple regression mechanics and inference, and so you can focus on those features that make time series applications different from cross-sectional ones.I think it is useful to discuss static and finite distributed lag models at the same time, as these at least have a shot at satisfying the Gauss-Markov assumptions.Many interesting examples have distributed lag dynamics.In discussing the time series versions of the CLM assumptions, I rely mostly on intuition.The notion of strict exogeneity is easy to discuss in terms of feedback.It is also pretty apparent that, in many applications, there are likely to be some explanatory variables that are not strictly exogenous.What the student should know is that, to conclude that OLS is unbiased – as opposed to consistent – we need to assume a very strong form of exogeneity of the regressors.Chapter 11 shows that only contemporaneous exogeneity is needed for consistency.Although the text is careful in stating the assumptions, in class, after discussing strict exogeneity, I leave the conditioning on X implicit, especially when I discuss the no serial correlation assumption.As this is a new assumption I spend some time on it.(I also discuss why we did not need it for random sampling.)

Once the unbiasedness of OLS, the Gauss-Markov theorem, and the sampling distributions under the classical linear model assumptions have been covered – which can be done rather quickly – I focus on applications.Fortunately, the students already know about logarithms and dummy variables.I treat index numbers in this chapter because they arise in many time series examples.A novel feature of the text is the discussion of how to compute goodness-of-fit measures with a trending or seasonal dependent variable.While detrending or deseasonalizing y is hardly perfect(and does not work with integrated processes), it is better than simply reporting the very high R-squareds that often come with time series regressions with trending variables.CHAPTER 11 TEACHING NOTES

Much of the material in this chapter is usually postponed, or not covered at all, in an introductory course.However, as Chapter 10 indicates, the set of time series applications that satisfy all of the classical linear model assumptions might be very small.In my experience, spurious time series regressions are the hallmark of many student projects that use time series data.Therefore, students need to be alerted to the dangers of using highly persistent processes in time series regression equations.(Spurious regression problem and the notion of cointegration are covered in detail in Chapter 18.)

It is fairly easy to heuristically describe the difference between a weakly dependent process and an integrated process.Using the MA(1)and the stable AR(1)examples is usually sufficient.When the data are weakly dependent and the explanatory variables are contemporaneously exogenous, OLS is consistent.This result has many applications, including the stable AR(1)regression model.When we add the appropriate homoskedasticity and no serial correlation assumptions, the usual test statistics are asymptotically valid.The random walk process is a good example of a unit root(highly persistent)process.In a one-semester course, the issue comes down to whether or not to first difference the data before specifying the linear model.While unit root tests are covered in Chapter 18, just computing the first-order autocorrelation is often sufficient, perhaps after detrending.The examples in Section 11.3 illustrate how different first-difference results can be from estimating equations in levels.Section 11.4 is novel in an introductory text, and simply points out that, if a model is dynamically complete in a well-defined sense, it should not have serial correlation.Therefore, we need not worry about serial correlation when, say, we test the efficient market hypothesis.Section 11.5 further investigates the homoskedasticity assumption, and, in a time series context, emphasizes that what is contained in the explanatory variables determines what kind of heteroskedasticity is ruled out by the usual OLS inference.These two sections could be skipped without loss of continuity.CHAPTER 12 TEACHING NOTES

Most of this chapter deals with serial correlation, but it also explicitly considers heteroskedasticity in time series regressions.The first section allows a review of what assumptions were needed to obtain both finite sample and asymptotic results.Just as with heteroskedasticity, serial correlation itself does not invalidate R-squared.In fact, if the data are stationary and weakly dependent, R-squared and adjusted R-squared consistently estimate the population R-squared(which is well-defined under stationarity).Equation(12.4)is useful for explaining why the usual OLS standard errors are not generally valid with AR(1)serial correlation.It also provides a good starting point for discussing serial correlation-robust standard errors in Section 12.5.The subsection on serial correlation with lagged dependent variables is included to debunk the myth that OLS is always inconsistent with lagged dependent variables and serial correlation.I do not teach it to undergraduates, but I do to master’s students.9 Section 12.2 is somewhat untraditional in that it begins with an asymptotic t test for AR(1)serial correlation(under strict exogeneity of the regressors).It may seem heretical not to give the Durbin-Watson statistic its usual prominence, but I do believe the DW test is less useful than the t test.With nonstrictly exogenous regressors I cover only the regression form of Durbin’s test, as the h statistic is asymptotically equivalent and not always computable.Section 12.3, on GLS and FGLS estimation, is fairly standard, although I try to show how comparing OLS estimates and FGLS estimates is not so straightforward.Unfortunately, at the beginning level(and even beyond), it is difficult to choose a course of action when they are very different.I do not usually cover Section 12.5 in a first-semester course, but, because some econometrics packages routinely compute fully robust standard errors, students can be pointed to Section 12.5 if they need to learn something about what the corrections do.I do cover Section 12.5 for a master’s level course in applied econometrics(after the first-semester course).I also do not cover Section 12.6 in class;again, this is more to serve as a reference for more advanced students, particularly those with interests in finance.One important point is that ARCH is heteroskedasticity and not serial correlation, something that is confusing in many texts.If a model contains no serial correlation, the usual heteroskedasticity-robust statistics are valid.I have a brief subsection on correcting for a known form of heteroskedasticity and AR(1)errors in models with strictly exogenous regressors.CHAPTER 13 TEACHING NOTES

While this chapter falls under “Advanced Topics,” most of this chapter requires no more sophistication than the previous chapters.(In fact, I would argue that, with the possible exception of Section 13.5, this material is easier than some of the time series chapters.)

Pooling two or more independent cross sections is a straightforward extension of cross-sectional methods.Nothing new needs to be done in stating assumptions, except possibly mentioning that random sampling in each time period is sufficient.The practically important issue is allowing for different intercepts, and possibly different slopes, across time.The natural experiment material and extensions of the difference-in-differences estimator is widely applicable and, with the aid of the examples, easy to understand.Two years of panel data are often available, in which case differencing across time is a simple way of removing g unobserved heterogeneity.If you have covered Chapter 9, you might compare this with a regression in levels using the second year of data, but where a lagged dependent variable is included.(The second approach only requires collecting information on the dependent variable in a previous year.)These often give similar answers.Two years of panel data, collected before and after a policy change, can be very powerful for policy analysis.Having more than two periods of panel data causes slight complications in that the errors in the differenced equation may be serially correlated.(However, the traditional assumption that the errors in the original equation are serially uncorrelated is not always a good one.In other words, it is not always more appropriate to used fixed effects, as in Chapter 14, than first differencing.)With large N and relatively small T, a simple way to account for possible serial correlation after differencing is to compute standard errors that are robust to arbitrary serial correlation and hetero-skedasticity.Econometrics packages that do cluster analysis(such as Stata)often allow this by specifying each cross-sectional unit as its own cluster.CHAPTER 14 TEACHING NOTES

My preference is to view the fixed and random effects methods of estimation as applying to the same underlying unobserved effects model.The name “unobserved effect” is neutral to the issue of whether the time-constant effects should be treated as fixed parameters or random variables.With large N and relatively small T, it almost always makes sense to treat them as random variables, since we can just view the unobserved ai as being drawn from the population along with the observed variables.Especially for undergraduates and master’s students, it seems sensible to not raise the philosophical issues underlying the professional debate.In my mind, the key issue in most applications is whether the unobserved effect is correlated with the observed explanatory variables.The fixed effects transformation eliminates the unobserved effect entirely whereas the random effects transformation accounts for the serial correlation in the composite error via GLS.(Alternatively, the random effects transformation only eliminates a fraction of the unobserved effect.)As a practical matter, the fixed effects and random effects estimates are closer when T is large or when the variance of the unobserved effect is large relative to the variance of the idiosyncratic error.I think Example 14.4 is representative of what often happens in applications that apply pooled OLS, random effects, and fixed effects, at least on the estimates of the marriage and union wage premiums.The random effects estimates are below pooled OLS and the fixed effects estimates are below the random effects estimates.Choosing between the fixed effects transformation and first differencing is harder, although useful evidence can be obtained by testing for serial correlation in the first-difference estimation.If the AR(1)coefficient is significant and negative(say, less than .3, to pick a not quite arbitrary value), perhaps fixed effects is preferred.Matched pairs samples have been profitably used in recent economic applications, and differencing or random effects methods can be applied.In an equation such as(14.12), there is probably no need to allow a different intercept for each sister provided that the labeling of sisters is random.The different intercepts might be needed if a certain feature of a sister that is not included in the observed controls is used to determine the ordering.A statistically significant intercept in the differenced equation would be evidence of this.CHAPTER 15 TEACHING NOTES

When I wrote the first edition, I took the novel approach of introducing instrumental variables as a way of solving the omitted variable(or unobserved heterogeneity)problem.Traditionally, a student’s first exposure to IV methods comes by way of simultaneous equations models.Occasionally, IV is first seen as a method to solve the measurement error problem.I have even seen texts where the first appearance of IV methods is to obtain a consistent estimator in an AR(1)model with AR(1)serial correlation.The omitted variable problem is conceptually much easier than simultaneity, and stating the conditions needed for an IV to be valid in an omitted variable context is straightforward.Besides, most modern applications of IV have more of an unobserved heterogeneity motivation.A leading example is estimating the return to education when unobserved ability is in the error term.We are not thinking that education and wages are jointly determined;for the vast majority of people, education is completed before we begin collecting information on wages or salaries.Similarly, in studying the effects of attending a certain type of school on student performance, the choice of school is made and then we observe performance on a test.Again, we are primarily concerned with unobserved factors that affect performance and may be correlated with school choice;it is not an issue of simultaneity.The asymptotics underlying the simple IV estimator are no more difficult than for the OLS estimator in the bivariate regression model.Certainly consistency can be derived in class.It is also easy to demonstrate how, even just in terms of inconsistency, IV can be worse than OLS if the IV is not completely exogenous.At a minimum, it is important to always estimate the reduced form equation and test whether the IV is partially correlated with endogenous explanatory variable.The material on multicollinearity and 2SLS estimation is a direct extension of the OLS case.Using equation(15.43), it is easy to explain why multicollinearity is generally more of a problem with 2SLS estimation.Another conceptually straightforward application of IV is to solve the measurement error problem, although, because it requires two measures, it can be hard to implement in practice.Testing for endogeneity and testing any overidentification restrictions is something that should be covered in second semester courses.The tests are fairly easy to motivate and are very easy to implement.While I provide a treatment for time series applications in Section 15.7, I admit to having trouble finding compelling time series applications.These are likely to be found at a less aggregated level, where exogenous IVs have a chance of existing.(See also Chapter 16.)

CHAPTER 16 TEACHING NOTES

I spend some time in Section 16.1 trying to distinguish between good and inappropriate uses of SEMs.Naturally, this is partly determined by my taste, and many applications fall into a gray area.But students who are going to learn about SEMS should know that just because two(or more)variables are jointly determined does not mean that it is appropriate to specify and estimate an SEM.I have seen many bad applications of SEMs where no equation in the system can stand on its own with an interesting ceteris paribus interpretation.In most cases, the researcher either wanted to estimate a tradeoff between two variables, controlling for other factors – in which case OLS is appropriate – or should have been estimating what is(often pejoratively)called the “reduced form.”

The identification of a two-equation SEM in Section 16.3 is fairly standard except that I emphasize that identification is a feature of the population.(The early work on SEMs also had this emphasis.)Given the treatment of 2SLS in Chapter 15, the rank condition is easy to state(and test).Romer’s(1993)inflation and openness example is a nice example of using aggregate cross-sectional data.Purists may not like the labor supply example, but it has become common to view labor supply as being a two-tier decision.While there are different ways to model the two tiers, specifying a standard labor supply function conditional on working is not outside the realm of reasonable models.Section 16.5 begins by expressing doubts of the usefulness of SEMs for aggregate models such as those that are specified based on standard macroeconomic models.Such models raise all kinds of thorny issues;these are ignored in virtually all texts, where such models are still used to illustrate SEM applications.SEMs with panel data, which are covered in Section 16.6, are not covered in any other introductory text.Presumably, if you are teaching this material, it is to more advanced students in a second semester, perhaps even in a more applied course.Once students have seen first differencing or the within transformation, along with IV methods, they will find specifying and estimating models of the sort contained in Example 16.8 straightforward.Levitt’s example concerning prison populations is especially convincing because his instruments seem to be truly exogenous.CHAPTER 17 TEACHING NOTES I emphasize to the students that, first and foremost, the reason we use the probit and logit models is to obtain more reasonable functional forms for the response probability.Once we move to a nonlinear model with a fully specified conditional distribution, it makes sense to use the efficient estimation procedure, maximum likelihood.It is important to spend some time on interpreting probit and logit estimates.In particular, the students should know the rules-of-thumb for comparing probit, logit, and LPM estimates.Beginners sometimes mistakenly think that, because the probit and especially the logit estimates are much larger than the LPM estimates, the explanatory variables now have larger estimated effects on the response probabilities than in the LPM case.This may or may not be true.I view the Tobit model, when properly applied, as improving functional form for corner solution outcomes.In most cases it is wrong to view a Tobit application as a data-censoring problem(unless there is true data censoring in collecting the data or because of institutional constraints).For example, in using survey data to estimate the demand for a new product, say a safer pesticide to be used in farming, some farmers will demand zero at the going price, while some will demand positive pounds per acre.There is no data censoring here;some farmers find it optimal to use none of the new pesticide.The Tobit model provides more realistic functional forms for E(y|x)and E(y|y > 0,x)than a linear model for y.With the Tobit model, students may be tempted to compare the Tobit estimates with those from the linear model and conclude that the Tobit estimates imply larger effects for the independent variables.But, as with probit and logit, the Tobit estimates must be scaled down to be comparable with OLS estimates in a linear model.[See Equation(17.27);for an example, see Computer Exercise C17.3.]

Poisson regression with an exponential conditional mean is used primarily to improve over a linear functional form for E(y|x).The parameters are easy to interpret as semi-elasticities or elasticities.If the Poisson distributional assumption is correct, we can use the Poisson distribution compute probabilities, too.But over-dispersion is often present in count regression models, and standard errors and likelihood ratio statistics should be adjusted to reflect this.Some reviewers of the first edition complained about either the inclusion of this material or its location within the chapter.I think applications of count data models are on the rise: in microeconometric fields such as criminology, health economics, and industrial organization, many interesting response variables come in the form of counts.One suggestion was that Poisson regression should not come between the Tobit model in Section 17.2 and Section 17.4, on censored and truncated regression.In fact, I put the Poisson regression model between these two topics on purpose: I hope it helps emphasize that the material in Section 17.2 is purely about functional form, as is Poisson regression.Sections 17.4 and 17.5 deal with underlying linear models, but where there is a data-observability problem.Censored regression, truncated regression, and incidental truncation are used for missing data problems.Censored and truncated data sets usually result from sample design, as in duration analysis.Incidental truncation often arises from self-selection into a certain state, such as employment or participating in a training program.It is important to emphasize to students that the underlying models are classical linear models;if not for the missing data or sample selection problem, OLS would be the efficient estimation procedure.CHAPTER 18 TEACHING NOTES

Several of the topics in this chapter, including testing for unit roots and cointegration, are now staples of applied time series analysis.Instructors who like their course to be more time series oriented might cover this chapter after Chapter 12, if time permits.Or, the chapter can be used as a reference for ambitious students who wish to be versed in recent time series developments.The discussion of infinite distributed lag models, and in particular geometric DL and rational DL models, gives one particular interpretation of dynamic regression models.But one must emphasize that only under fairly restrictive assumptions on the serial correlation in the error of the infinite DL model does the dynamic regression consistently estimate the parameters in the lag distribution.Computer Exercise C18.1 provides a good illustration of how the GDL model, and a simple RDL model, can be too restrictive.Example 18.5 tests for cointegration between the general fertility rate and the value of the personal exemption.There is not much evidence of cointegration, which sheds further doubt on the regressions in levels that were used in Chapter 10.The error correction model for holding yields in Example 18.7 is likely to be of interest to students in finance.As a class project, or a term project for a student, it would be interesting to update the data to see if the error correction model is stable over time.The forecasting section is heavily oriented towards regression methods and, in particular, autoregressive models.These can be estimated using any econometrics package, and forecasts and mean absolute errors or root mean squared errors are easy to obtain.The interest rate data sets(for example, INTQRT.RAW)can be updated to do much more recent out-of-sample forecasting exercises.CHAPTER 19 TEACHING NOTES

This is a chapter that students should read if you have assigned them a term paper.I used to allow students to choose their own topics, but this is difficult in a first-semester course, and places a heavy burden on instructors or teaching assistants, or both.I now assign a common topic and provide a data set with about six weeks left in the term.The data set is cross-sectional(because I teach time series at the end of the course), and I provide guidelines of the kinds of questions students should try to answer.(For example, I might ask them to answer the following questions: Is there a marriage premium for NBA basketball players? If so, does it depend on race? Can the premium, if it exists, be explained by productivity differences?)The specifics are up to the students, and they are to craft a 10-to 15-page paper on their own.This gives them practice writing the results in a way that is easy-to-read, and forces them to interpret their findings.While leaving the topic to each student’s discretion is more interesting, I find that many students flounder with an open-ended assignment until it is too late.Naturally, for a second-semester course, or a senior seminar, students would be expected to design their own topic, collect their own data, and then write a more substantial term paper.15

第二篇：计量经济学课程总结

经过一个学期对计量经济学的学习，我收获了很多，也懂得了很多。通过以计量经济学为核心，以统计学，数学，经济学等学科为指导，辅助以一些软件的应用，从这些之中我都学到了很多知识。同时对这门课程有了新的认识，计量经济学对我们的生活很重要，它对我国经济的发展有重要的影响。

计量经济学对我们研究经济问题是很好的方法和理论。学习计量经济学给我印象和帮助最大的主要对EVIES软件的熟练操作与应用,初步投身于计量经济学，通过利用Eviews软件将所学到的计量知识进行实践，让我加深了对理论的理解和掌握，直观而充分地体会到老师课堂讲授内容的精华之所在。在实验过程中我们提高了手动操作软件、数量化分析与解决问题的能力，还可以培养我在处理实验经济问题的严谨的科学的态度，并且避免了课堂知识与实际应用的脱节。虽然在实验过程中出现了很多错误，但这些经验却锤炼了我们发现问题的眼光，丰富了我们分析问题的思路。

计量经济学的定义为：用数学方法探讨经济学可以从好几个方面着手，但任何一个方面都不能和计量经济学混为一谈。计量经济学与经济统计学绝非一码事;它也不同于我们所说的一般经济理论，尽管经济理论大部分具有一定的数量特征;计量经济学也不应视为数学应用于经济学的同义语。经验表明，统计学、经济理 论和数学这三者对于真正了解现代经济生活的数量关系来说，都是必要的，但本身并非是充分条件。三者结合起来，就是力量，这种结合便构成了计量经济学。克莱因(R.Klein)：“计量经济学已经在经济学科中居于最重要的地位”，“在大多数大学和学院中，计量经济学的讲授已经成为经济学课程表中最有权威的一部分”

计量经济学关心统计工具在经济问题与实证资料分析上的发展和应用，经济学理论提供对于经济现象逻辑一致的可能解释。因为人类行为和决策是复杂的过程，所以一个经济议题可能存在多种不同的解释理论。当研究者无法进行实验室的实验时，一个理论必须透过其预测与事实的比较来检验,计量经济学即为检验不同的理论和经济模型的估计提供统计工具。

在计量经济学一元线性回归模型，我认识到：变量间的关系及回归分析的基本概念，主要包括：

其次有一元线形回归模型的参数估计及其统计检验与应用，包括： 这个公式得给出，以及样本回归函数的随机形式。总的说来，这一节留给我印象最深刻的，便是根据样本回归函数SRF，估计总体回归函数PRF，即总体回归线与样本回归线之间的关系。除此以外，我也学会了参数的最大似然估计法语最小二乘法。对于最小二乘法，当从模型总体随机抽取n组样本观测值后，最合理的参数估计量应该使得模型能最好的拟合样本数据，而对于最大似然估计法，当从模型总体随机抽取n组样本观测值后，最合理的参数估计量应该使得从模型中抽取该n组样本观测值的概率最大。显然，这是从不同原理出发的两种参数估计方法。即：

1.一元回归模型：

关于拟合优度的检验，也就是检验模型对样本观测值的拟合程度。被解释变量Y的观测值围绕其均值的总离差平方和可分解为两个部分：一部分来自于回归线，另一部分来自于随机势力。所以，我们用来自回归线的回归平方和占Y的总离差的平方和的比例来判断样本回归线与样本观测值的拟合优度。这个比例，我们也较它可决系数，它的取值范围是0<=R2<=1。

关于变量的显著性检验，是要考察所选择的解释变量是否对被解释变量有显著的线性影响。所应用的方法是数理统计学中的假设检验。关于置信区间估计。当我们要判断样本参数的估计值在多大程度上可以“近似”的替代总体参数的真值，往往需要通过构造一个以样本参数的估计值为中心的“区间”，来考察它以多大的概率包含这真是的参数值。这样的方法就是我们所说的参数检验的置信区间估计。当我们希望缩小置信区间时，可以采用的方法有增大样本容量和提高模型的拟合优度。

2.多元回归模型

多元回归分析与一元回归分析的几点不同：

关于修正的可绝系数。我们可于发现，在样本容量一定的情况下，增加解释变量必定使得自由度减少，所以调整的思路是:将残差平方和与总离差平方和分别除以各自的自由度，以剔除变量个数对拟合优度的影响。这样就引出了我们这里说的调整的可绝系数。

关于对多个解释变量是否对被解释变量有显著线性影响关系的联合性F检验。F检验的思想来自于总离差平方和的分解式：TSS=ESS+RSS。通过比较F值与临界值的大小来判定原方程总体上的线性关系是否显著成立。计量经济学是一门比较难的课程，其中涉及大量的公式，不容易理解且需要大量的运算，其中需要很好的数学基础、统计基础和自己的分析思考能力，以及良好的计量软件应用能力，所以在学习的过程中我遇到了很多困难。例如异方差的实验，异方差通常发生于横截面数据中，一般是有解释变量的方差与随机误差项的方差成比例。要发现这一问题，我们学习了很多检验，包括park test，Goldfeld-Quant test，White test等。要纠正异方差，常用的方法是WLS，通过对数据的处理能够有效消除异方差的问题。自相关的问题一般见于时间序列数据中，一阶序列相关是指当前的误差项与以前的误差项线性相关。在发生自相关的情况下，我们在进行变量的显著性检验时更倾向于拒绝虚拟假设。发现一阶自相关问题的最重要检验是Durbin-Watson test，这一检验的特点是存在未决区域。纠正自相关的问题，我们学会了GLS和Cochrane-Orcutt迭代法，并在计算机应用中学习了其操作，受益匪浅。但通过这次的实验，我对课上所学的最小二乘法有了进一步的理解，在掌握理论知识的同时，将其与实际的经济问题联系起来。

在目前的学术现状下，要求研究者必须掌握计量的研究方法，这是实证研究最好的工具。用计量的工具，我们才能够把经济现象肢解开来，找到其中的脉络，进而分析得更加清晰。

第三篇：各章习题总结

范围管理

8、工作分解结构中的每一项都被标以一个独特的标示符，标示符的名称是什么? A、质量检测标示符 B、帐目图表

C、项目活动编码

D、帐目编码

9、编制项目范围说明书时不需要包含以下哪项? A、成本/利益分析 B、项目历史 C、项目可交付成果 D、可测量的目标

11、范围说明是重要的，因为范围说明 A、为制定未来项目的决策提供依据 B、提供了项目的简洁概要 C、替项目干系人批准项目 D、提供衡量项目成本的标准

14、为了有效的管理项目，应该将工作分解为小块，以下各项中哪项不能说明任务应该分 解到什么程度? A、可以在80 小时以下完成 B、不能再进一步进行逻辑细分了 C、可由一个人完成

D、可以进行实际估算

17、客户要求进行范围变更。为了分析变更对项目的影响，项目经理应该回顾工作分解结 构、变更请求、范围管理计划和‐‐‐‐‐‐‐? A、绩效报告 B、职责分配矩阵 C、帕累托图

D、蒙特卡洛模拟

20.在项团队会议上，一个小组成员建议扩大工作范围，他的建议已经超越了项目章

程中的范围。这时，项目经理指出项目团队应该集中精力完成仅限于需要完成的所有工作。这是一个什么样的例子? A、范围定义 B、范围管理 C、项目章程

D、范围分解

22、创建工作分解结构的过程可以产生什么? A、项目进度计划 B、小组外购

C、项目完工日期

D、风险清单

23、项目经理可以使用‐‐‐‐‐‐来保证项目团队清楚的了解到他们的每一项任务包含的工作。

A、项目工作范围 B、项目章程 C、WBS 词典

D、风险管理计划

30、以下哪一项工具或技术用于项目启动? A、确认替代方案 B、配置管理 C、决策模式 D、分解

35、项目章程最少应该：

A、描述项目经理和职能经理的职责和权利 B、探讨项目的风险和限制以及针对这些问题的计划 C、指定项目的组织结构 D、说明执行组织的商业目标 40 以下哪项不是项目启动的输入? A、产品或服务说明 B、组强战略计划 C、项目筛选计划

D、项目章程

42、项目章程应该由谁发布? A、项目经理

B、执行组织的领导 C、项目外的一名经理

D、项目发起人

47.上个星期你还舒舒服服地在海边休假，今天你却不得不埋头于工作。有个项目经理的位置目前空缺，因为前任经理决定退休并且要在阿肯色州开办一个农场，而你接管了这个项

目，现在要检查一堆关于这个项目的范围变更请求。为了评估这个项目将在什么程度上变 更，你需要将这些变更要求跟哪一个项目文件的要求作比较? A、范围说明

B、工作分解结构 C、项目计划 D、管理计划范围

系统的维护不算在项目的生命周期中

53.公司是一个鸡肉食品公司，目前正在实施一个项目，目的是完全消除产品中沙门氏菌的 威胁。你是该项目的项目经理。你已经完成了项目的构思阶段。构思阶段的成果是： A、项目计划 B、工作说明 C、项目章程

D、资源电子数据表

62.你在负责管理一个视频游戏的项目。上个月客户已经签署项目需求说明和范围说明。但 是现在她提出了一项范围变更要求。她希望把这个游戏做成一种电视和电脑上都能玩的互 动游戏。这种范围变更至少会表现在哪一个方面? A、修改工作分解结构已经确定的项目范围 B、导致所有项目基线的变更 C、需要对成本 时间 质量以及其他目标进行调整

D、得到一个经验教训

65.在项目生命周期的概念阶段，管理层表示希望每个新项目的效益应超过开发成本。这 是以下什么的例子： A、假定

B、限制条件

C、通过约束优化选择项目

D、一个技术要求

68.你所在的公司原来主要生产是一家处于领先地位的食品供应商。为了增加公司收入，管 理者有意开拓新的市场和产品。你现在领导着一个负责开发产品的团队。由于你的背景和 对信息技术的兴趣，你建议公司开发无线通信产品。但当你将建议提交审议的时候，管理 层认为这项产品和公司的核心竞争力不符合。你需要返回规划委员会推荐其他产品，并把 管理层的指导方针作为 A、一条假设 B、一项约束 C、一个规范

D、一项技术要求

70.各种项目的档案资料可以用于

A、将目前的业绩和预期获得的教训与之相比较 B、准备干系人管理计划 C、筛选项目团队成员

D、作为项目开始的输入项

73.在项目生命周期中的哪一个阶段遇到的不确定性最大? A、概念阶段

B、计划编制阶段 C、实施阶段 D、收尾阶段

79.项目失败的理论原因是

A、缺少项目式的或者强大的矩阵结构，不良的范围定义，以及缺少项目计划

B、缺少上级管理部门的支持和承诺，项目团队不和谐，以及项目经理缺少领导能力 C、客户需要的不良定位，项目团队工作位置上的分散，以及在整个项目进程中缺乏 与客户的沟通

D、组织结构因素，客户需要的不良定位，不合适的项目具体要求，以及不良的计划编 制和控制

82.你所在项目的技术主管提出了一项会给项目带来增值的请求，但是这个请求同时也会导 致项目范围的扩大。为了评估实施这一变更可能带来的影响幅度，你要求在项目中使用净 值分析法。这种方法代表的是 A、绩效评估技术 B、配置管理程序 C、成本核算程序 D、范围报告机制

项目时间管理

89.可以帮助我们明确哪些工作在规定的时间必须完成的工具是： A、项目主进度表

B、预算

C、工作分解结构 D、甘特表

91.在项目工作网络中有几种类型的浮动期。那些在特定活动中使用并且不影响后来活动的 浮动期被称作 A、多余的浮动期 B、自由的浮动期 C、总的浮动期 D、预期的浮动期

95.你正计划指挥你的新的项目管理户外培训课程的团队组建部分，参与者将参加一个生存 试验来剔除最“弱”团队成员。获胜者将得到公司的达尔文奖。因为这个课程只能在绿地 上执行，在安排课程的实践上你只能限制在一年中的几个特定时间。课程开始的最佳时机 是七月中旬。在你设计项目进度时一个更为普遍的时间限制是 A、不早于开始 B、不晚于结束

C、有一个确定的最晚开始时间 D、有一个确定的最早结束时间

99.项目经理在评估项目时间业绩表现时应该关注关键的和次关键的行为，一个这样做的方 法就是以浮动时间上升排序分析十个次关键的路径。这种方法是如下哪一个分析管理的一 部分? A、方差分析 B、进度模拟 C、挣值管理 D、趋势分析

88.在项目发展过程中，诸如谁来执行这个工作，这个工作在那里执行，工作的类型以及工 作分解结构(WBS)都是下面哪一个的示例? A、活动属性 B、限制条件

C、在工作分解结构库中贮存的数据 D、定义细化

风险管理：

2、有两类风险：商务和可保险型，以下哪项可看作可保险型风险 A、薪水册成本 B、机会成本

C、沉淀成本

D、有担保的承包商造成的损害

4、针对固定价合同，付款的风险是： A、承包商的实际成本

B、承包商的成本加固定费用 C、在承包商的投标中未公开的应急费用

D、根据风险评估预测所作的预测成本并用于处理风险

5、获得可以降低风险量的项目信息的最准确的方法是： A、采用头脑风暴技术识别风险 B、利用以前类似项目的历史数据 C、灵敏度分析 D、Delphi 技术

10、灵敏度分析和头脑风暴法是两种不同的风险识别方法，灵敏度分析的优点有： A、仅针对公众确定风险 B、考虑独立的答案

C、管理层理解可能会有大量不同的结果 D、可以提供项目经理可能缺乏的对项目的理解

12、某风险事件已经发生并产生了占总项目成本15%的影响，下列哪些行动是最合适的措 施? A、通知正确的项目干系人 B、更新项目预算

C、控制成本

D、与团队成员一起采用集体自由讨论的方式

19、假设估计幅度的两端是平均数的±3 西格玛，以下哪项幅度估计的风险最低? A、30±5 天，B、22‐30 天

C、最乐观为26 天，最可能为30 天，最悲观为33 天 D、A 和B 一样，风险都低于C

25、有效风险管理的首要要求是 A、决策所需信息的透明度高

B、风险所有关系明确

C、在管理已识别风险的过程中尽早的委任项目经理

D、受过风险培训并能理解风险起因的项目团队成员帮助创建和实施风险降低策略

27、假如风险事件发生的机率是85%，而产生的影响是US10000 美元，则US10000 美元代 表什么? A、风险值 B、净现值 C、期望值

D、应急储备金

29、某项目经理刚刚完成了项目的风险应对计划，他下一步该做什么? A、确定项目整体风险的比率

B、开始分析哪些在产品中发现的风险 C、在工作分解结构上增加任务

D、进行项目风险审核

31、项目经理正在评估供货商的标书，两个供货商出售类似的电子元件，并且在供货商方 进行集成，为了避免最大的风险，项目经理审查供货商的： A、价格、销售额、利润率

B、价格、交付承诺、检验进度计划 C、价格、经验、交付方式

D、经验、个人技能、材料控制步骤

32、EVMS 报告显示CV=SV=O，然而，由于遗漏了一个里程碑，整个项目将推迟。以下哪

项报告不充分? A、风险分析报告 B、沟通计划偏差 C、资源管理计划 D、关键路径状态

33、项目经理可能意识到不能满足某些合同条款和项目目标，要达到某些规范既费成本又 花时间。这时项目出现风险可能性较高，同客户协商降低风险可能性这种手段： A、可以消除所有项目和客户风险而不需要任何成本 B、可以重新定义风险对客户发生的可能性

C、可以使项目范围减小并改进交付给客户的产品

D、可能减少支付罚金的成本并且满足修订过的规范达到客户的最低要求

34、什么是风险的拥有者? A、对风险的识别负责

B、掌握风险来源的某个组织

C、受到风险严重影响的某个组织

D、对风险应对策略的实施负责

36、项目经理要求项目团队对其项目风险进行量化和评估，以下几点不能证明这样做的好 处的是：

A、彻底理解项目、相关风险、以及风险对项目各部分的影响 B、制订处理已经识别的问题的风险降低策略

C、保证所有已以识别的风险问题纳入项目计划编制

D、识别可能存在的替代方案

37、在做好项目成本结果概率分布后，有15%可能被超过的估算大约‐‐‐一个标准差 A、低于平均数 B、高于中数 C、高于平均数 D、高于中数

第四篇：各章总结2

2、细胞外信息传递方式：（6种）

内分泌：其信息分子即为激素，由内分泌器官所合成及分泌，经血液流经全身作用于远距离的靶器官。

旁分泌，自分泌，近分泌或并置分泌，胞内分泌，逆分泌。

3、内分泌系统的生理作用：

（1）保证集体内环境的相对稳定。A、控制消化道运动及消化腺的分泌； B、控制能量产生；

C、控制细胞外液的组成和容量。

（2）调节集体与外界环境的相对平衡。（3）调节生殖功能

4、内分泌系统作用的调节：

（1）内分泌腺功能的相互调节：腺体之间通过说分泌的激素表现协同、拮抗等复杂的相互关系

（2）神经系统和内分泌系统的相互调节

（3）神经系统-内分泌系统-体液之间的相互调节（4）神经-内分泌-免疫调节网络

5、激素作用的特点：

激素的生理作用是将信息从一个细胞传递到其他细胞，作用有四点：（1）特异性：激素在血液循环过程中虽然广泛接触各种组织、细胞，但却是有选择性的作用于该激素的靶器官、靶腺体或靶细胞。

（2）高效性：生理状况下，激素在血液中的含量很低，但却表现出了强大的生理作用。

（3）协同性与拮抗性：动物体各内分泌腺所分泌的激素之间是相互联系、相互影响的，其主要作用表现为相互增强与拮抗性。

（4）激素的作用极为复杂，主要表现在：A、一种激素多种作用；B、一中功能多种激素。

6、激素的分类及其特点：

（1）含氮激素：产生后贮存于该腺体，当机体需要时分泌到邻近的毛细血管中。

（2）类固醇激素：产生后立即释放，并不贮存，血液中含有各种此类激素的原因是蛋白类载体与之结合后限制了其扩散。

（3）脂肪酸类激素：目前所知，仅有前列腺素，他在机体需要时分泌，边分泌边应用，并不贮存。

7、生殖激素：能直接影响生殖机能的激素称为生殖激素，例如催乳素、前列腺素、等等。它在哺乳动物的复杂生殖过程中骑着重要的的掉空作用。

由特殊的无管腺和由一定的器官组织合成的化学物质，它通过弥散或借助血液循环的方式运输到靶组织及靶器官而七作用。作用特点：（1）生理效应很强；如前列腺素对(永久)黄体的消除作用，生产上用0.2mg／头600kg牛；（2）对靶组织和靶器官有高度转移亲和性；（3）借助血液循环或弥散作用产生生理效应；（4）投药处距靶器官越近，七作用越强烈（可据此减少用量）；（5）具有相互协同或拮抗作用（雌激素对孕激素的协同作用，孕激素对雄激素的拮抗作用，对动物控制发情的控制）

几种生产上常用的生殖激素：

（1）FSH：即促卵泡素，属于垂体前叶促性腺激素，主要来源：垂体前叶，化学性质：糖蛋白，靶器官：卵巢、睾丸（曲细精管），主要作用：促使卵泡发育成熟，促进精子发生。

（2）LH／ICSH：即促黄体素或间质细胞刺激素，属于垂体前叶促性腺激素，主要来源：垂体前叶，化学性质：糖蛋白，靶器官：卵巢、睾丸（间质细胞），主要作用：促使卵泡排卵，形成黄体，促进孕酮、雌激素及雄激素分泌。（3）HCG：即人绒毛膜促性腺激素，属于胎盘促性腺激素，主要来源：灵长类胎盘绒毛膜，化学性质：糖蛋白，靶器官：卵巢、睾丸，主要作用：与LH类似。

（4）eCG／PMSG：即马绒毛膜促性腺激素或孕马促性腺激素，属于胎盘促性腺激素，主要来源：马胎盘的子宫内膜杯化学性质：糖蛋白，靶器官：卵巢，主要作用与FSH类似。（PMSG易引起卵巢囊肿）PMSG与FSH相比：一般情况下二者可以相换，前者用药后半衰期长，后者多用药两次，发情配种后前者近期不易配种，前者多用引起多卵泡发育。

HCG与LH可完全互换，两颗稍大。

（5）PGs：即前列腺素，属于局部刺激素，主要来源：各种组织，化学性质：不饱和羟基脂肪酸，靶器官：各种组织和器官，主要作用：具有广泛的生理作用，PGF2α具有溶黄体作用。

8、母畜生殖功能的发展阶段：

初情期：母畜初次表现发情并排卵的时期，幼畜发育到初情期，性腺才真正具有了配子生成和内分泌的双重作用。

性成熟：母畜生长发育到一定年龄，生殖器官已经发育完全，生殖机能达到了比较成熟的阶段，基本具备了正常的繁殖功能，称为性成熟，但此时身体的生长发育尚未完成。

繁殖适应龄期：母畜达到性成熟又达到了体成熟（身体已发育完全并具有雌性动物固有的特征与外貌），开始配种时体重应达到成年体重的70%以上，生产上牛18月龄，350kg；饲养条件较好时，常采用16月龄，300kg。

繁殖年限：限制因素：动物衰老丧失繁殖功能；疾病使生殖器官严重受损或功能障碍，反之活动停止。

9、发情周期：母畜达到初情期以后，其生殖器官及性行为重复发生一系列明显的周期性变化称为发情周期。

发情期子宫变化：卵泡生长→雌激素↑导致：A乳房腺管↑，乳房增大；B大脑兴奋性↑；C子宫弹性↑，分泌物↑，子宫黏膜潮红。排卵后，黄体↑→孕酮（拮抗雌激素）→负反馈丘脑导致LH、FSH分泌减少→发情停止，动物安静→子宫弹性↓→卵巢弹性↓→产生子宫乳，此时胚胎处于游离状态。

10、动物发情特点与发情鉴定：

（1）阴道、子宫颈：阴道黏膜潮红充血，子宫颈弛张1-2倍，便于分泌物排除，精子进入。

（2）卵泡发育：此时卵巢上有较多各期发育程度不同的卵泡及黄体。（3）分泌物：阴门黏液：发情时动物兴奋，黏液稀薄透明如蛋清；怀孕时（多在4月后）混沌，粘性稠，可拉长；子宫有炎症时，分泌物呈絮状，（条状）分泌物多是如豆花状：子宫内膜炎时分泌物检查：夜晚棉签挑起手电照射下清液中含有粉笔灰状沉淀物。卵（泡）巢囊肿、永久黄体：长期、大量时变稀薄。

（4）直肠检查：可见子宫壁紧张，卵泡直径可达1-2cm。

（5）行为变化：狗四处乱跑找交配，猫怪叫，牛常表现出不安有排尿姿势，有爬跨动作。有时候表现不明显。

母畜发情期生殖器官及性行为周期性变化参见P93。

11、妊娠识别与鉴定：

A、牛妊娠鉴定：直肠检查参见P146 B、妊娠时间：（从配种时开始算）黄牛：274-291天，平均285天；水牛平均307；奶牛250-305，平均280；山羊146-161，平均152；猪110-123，平均114。

C、母体妊娠识别：黄体和孕酮的作用，参见P109-111。D、妊娠母体变化：

（1）生殖器官变化：卵巢中有黄体的存在；子宫逐渐增大，有被推入腹腔在还纳至骨盆腔的过程；子宫中动脉孕侧、两侧逐渐变粗出现特有的妊娠脉动；阴道、子宫颈及乳房变化：阴道粘膜苍白，阴道先变长后变短而粗、充血而水肿；子宫颈缩紧，黏膜增厚，其腔内充满黏液（子宫颈塞）；乳房增大，变实。腺管增生，为泌乳做准备。（2）全身变化：营养状况良好的动物一般皮毛光亮，骠形较好。到一定的妊娠阶段，母畜腹围增大，胃肠容积减小，排粪尿次数增加，不喜运动，后肢多水肿。

（3）内分泌：大体趋势为：孕酮从妊娠开始至分娩前短时间，保持较高水平，雌激素则一直保持很低水平，在孕酮水平降低时逐渐升高，分娩后降低为几乎0。

12、分娩前动物体变化（分娩预兆）：

分娩：妊娠期满，胎儿发育成熟，母体将胎儿及其附属物从子宫排出体外的生理过程。分娩预兆：（1）乳房变化：（乳房进一步增大）乳房极度膨胀、皮肤发红，乳头中充满白色初乳，乳头表面被覆一层蜡样物质。

（2）软产道变化：分娩前1周阴唇开始变软、肿胀，增大2-3倍，前1-2d子宫颈开始肿大，松软，封闭宫颈管的黏液软化流入阴道，有事掉在阴门外，呈透明条索状（牛）。

（3）骨盆韧带的变化：骨盆韧带、荐坐韧带、荐髂韧带韧带变软，荐骨后端活动性增大。在牛可见尾根“下陷”的情况。

（4）精神状态的变化：产前精神抑郁，徘徊不安，离群寻找安静地方分娩，乳牛产前体温升高至39-39.5℃。（分娩机理：参见P153）。

13、决定分娩过程的要素、分娩过程： 分娩取决于产力、产道及胎儿三者关系。

产力：胎儿从子宫中排出的力量，由子宫（阵缩）肌、腹肌和膈肌节律性收缩构成。软产道：由子宫颈、阴道、前庭及阴门这些软组织构成的管道。

硬产道：即骨盆（骨盆入口、出口，骨盆腔和骨盆轴），助产应在产道充分扩张的情况下进行。胎儿与产道的关系：

胎向：A、纵向：胎儿纵轴与母体纵轴平行，包括正生和倒生；B、横向：胎儿纵轴与母体纵轴在水平方向垂直；C、竖向：胎儿纵轴与母体纵轴上下垂直。

正常的胎位：正生，上胎位。分娩过程：

（1）子宫开口期：子宫阵缩，此期无努责。母畜表现出尾根抬起，频做排尿姿势，脉搏、心跳加快。

（2）胎儿产出期：子宫颈充分开大，胎囊及胎儿前置部分楔入阴道。母畜有后肢踢腹的表现。分娩时母畜多采取侧卧后肢挺直努责，便于骨盆开张，胎儿产出。（在胎膜破裂后，应迅速将胎儿拉出，防止腹压减小，母畜停止努责，分娩中止。）

（3）胎衣（胎膜）排出期：胎儿产出后，阵缩及努责幅度减小，子宫阵缩（努责）将胎衣排出。牛产后不起，可利用胎儿置于其前舔舐而诱导其站起。

14、接产：

（1）准备：产房尽量宽敞、干燥安静通风良好无贼风。药械：70%酒精，5%碘酒，消毒液，催产药液，注射器，无菌棉，体温计等。人员：接受过训练的专业人员。

（2）步骤：临产检查，及时助产。

（3）处理新生仔畜：A、擦干羊水；B、处理脐带（断脐消毒）；C、帮助吃初乳。

15、产后变化：

行为变化：舔舐仔畜，哺乳，护仔（孕酮激活中枢神经有关母性行为的调节系统）。

生殖器官变化：胎衣排出后子宫迅速收缩变小；产后卵巢有卵泡开始发育；阴道、前庭及阴门，骨盆及其韧带4-5d内复原，经10d左右妊娠浮肿消失。

牛的诱导分娩：从妊娠265-270d开始，一次肌肉注射20mg地塞米松或5-10mg氟美松，母牛在30-60h后分娩。

16、人工催奶技术：

A、测量母畜体重：胸围×体斜长／10800；

B、苯甲酸雌二醇（雌激素、促性激素）0.1mg／kg； C、孕激素0.25mg／kg； D、利血平3-5mg／次／d。

B、C二步用至第7天，D步用第6至9天，可挤奶。

1号针剂为苯甲酸雌二醇，5 ml/支；2号针剂为利血平5 ml/支。

二、产科病理学部分：

17、流产原因及处理： 流产原因：

（1）疾病引起的流产：如全身感染，生殖器官疾病等；（2）兽医诊疗错误引起；

（3）药物性流产：A、全身麻醉；B、子宫收缩药：氨甲酰胆碱，麦角心碱；C、药物引起：地塞米松等；D、疫苗导致；E、饲料性：长期严重的维生素、矿物质缺乏（Va、Ve），品质不良：过酸、霜冻、霉变、有害、含雌激素过高饲料；F、管理性：打斗、摔倒，运输惊吓等刺激等。

处理：排出以上原因，主要在于预防流产的发生，对于已经发生了流产的母畜，应采取：检查、分析病因，防流产保胎，促使死胎排出，排出子宫恶露，防止发生子宫内膜炎，生殖道感染。

18、孕畜截瘫、浮肿、假孕：

孕畜截瘫：妊娠末期孕畜既无导致瘫痪的局部因素（如腰、臀部及后肢损伤），又无明显的全身症状，而后肢无法站立的一种疾病。病因多为Ca、P、Vd缺乏，营养不良，子宫捻转等。

妊娠浮肿：妊娠末期孕畜腹下及后肢等处发生水肿，面积小、症状轻的是正常的生理现象，症状严重的才认为是病理状态。病因主要是腹下、乳房及后肢静脉血流滞缓，毛细血管渗透压增高，血液中水分渗出增多。治疗采取强心利尿。

假孕：多见于猫、狗，交配后神经刺激或因们刺激后，即行排卵，并产生存在时间较长的黄体，由于孕酮含量与怀孕黄体相同，而且持续发挥作用，因而引起一些母狗、母猫，出现类似怀孕的症状。狗：60-100d，猫：黄体在持续20天后开始退化，40-44天完全消失，假孕结束。

症状：与正常怀孕相似：乳腺发育，并能泌乳；行为变化，如搭窝等；母性增强；临床检查同正常怀孕一致；食欲减退，厌水；X光，B超等检查可以确诊。

一般不予治疗，下一发情季节恢复，可肌注PMSG，但易引起卵巢囊肿。

19、阴道脱、子宫脱（内翻）：

阴道脱：阴道底壁、侧壁和上壁一部分组织和肌肉松弛扩张连带子宫和子宫颈向后移使松弛的阴道壁形成折襞嵌堵于阴门之内或突出于阴门之外。病因：

（1）孕期雌激素原因致阴门松弛；（2）圈舍设计前高后低；（3）母畜年老，肌收缩无力；（4）饲喂霉变饲料，毒素引起韧带松弛，里急后重。治疗：荐尾、尾椎行硬膜外麻醉，对脱出部分清洗消毒，整复回阴道，行结节缝合，注意流出尿液通路。术后注意观察，保持后高前低姿势。子宫腔翻入子宫腔或阴道内，称为子宫内翻，子宫全部翻出于阴门之外，称为子宫脱出。病因：

A、产后强烈怒责如产道及阴门损伤、胎衣不下母畜强烈怒责腹压增高；B、外力牵引，如胎衣排出时母畜强烈怒责；C、子宫迟缓，可延迟子宫颈闭合时间和子宫角体积缩小速度，更易受腹壁肌收缩和胎衣牵引的影响。治疗：

整复法：A、保定；B、清洗；C、麻醉，荐尾硬膜外麻醉，（太深母畜卧下）；D、整复，用饱和明矾水处理子宫缩小体积，从阴门上口小部分开始。侧卧保定时注射葡萄糖酸钙溶液减少瘤胃鼓起。术后护理：强心输液，对于脱出污染严重部分，应行切除。20、难产： 病名：由于各种原因而使分娩的第一阶段，尤其是第二阶段明显延长，如不进行人工助产，则母体难于或不能排出胎儿的产科疾病。病因：

普通病因：通过影响母体或胎儿而使正常的分娩过程受阻：遗传、环境、内分泌、传染性、外伤性、饲养管理因素等。直接病因：母体性、胎儿性，参见P253。检查：为了挽救胎儿、母畜生殖力；

A、病史调查：产期，年龄与胎次，产程，既往病史、繁殖史，胎儿产出情况，是否进行助产等。

B、母畜全身检查：一般临床检查，站立、精神情况；阴门扩张情况等；

C、产科检查：

（1）外部检查：视诊，检查乳房，骨盆韧带，阴门及周围区域，阴道分泌物，腹、腹协部。（2）阴道检查：检查产道的松弛度及润滑程度。（3）胎儿检查：主要检查胎向、胎位、胎势、胎儿大、死活及进入产道的程度。（4）直肠检查：检查子宫捻转的程度，自贡站立和收缩力。

助产准备：母畜保定（尽量采取站立姿势）；麻醉镇静、镇痛、肌松药物；消毒用0.1%高锰酸钾或新洁尔灭擦洗阴门、尾根等部位；润滑用温和无刺激的肥皂水、石蜡油等润滑产道； 胎儿助产：牵引，位置矫正（正生上胎位），截胎术。母畜助产：剖腹产，外阴切开（多用于胎儿过大，胎位不正无法整复），子宫切除（用于难产已久，子宫壁破裂或损伤，胎儿死亡气肿等）常见助产：

A、子宫迟缓：子宫开口期及胎儿排出期子宫肌层收缩频率、持续期及强度不足导致胎儿不能排出。助产：（1）、牵引；（2）、催产素10-20IU肌注或皮下注射。

B、努责过强及破水过早：分娩时子宫壁及腹壁收缩时间过长，间隙短，时间过长，力量强烈，子宫壁肌肉出现痉挛性不协调收缩，形成狭窄环；后者是指子宫颈尚未松软开张、胎儿姿势尚未转正和进入产道胎囊即已破裂，胎水流失。助产：视胎儿情况进行牵引、局部麻醉镇静及截胎、剖腹产。

C、子宫捻转：整个子宫、子宫角或其部分围绕纵轴发生扭转。助产：（1）、产道纠正；（2）、直肠矫正；（3）、母体翻转；（4）、剖腹矫正及剖腹产。

D、子宫颈开展不全（双子宫颈）：牛羊子宫颈肌肉十分发达，产前受雌激素作用发生浆液性浸润而变软过程缓慢，若阵缩过早及早产，则易发病。助产：牛注射乙烯雌酚50mg，羊5mg，在注射催产素及葡萄糖酸钙，然后施牵引术；双子宫的，视情况进行两子宫间隔膜切开或剖腹产。

E、阴道、阴门及前庭狭窄：常见于首胎配种过早，阴道及阴门肿瘤，骨盆肿胀等，助产：润滑产道，牵引术，严重的施剖腹产。

F、骨盆狭窄：骨盆骨折、异常或损伤引起骨盆腔和大小和形态异常，妨碍胎儿排出。助产：灌注大量润滑剂，牵引胎儿排出，适当考虑剖腹产，除营养性及配种过早引起外，其他原因引起不应再做种用。G、子宫疝：子宫通过脐孔、腹壁、腹股沟、膈等破裂口形成各种子宫疝。助产：胎儿无难产的，修复疝，子宫脱出部分坏死的，应行手术解除。

H、胎儿过大：相对较大及绝对过大如巨型胎儿，巨型胎儿人工诱导分娩，在牵引术不能产下时，行阴门切开或剖腹产。

I、双胎难产：两个胎儿同时楔入母体骨盆，但二者皆不能通过，同时伴发胎势及胎位异常。助产：先推回一个胎儿，拉出一个再使第二个产出。难产时间过久，注射催产素，施行手术剥离。

J、胎儿畸形：常见畸形包括胎儿水肿、胎儿腹腔积水、胎头积水、肢体不全、颈歪斜、双头、双肢体重复畸形等；助产：胎儿牵引，截胎，剖腹产。

K、胎势异常：常发病有腕关节屈曲、头颈侧弯等；助产：胎儿矫正，牵引产出，截胎，剖腹产等。

L、休克处理：休克是机体在神经、内分泌、循环、代谢等系统发生严重障碍时表现出的症候群，以有效循环量锐减、微循环障碍为特征的急性循环不足，是一种组织灌注不良导致组织缺氧和器官损害的综合征。

治疗：此病主要由腹压下降过快、疼痛、大量失血、过敏反应引起，治疗原则：早期诊断、早期治疗。（1）消除病因：终止及矫正休克的发展；（2）补充血容量：视情况及早采取输血补液及解除微血管痉挛的药物，同时还可应用抗坏血酸及钙制剂等各种综合措施，或针刺分水、耳尖及尾间等穴，还可输入右旋糖酐葡萄糖盐水。（3）防止腹压过低：胎水流失，胎儿腹部露出体外时，应缓慢拉出胎儿防止腹压急剧降低。（4）改善心功能，使用提高心肌收缩力的药物：多巴胺、异丙肾上腺素、洋地黄及皮质醇等。（5）调节代谢障碍，纠正酸中毒：轻度用生理盐水，中度用碱性药物（常用药物为5％碳酸氢钠，碱中毒也用此药）。

（6）外伤性休克伴发感染时，在休克早期应用广谱抗生素治疗。M、难产预防：（1）避免过早配种；（2）保证发育期、妊娠期营养供给；（3）妊娠母畜适当运动、使役；（4）产前提前一周或半月将临产母畜送入产房，适应环境；（5）产乳牛产前60天干乳；（6）防治难产：临床检查（直肠检查等），及时采取助产措施。正常分娩时间：第一阶段（开口期）马＜4h；牛，绵羊、山羊＜6--12h；犬猫和猪＜6--12h。或第二阶段（胎儿排出期）马＜20--40min；牛，绵羊、山羊＜2--3h；犬猫和猪＜2--4h。超过此时间应行助产。

21、剖腹产：

牛的剖腹产切口有6处，临床常用、实用的是腹侧壁切口。A、牛保定：倒牛，绳子保定；

B、选择腹侧壁切口：髋关节和脐孔连线中部切开，可平移； C、剃毛，消毒，消毒方法碘酒棉球从中间自外；

D、麻醉：2%盐酸普鲁卡因分8点创口皮下注射麻醉；

E、切开皮肤、腹横肌、横肌、腹膜，探查子宫及胎儿情况，将子宫拉出体外；

F、固定子宫，作牵引线，尽量避开子叶切开子宫，将胎儿拉出； G、缝合，整复子宫，缝合速度一定要快，控制在5分钟以内； H、关闭腹腔：每逢一针将手探入检查，保证将腹膜缝合完整，避免将肠壁等内脏缝住。

I、快速缝合腹壁肌层，防瘤胃鼓气导致腹腔关闭困难。J、缝合皮肤，作中部对分缝合，皮肤吻合良好；

K、切口面贴盖消毒创巾，用圆枕法缝合在皮肤上，防止感染。主意：手术过程要快，减少瘤胃鼓气带来的影响；失液过多的，家伙死补液。剖腹产后牛一般不易再孕。

22、产道损伤：阴道阴门及宫颈损伤、子宫破裂等，主要原因是生产过程中撕裂，病理表现为患畜弓背举尾，作排尿姿势，不安，有痛苦状。

治疗：杀菌消炎，止血镇痛，缝合等。

23、胎衣不下：母畜娩出胎儿后在第三产程的生理时限内胎衣未能排出，牛超过12h，马1-1.5h，猪1h，羊4h。原因包括：产后子宫收缩无力，胎盘未成熟或老化，胎盘充血和水肿，胎盘炎症，胎盘组织构造异常等。

治疗：药物疗法：子宫内投药四环素、土霉素、磺胺药等1-2g；子宫颈缩小时注射雌激素；肌肉注射抗生素；己烯雌酚20mg，1小时候注射催产素50-100IU，2h后重复一次。

手术疗法：人工剥离胎衣，缓慢剥离，不能过多损伤子叶（少于5-8个）行荐尾间隙注射15ml2%盐酸普鲁卡因。

24、子宫内膜炎：产后子宫数天内发生的急性炎症。转为慢性炎症时导致不孕。

治疗：患畜子宫49℃消毒液冲洗，应用广谱抗生素防止毒素自体吸收中毒，如宫内注射呋喃唑酮混悬液2-5ml，同时可静脉注射50IU催产素，PGF2α等。禁止使用雌激素，虽可增加抵抗力但同时增加血流量加速毒素吸收。

25、生产瘫痪、产后截瘫：又称低钙血症、乳热症，是母畜分娩前后突然发生的一种严重代谢性疾病。

治疗：静脉注射25%硼葡萄糖酸钙500ml，也可一半皮下一半静脉注射，6-12h后无效可重复注射，不超过3次，每500ml给药10min。胰岛素及肾上腺皮质激素配合高糖及5%碳酸氢钠连用。

乳房送风法：四个乳区打满空气，乳区血流减少，血钙回升，脑缺氧改善等。

26、母畜不孕及其检查诊疗：先天及后天不孕，参见P342-366。

27、常见疾病性不孕：参见P391 A、卵巢静止：发情情况：无，卵巢触诊：较硬、光滑，子宫触诊：收缩无力，黏液：无，B、永久黄体：无，坚实突起，收缩无力，混浊，粘稠 C、卵泡囊肿：长期发情，较大卵泡状物，有时正常 D、黄体囊肿：无，一突起顶部软，收缩无力。

28、新生仔科学：新生仔环境变化离开适宜母体保护，接触复杂外界环境。营养变化新生仔消化功能不全，营养获取不足，消化不良。温度变化：新生仔温度调节能力差，出生后1-2小时内体温下降0.5-1℃，靠哆嗦和肌肉活动供热。

呼吸和循环系统变化、消化系统变化、体温变化、排尿变化、脐带脱落、代谢与激素变化、血液变化。

29、新生仔疾病：新生仔窒息：假死，刚出生的仔畜呈现呼吸障碍或无呼吸而仅有心跳，常见于马和猪。

治疗：擦尽鼻孔及口腔内羊水，用浸有氨水的棉花放在鼻孔上刺激鼻腔粘膜诱发呼吸，或者向仔畜身上泼冷水，使用尼可刹米刺激呼吸中枢。

30、乳房炎及其治疗：

乳房炎是由各种病因引起的乳房炎症，其主要特点是乳汁发生理化性质及细菌学变化，如县组织发生病例变化。乳房炎治疗：（牛站立保定）A、生殖股神经封闭：第3、4腰椎直进针，刺透皮肤，针向椎体45°，刺满（牛腰闪动）退针0.2cm，注射2%盐酸普鲁卡因15-20ml，药不能过快，退针麻醉浅表神经，注射药物5ml。

B、乳房内灌注抗生素法：乳房内灌注青霉素、庆大及阿米卡星等加入80-100ml生理盐水在挤尽乳汁后一次灌入，连续2-3天。

C、乳房基部封闭：前乳基：乳基部与腹壁之间避开神经与血管，沿腹壁切线对准后肢系关节进针，边注药边退针，注入青链霉素50-80W单位。后乳基：距乳中隔1-2cm处，偏向患侧以前肢髋关节为参照，腹壁切线进针，刺满推药出针。

D、会阴神经封闭：牛尾跟下坐骨联合处消毒进针。E、健胃药＋瓜蒌散

第五篇：中药学各章总结

第一章作业

作业答案

1.什么是中药 ？什么是中药学 ？答：中药，是指在中医药理论指导下认识和使用的药物，是用以防治疾病的重要武器 中药学是研究中药基本理论和各种中药的来源、采制、性能、功效、临床应用及其他有关知识的一门学科，是中医学的重要组成部分。

2.说明《神农本草经》、《本草经集注》、《本草纲目》、《本草图经》、《证类本草》、《本草纲目拾遗》6部本草的成书时代、作者、药数和主要贡献。答：成书于东汉（不晚于公元2世纪）的《神农本草经》论载药365种。《本草经集注》（简称《集注》）：作者陶弘景为南朝梁代的著名医药学家

1552年至1578年，伟大的医药学家李时珍完成了《本草纲目》，载药1892种(新增374种)。

18世纪著名的本草学家赵学敏辑成《本草纲目抬遗》10卷，载药921种《本草纲目》未提及者达716种之多。本草图经》由苏颂辑成所附的900多幅药图。

北宋蜀地名医唐慎微所著《经史证类备急本草》(后人简称《证类本草》)收载矿物药253种。3.我国被学术界多数人视为古代世界上最早的药典著作是什么？共收载多少药物。答：我国是世界上最早颁行药典的国家。自唐《新修本草》于公元659年颁行后，历史上的官修本草曾经对中药学的发展产生重要影响。书中载药844种。

4.中医的健康标准？（书第8页）答：1.两眼有神2.面色红润3.语声宏亮4.呼吸微徐5.情绪稳定6.牙齿坚固7.腰腿灵便8.胖瘦适宜9.脉象匀缓10.头发润泽11.记忆良好。

第二章作业

1.什么是中药的功效？答：中药的功效，是在中医药理论指导下，对于药物治疗和保健作用的高度概况，是药物对于人体医疗作用在中医学范畴内的特殊表述形式。中药功效的作用对象主要是人体的病理状态，这是中药学的性质和形成历史所决定的。其在理论上、内容上和形式上都有别于其他医药学对药物作用的认识和表述，具有明显的自身特色。

2.什么是中药的基本作用？答：中医理论认为，人体在健康状态下，脏腑经络的生理活动正常，并与外界环境之间保持着“阴平阳秘”的动态平衡状态。当各种致病因素影响人体后，便会破坏这种协调和谐的关系，导致邪盛正衰，阴阳气血失常，脏腑经络功能紊乱等病理改变，发生疾病。针对不同的病机，使用相应的中药，或祛除病邪，或扶助正气，或协调脏腑功能，纠正阴阳的盛衰，使机体恢复或重建其阴平阳秘的正常状态，这就是中药的基本作用。

3.五行与五脏六腑的关系？（书33）答：五脏是指肝(木)、心(火)、脾(土)、肺(金)肾(水)六腑：胆、小肠、胃、大肠、膀胱、三焦

4.解释阴阳学说的含义？（书26）答：阴和阳，既可以表示相互对立的事物，又可用来分析一个事物内部所存在着的相互对立的两个方面。一般来说，凡是剧烈运动着的、外向的、上升的、温热的、明亮的，都属于阳；相对静止着的、内守的、下降的、寒冷、晦暗的，都属于阴。以天地而言，天气轻清为阳，地气重浊为阴；以水火而言，水性寒而润下属阴，火性热而炎上属阳。任何事物均可以阴阳的属性来划分，但必须是针对相互关联的一对事物，或是一个事物的两个方面，这种划分才有实际意义。如果被分析的两个事物互不关联，或不是统一体的两个对立方面，就不能用阴阳来区分其相对属性及其相互关系。事物的阴阳属性，并不是绝对的，而是相对的。这种相对性，一方面表现为在一定的条件下，阴和阳之间可以发生相互转化，即阴可以转化为阳，阳也可以转化为阴。另一方面，体现于事物的无限可分性。第三章作业

1.简述药物四气、五味的含义、作用，并举例说明其对临床用药的指导意义答：四气，是指药物的寒、热、温、凉四种药性，又称为四性。四气主要用以反映药物影响人体寒热病理变化的作用性质，是药物最主要的性能。

意义：一般属于寒性或凉性；能够减轻或消除寒证的药物，一般属于温性或热性。寒凉性质药物，大多有清热作用，如清热、泻火、凉血、解毒、攻下、滋阴等功效，主要用于阳证、热证；温热性质药物，大多有散寒作用，如散寒、温里、行气、活血、补气、助阳等功效，主要用于阴证、寒证。

最初，五味的本义是指辛、甘、苦、酸、咸五种口尝而直接感知的真实滋味。滋味实际上不止此五种，为了能与五行学说相结合，前人将淡味视为甘味的“余味”，而附于甘味；又将涩味视为酸味的“变味”，而附于酸味。因此，一直习称五味。在性能理论中，药物的五味除了用以表示其实际滋味以外，主要是用以反映该药的作用特点。1．辛能散、能行 用辛味表示药物具有发散、行气、活血等方面的作用。所以，能发散表邪的解表药，消散气滞血淤的行气药和活血化淤药，一般都标以辛味。2．甘能补、能缓、能和 用甘味表示药物有补虚、缓急止痛、缓和药性或调和药味等方面的作用。所以，补虚药(包括补气、补阳、补血、补阴、健脾、生津和润燥等)及具有缓急止痛，缓和毒烈药性，并可调和药味的甘草、蜂蜜等药，都标以甘味，实际上这些药物都是补虚之药。3．苦能泄、能燥 泄的含义主要有三：一是降泄，使壅逆向上之气下降而复常。如杏仁、葶苈子能降壅遏上逆的肺气而止咳平喘；枇杷叶、代赭石能降上逆的胃气而止呕吐呃逆。二是指通泄，能通便泻下。三是与寒性相结合，表示清泄，能清除火热邪气。燥是指燥湿，若干苦味药能祛湿邪，治疗湿证。结合药性来看，燥湿作用又有苦温燥湿和苦寒燥湿（又称清热燥湿）之分。所以，止咳平喘药、止呕逆药、攻下药、清热药及燥湿药，一般标以苦味。4．酸与涩都能收能涩 用酸味或涩味表示药物有收敛固涩作用。所以，能治疗滑脱不禁证候的敛肺、涩肠、止血、固精、敛汗药，一般标以酸味或涩味。习惯上多将滋味本酸的收涩药多标为酸味，其滋味不酸者，多标以涩味；因为涩附于酸，故经常又酸味与涩味并列。5．咸能软能下 表示药物有软坚散结或泻下作用，所以，能治疗癥瘕、痰核、瘿瘤等结块的牡蛎、鳖甲、昆布等药，多标以咸味；以上结块多与淤血、气滞、痰凝相关，故软坚散结药亦多辛味之品。因为泻下通便是苦能通泄所表示的作用特点，咸能下之说与之交叉重复。所以，咸能下的使用十分局限，相沿仅指芒硝等少数药的泻下特点。实际上各论中药物后的咸味，更多用以反映动物药、海洋药的滋味特征。6．淡能渗能利 表示药物有渗湿利水作用。虽然利尿药物甚多，但习惯上只将茯苓、猪苓等部分利水药标以淡味，而且往往甘味与淡味并列；多数利水药的药味并无规律性。

2.何谓引经药？它们与脏腑经络的关系？举例几味中药。（书66）答：前人认为一些药物对某一脏腑经络具有特殊作用，其选择性特别强，并且可以引导与之同用的其他药物达于病所，而提高临床疗效，因而将此称为引经（或称引经报使、主治引使、响导、各归引用等），又将这类药物称为引经药。脏腑，是中医学中特有的定位概念，其与解剖上的实际脏器有较大的区别，不能与之混淆。对于药物归经的理解，也不一定是指药物有效成分实际到达的部位，而主要是药物产生效应的部位所在。川芎，柴胡等引经药。

3． 何谓药物升降浮沉？与药物四气五味及功效有何关系？答：升降浮沉是用以表示药物作用趋向的一种性能。升是上升，表示作用趋向于上；降是下降，表示作用趋向于下；浮是发散，表示作用趋向于外；沉是收束闭藏，表示作用趋向于内。

药物升降浮沉作用趋向性的形成，虽然与药物在自然界生成禀赋不同，形成药性不同有关，并受四气、五味、炮制、配伍等

诸多因素的影响但更主要是与药物作用于在体所产生的不同疗效、所表现出的不同作用趋向密切相关。

影响药物升降浮沉的因素主要与四气五味、及药物质地轻重有密切关系，并受到炮制

和配伍的影响。

药物的升降浮沉与四气五味有关：王好古云：“夫气者天也，温热天之阳；寒凉天之

阴，阳则升，阴则降；味者地也，辛甘淡地之阳，酸苦咸地之阴，阳则浮，阴则沉”。

一般来讲，凡味属辛、甘，气属温、热的药物，大都是升浮药，如麻黄、升麻、黄芪

等药；凡味属苦、酸、咸、性属寒、凉的药物，大都是沉降药，如大黄、芒硝、山楂等。.什么是中药的毒性及影响毒性的因素？答：毒性是药物对机体的伤害性，是用以反映药物安全程度的性能。毒性反应会造成脏腑组织损伤，引起功能障碍，使机体发生病理变化，甚至死亡。毒性虽然是普遍的，而引起毒性反应则是不多的。药物毒性的大小是相对的，是否出现毒性反应，主要取决于用量。前述国务院令中确定的毒性中药有砒石、砒霜、水银、生马钱子、生川乌、生草乌、生附子、生白附子、生半夏、生南星、生巴豆、斑蝥、青娘虫、红娘虫、生甘遂、生狼毒、生藤黄、生千金子、生天仙子、闹羊花、雪上一枝蒿、红升丹、白降丹、蟾酥、洋金花、红粉、轻粉及雄黄等28种。对于这些毒药，哪怕是毒性最大的砒霜，只要在安全有效的剂量内合理使用，是不会引起中毒的。而历代指为无毒的人参、五加皮、火麻仁等，因服用

过量，亦有致人中毒，甚至死亡的报道。

第四章作业 ．中药炮制目的有哪些？举例说明。答：㈠增强药物作用，提高临床疗效

增强药物的某一作用，提高其临床疗效，是中药炮制最常见的炮制目的。如在中药炮制时，经常要加入一些辅助药料（简称辅料），其具体作用虽然互不相同，但一般均是为了增效。现代研究还发现一些药物经过炮制有利于稳定药效。如含苷类有效成分的药物经加热处理以后，其相应的酶被破坏或失去活性，可防止苷类水解而避免重要的有效成分含量下降，如人参、黄芩等。㈡降低或消除药物的毒性或副作用，保证用药安全一些有毒性或明显副作用的药物，如马钱子、天南星、乌头及常山等，不经炮制而直接生用，即使在常用的有效剂量内，也容易产生毒性反应和副作用。如经过特殊的炮制处理，可以明显降低甚至消除某些毒副反应，确保临床用药安全。因天南星含有苛辣性毒素，对口、舌、咽喉等有较强的刺激性，可引起口舌麻木，声音嘶哑，甚至粘膜糜烂和坏死，若与白矾、生姜水共浸并煮透后，则基本无此毒性。

㈢改变药物的性能功效,使其更加适应病情或扩大应用范围中药固有的寒热、升降、补泻等性能和功效，在有的情况下不一定完全适合病情的需要，经过特殊的炮制处理，将这些性能和功效适当地改变，就可以更加与病情相符合。如豨莶草具有祛风湿，通经活络的功效，但性味苦寒，与风湿寒痹不尽相宜，经拌入黄酒蒸制后，其性偏于辛温，则更能对证。药物炮制改变性能和功效后，还可以在原药物的基础上扩大应用范围。如生地黄性寒而主要用以清热凉血，经蒸制为熟地黄后，变为温性之药，则能补血而治疗血虚证。㈣ 改变药材的某些性状，便于贮存和（或）制剂药材大都可以随采随用，不少动植物药使用鲜品疗效更佳。但因产地季节等因素的制约，皆要干燥后贮存备用。一般药材都可以采用阴干、晒干或烘烤使之干燥。有的药材则必须经过特殊的炮制，才能贮存和运输。如马齿苋柔嫩多汁，必须入沸水 单后才能干燥。桑螵蛸、五倍子必须蒸制以杀死虫卵或蚜虫。否则桑螵蛸可因虫卵孵化而失效，而且生用还有滑肠之弊。此外，将植物药切制成一定规格的饮片，矿物药的煅、淬、砸、捣，均是便于制剂和调配。㈤ 使药材纯净，保证药材质量和称量准确，药材在采收、贮存和销售过程中，往往带有一些非药用部分及杂质（如肉桂之栓皮、枳壳之瓤等）、砂土甚至变质者，既影响药材质量，又造成称量的不准确。经过修制或特殊处理，则完全可以避免因此造成的不良影响。㈥ 矫味矫臭，便于服用，一些药物（如乳香、没药、地龙等）具有臭气、异味或刺激性，患者难于接受，服药后还易引起恶心、呕吐等不适反应，经过炮制不仅可使作用增强，亦可减少不适反应，其效良而不至“苦”口。

2．十八反、十

九

畏的具

体

内

容

是

什

么？

（书

68）

答

十八反：乌头反半夏、瓜蒌、贝母、白蔹及白及；甘草反海藻、大戟、甘遂及芫花；藜芦反人参、沙参、玄参、丹参、苦参、细辛及芍药。本草明言十八反 贝蒌半蔹及攻乌 藻戟遂芫具战草 诸参辛芍叛藜芦

十九畏：硫黄畏朴硝，水银畏砒霜，狼毒畏密陀僧，巴豆畏牵牛，丁香畏郁金，牙硝畏三棱，川乌、草乌畏犀角，人参畏五灵脂，官桂畏赤石脂。硫黄本是火中精，朴硝一见便相争； 水银莫与砒霜见，狼毒最怕密陀僧巴豆性烈最为上，偏与牵牛不顺情； 丁香莫与郁金见，牙硝难合荆三棱川乌草乌不顺犀，人参最怕五灵脂； 官桂善能调冷气，若遇石脂便相欺大凡修合看顺逆

。。

3．举例说明煎药法中先煎、后下、包煎、另煎、烊化的意义。

答：1.先煎 有效成分不容易煎出的药，与不宜久煎的药同用，人汤剂时，有效成分不易煎出的药应先煎一定时间后，再纳入其余药物同煎。一般来说，动物角（如水牛角、山羊角、鹿角等）、甲（如龟甲、鳖甲等）、壳（如海蛤壳、石决明、牡蛎及珍珠母等）类药物和矿物类药物（如石膏、花蕊石、灶心土、磁石、代赭石及龙骨等），大多需要先煎30分钟左右，再纳入其他药同煎。植物药中，苦楝皮等有效成分难溶于水的药，与一般药同入汤剂时，也需先煎。另外，有的药久熬可使其毒性降低（如川乌、草乌、附子及雷公藤等），亦应先煎。制川乌、制草乌、制附子应先煎0.5～1 小时（至人口无麻味为度），雷公藤应先煎1～2小时，再纳入他药同煎，以确保用药安全。2.后下 含挥发性有效成分，久煎易挥发失效的药物（如金银花、连翘、鱼腥草、肉桂、沉香、檀香及解表药、化湿药中的大部分药），或有效成分不耐煎煮，久煎容易破坏的药（如青蒿、大黄、番泻叶、臭梧桐、麦芽、谷芽、神曲、白芥子、杏仁及钩藤等），与一般药同入汤剂时，宜后下微煎，待他药煎煮一定时间后，再纳入这类药同煎一定时间。有的药甚至只需用开水浸泡即可，不必入煎（如大黄、番泻叶用于泻下通便）。3.包煎 药材有毛对咽喉有刺激性及漂浮水面不便于煎煮者（如辛夷、旋覆花等），或药材呈粉未状及煎煮后容易使煎液混浊者（如蚕沙、海金沙、蒲黄、灶心土、五灵脂及儿茶等），以及煎煮后药液粘稠不便于滤取药汁者（如车前子等），入汤剂时都应当用纱布包裹入煎。4.另煎 部分贵重药材（如人参、西洋参、羚羊角等）与他药同用，人汤剂时宜另煎取汁，再与其他药的煎液兑服，以免煎出的有效成分被其他药的药渣吸附，造成贵重药材的浪费。5.烊化 胶类药材（如阿胶、鹿角胶、龟甲胶等）与他药同煎，容易粘锅、熬焦，或粘附于其他药渣上，既造成胶类药材的浪费，又影响其他药的有效成分的溶出，因此应当单独烊化（将胶类药物放入水中或己煎好的药液中加热溶化）兑服。

4．中药的用量有何特点？用量多少与哪些因素有关？举例说明之。答：《中药学》讨论的剂量，主要指为达到一定的治疗目的，所应用的单味药的剂量，又称用量。教材中各具体药物用量项下所标用量，系单昧药的常用有效量。这是临床确定单味药用量时的重要参考依据。用量项下的用量，除特别注明者外，都是指干燥饮片在汤剂中，成人一天内服的常用有效量。鲜品人药及药物人丸散时的用量则另加注明。剂量的单位：斤;两;钱;克

5．妇女妊娠为何忌用破血、活血及有毒中药？各列举十种妊娠禁用和慎用药。答：妇女在妊娠期间，除为了中断妊娠、引产外，禁忌使用某些药物，称为妊娠用药禁忌。妊娠用药禁忌的理由：避免引起堕胎是禁忌的主要理由。除此之外，凡对母体不利、对胎儿不利、对产程不利、对产后儿童生长发育不利的药物，对妊娠妇女均当尽量避免使用。总的说来，凡对妊娠期的母亲和胎儿不安全及不利于优生优育的药物，均属妊娠禁忌药。一般将妊娠禁忌药分为禁用药和慎用药。禁用药包括剧毒药、堕胎作用较强的药及药性作用峻猛的药，如砒石、水银、马钱子、川乌、草乌、斑蝥、轻粉、雄黄、巴豆、甘遂、大戟、芫花、牵牛子、商陆、藜芦、胆矾、瓜蒂、干漆、水蛭、虻虫、三棱、莪术及麝香等。慎用药主要是活血化淤药、行气药、攻下药及温里药等章节中的部分药，如牛膝、川芎、红花、桃仁、姜黄、枳实、枳壳、大黄、番泻叶、芦荟、芒硝、附子及肉桂等。第五章作业

1.何谓解表药？简述解表药的作用和适应证。答：以发散表邪为主要功效，常用以治疗表征的药物，称为解表药。解表药可主治外感表证，症见发热，恶寒或恶风，头身疼痛，无汗或有汗而不畅，脉浮，或有鼻塞流涕、咽痒、咳喘等表现者。发散风寒药与发散风热药，除主治风寒表证（感冒风寒）和风热表证（风热感冒）外，风邪所致的头昏头痛、目赤咽痛、皮肤瘙痒等，亦多选用；此外，本类药还分别兼有止痛及透疹等其他多种功效，因而又有其相应的主治病证。这些内容将分述于以下两节的概述之中。

2.何为中药的君臣佐使，举例说明。（书70）答：君药：是针对主病或主证起主要治疗作用的药物。臣药：是协助主药以加强治疗作用的药物。佐药：一是治疗兼证或次要症状的药物。二是用于主药有毒，或药性峻烈须加以制约者。三是反佐药，即与君药药性相反而又能在治疗中起相反作用的药物。使药：一是引经药，即引导它药直达病所的药物。二是调和药性的药物，如方剂中常用甘草，大枣以调和药性等。• 麻黄9克 发汗解表以散风寒，宣利肺气以平喘咳，为君药• 桂枝6克 发汗解肌，温经散寒助麻黄解表又除肌体疼痛，为臣药.杏仁9克 宣畅肺气助麻黄平喘，为佐药• 炙甘草3克 调和诸药，为使药 3.解表药分为哪几类？其作用和适应证有何不同？答：根据解表药的药性和功效主治差异，常将其分为:发散风寒药与发散风热药两类。有时又有称为辛温解表药与辛凉解表药者。发散风寒药性味辛温，故又称辛温解表药。其辛能外散风邪，温可祛寒，以发散肌表风寒邪气为主要功效，主治风寒表征，症见恶寒发热，头身疼痛，口不渴、苔白而润，脉浮紧，或兼咳喘，鼻塞流涕者。本类药物，性偏湿燥，多能开腠发汗，忌用于燥热内盛者；平素阴虚津亏，表虚不固而外感风寒者，亦当慎用。以发散风热为主要功效，常用以治疗风热表证及温热病卫分证的药物，称为发散风热药。或称辛凉解表药。其实，辛凉解表主要指本类药物对风热表证的治疗作用；而发散风热还包括对风热头晕头痛、风热目疾、风热咽痛、风热皮肤瘙痒等证的治疗作用，故称发散风热药更为允当。发散风热药性味多辛苦而偏寒凉，辛以祛风，苦寒则清热；其作用趋向升浮为主，多兼苦寒沉降。其发散之力较为缓和。

4.麻黄与桂枝皆能发汗解表，效果有何不同？答：麻黄：【性味归经】辛、微苦，温。归肺、膀胱经。【功效】发汗解表，平喘，利尿。【应用】用于风寒表实证，喘咳证，水肿

桂枝：【性味归经】辛、甘,温。归肺、心、肾、肝经。【功效】发汗解表，温通经脉，温助阳气。【应用】用于风寒表证，寒凝血淤及风寒痹证等多种里寒证，阳虚证。

5.比较桑叶与菊花的功效异同？答：相同点：都可以疏散风热，清肺热，清肝。不同点：黄菊可以润燥，明目。桑叶可以平肝。6.介绍详细介绍麻黄现代医学治疗哪些疾病？（书71）答：现代医学常用来治疗感冒，支气管哮喘，喘息性支气管炎，肾炎水肿，低血压等。1.感冒：以麻黄为主的复方制剂，如麻黄汤，答青龙汤等常用于治疗普通感官，流行性感冒等。2.哮喘性支气管炎，支气管哮喘：以麻黄为主配伍的止咳平喘的方剂，如麻杏石甘场，麻黄定喘汤，小青龙汤等，治疗哮喘性支气管炎，支气管哮喘等证，疗效满意，麻黄碱可以用于预防和治疗慢性轻症支气管哮喘。3.肾炎，水肿：麻黄为主的方剂，如麻黄连翘赤小豆汤肾炎所致全身水肿，小便不利等症状有一定效果。4.鼻塞：0.5%~1%麻黄素液滴鼻，可消除鼻粘膜肿胀起的鼻塞。第六章作业

1．试述清热药的含义、分类、使用注意及各类的性能特点和主要适应症。答：含义:凡药性寒凉,具有清泄里热作用的药物,称为清热药分类：清热泻火药，清热凉火药，清热解毒药，清热燥湿药，清虚热药。本类药物主要用于各种热证.所谓热证是一个很广泛的概念,它不仅指体温升高的发热,而且也泛指体温虽正常或接近正常,患者常具有某些热证症状,如口干

使用注意：使用清热药，应辨清热证的阶段、部位及虚实，选择相宜的药物。如热在气分用清热泻火药，热在营血分用清热凉血药；胃热用清胃热药，肺热用清肺热药，心热用清心热药，肝热用清肝热药；湿热证用清热燥湿药；阴虚内热证用清虚热药等。对于宜用本类药物之证，亦不可寒凉清泄太过，以免其损伤阳气，影响脾胃或化燥伤阴。使用清热药还必须以《本经》“疗热以寒药”的原则为指导，忌用于寒证，对于真寒假热者，尤应辨清，决不能误用。脾胃气虚、食少、便溏者，亦应慎用。

性能特点： 清热药是用以治疗热证的，根据药性确定的原则，相对于病性来说，其药性皆为寒性。按照苦能清泄的五味理论，清热药都可标以苦味；兼能养阴生津者，活血祛淤者，尚有甘或辛味。清热药的作用趋向是以沉降为主的。

2．比较石膏与知母，黄芩、黄连与黄柏，鲜地黄与干地黄，金银花与连翘，丹皮与赤芍，银柴胡和柴胡性能、功效与应用之异同点。答：石膏：

能清热泻火，除烦止渴。用于外感热病，高热烦渴，肺热喘咳，胃火亢盛，头痛，牙痛等。敛疮生肌，用于疮疡溃而不敛、湿疹、水火烫伤等（外用）内服只用于实证，虚证不宜用。煅石膏严禁内服。脾胃虚寒、阴虚内热忌服主要成分为含水硫酸钙，此外还含有人体所需常量的Al、Mn以及Fe、Zn、Cu等微量元素。具有解热、增强机体免疫功能、止渴、提高肌肉和外周神经的兴奋性等作用。知母：用于外感热病，高热烦渴，肺热燥咳，内热消渴，肠燥便秘等。本品含多种甾体皂苷，并含多量的粘液质。具有抗菌、解热、降血糖、影响神经体液调节功能、抑制Na+-K+-ATP酶活性、降低组织耗氧量及抗血小板聚集等作用。黄柏、黄芩、黄连三药，都是苦寒的药品，均能清热燥湿、泻火解毒。但黄柏泻肾火而退虚热，且能除下焦湿热；黄芩则以清肺热为专长，又能安胎；黄连泻心火而除烦，善止呕逆。这是三药不同之点。因此，一般所谓黄芩治上焦、黄连治中焦、黄柏治下焦的说法，就是根据黄芩清肺火、黄连止呕逆、黄柏泻肾火的特点而来的。但是，现在临床上作为清热解毒药应用时，芩、连、柏三药都是通用的，没有上述这样严格的区分。金银花：用于痈肿疗疮，喉痹，丹毒，热毒血痢，风热感冒，温病发热。连翘：治温热，丹毒，斑疹，痈疡肿毒，小便淋闭；咽喉肿痛，风疹。鲜地黄：清热生津，凉血，止血。干地黄：清热凉血，养阴，生津。牡丹皮：用于温毒发斑，吐血，夜热早凉，无汗骨蒸，经闭痛经，痈肿疮毒，跌扑伤痛。赤芍：清热凉血，活血化淤。【应用】用于温热病热入血分，淤血证。

3．石膏配知母，黄连配木香，知母配黄柏各有什么意义。答;石膏配知母的意义：清热而不伤阴液。

黄连配木香的意义：黄连与木香配伍即是方剂香连丸。湿热泻痢即是湿热之邪壅滞肠中，以致气机不畅，传导失常，而致腹痛、里急后重等症状。黄连苦寒，可清热燥湿、泻火解毒，以清泻肠胃之湿热；木香辛苦、温，有行气、调中止痛之功效，配黄连可清热止痢，行气止痛而达到治疗目的。知母配黄柏的意义：黄柏、知母均味苦同，性寒，入肾经，同具清热泻火功效，相互配伍，可以增强清相火，退虚热的功效。

4.黄芩、黄连、黄柏在清脏腑方面各有何特长 答：黄芩：黄芩能清实热，泻肺火。黄芩能泻上焦肺火。黄连： 黄连清热燥湿的作用很强 黄柏：与黄芩、黄连相似，但以除下焦之湿热为佳。黄柏燥湿泻火解毒的功效颇好。

5.从青蒿到青蒿素看现代中药的研究意义 答：在现代科学技术飞速发展的今天，通过现代科学技术对中医药的科学内涵进行证明和阐述，将不断提高中医药的学术水平，拓展自身的生存空间。在继承的同时进行创新，以获取和保护知识产权。中药现代化科技产业行动的成功，对现代科学相关学科的发展将会产生巨大的启迪和促进作用。

6.何谓清热泻火药、清热燥湿药、清热解毒药、清热凉血药、清虚热药 答：清热泻火药：寒凉性突出，善入气分，既可清解里热以治本，又可解肌以退热以治标，清热泻火为主要作用，善治疗气分实热证。清热燥湿药：药性偏于苦燥清泄，以清热燥湿为主要作用，善应用于湿热内蕴或湿邪化热的病症。清热凉血药：善入血分，以清热凉血为主要作用，善治疗血分实热证。清热解毒药：以清热解毒为主要作用，善治疗热毒，火毒证。清虚热药：多入阴份，以清虚热，退骨蒸为主要作用。善治疗热邪伤阴及阴虚潮热。

第七章作业

1.大黄的正确用法是：生大黄泻下力 强，故欲攻下者宜 生 用，入汤剂应 后 下；久煎则泻下力 减弱。酒制大黄 活血 作用较好，宜于 活血祛瘀 证。大黄炭则偏于止血，多用于 凉血止血 证。

2.何谓泻下药？使用时应注意哪些问题？答：凡以泻下通便为主要功效，常用以治疗便秘证或其他里实积滞证的药物，称为泻下药。使用注意：攻下药与峻下药容易操作正气或脾胃，故小儿、老人及体虚患者慎用，必要时应攻补兼施。对体壮里实者，亦应攻邪而不伤正，中病即止，一般得泻即可，切勿过剂。妇女妊娠期忌用、月经期及哺乳期慎用攻下和峻下药，以免损害胎儿和孕妇。对于峻猛而有毒的泻下药，应严格注意其炮制、配伍禁忌、用法及用量的特殊要求，确保用药安全而有效。

3.大黄、巴豆均可泻下，二者的适应证有何异同？答：大黄：【功效】攻下积滞，泻火解毒，凉血止血，活血祛淤，清泄实热。【应用】用于便秘及其他胃肠积滞证，温热病高热神昏或脏腑火热上炎证，血热妄行的出血证，热毒疮疡及烧烫伤，淤血热，湿热黄疸及湿热淋证。巴豆：【功效】攻下冷积，逐水退肿，祛痰利咽。【应用】用于寒积便秘腹痛或食积阻结肠胃之证，臌胀腹水，喉痺痰涎壅盛、呼吸不利。

4.现代医学大黄用于哪些疾病？（书73）答：1.便秘 2.急腹症 3.急性肠炎，菌痢，慢性结肠炎 4.黄疸肝炎 5.上消化道出血 6.产后腹痛，血瘀经闭。第8-11章作业

1.填空：附子、肉桂、干姜均能 温里散寒止痛。干姜长于温里散寒健运脾阳而止呕，附子、肉桂 撒寒止痛 力强，又能 补火助阳。肉桂还能 引火归原，温经通脉，附子、干姜能 回阳救逆。干姜还能 温肺化饮。

2.温里药在临床上宜如何配伍使用？ 答：使用本类药物应根据不同证候作适当配伍。若外寒内侵，而表寒未解者，须与发散风寒药配伍，以表里双解。寒主收引，气机易于郁滞，兼见气滞者，常与行气药配伍，以温通气机。寒性凝滞，寒凝经脉，兼见血淤者，宜与活血祛淤药配伍，以温通经脉。寒与湿合，寒湿内阻者，宜与芳香化湿或苦温燥湿药配伍，以温散寒湿。寒性主痛，寒凝疼痛较甚者，当与止痛药配伍，以散寒止痛。寒为阴邪，易伤阳气，虚寒相兼，可与补阳药配伍，以温阳散寒。若阳虚气脱者，须与大补元气药配伍，以补气回阳固脱。

3.试比较茯苓与薏苡仁功效、主治病证的共同点与不同点。答：茯苓：【功效】利水渗湿，健脾补中，宁心安神。【应用】用于水湿所致的小便不利、水肿、泄泻、痰饮、带下等证，脾虚证，心神不宁证。

薏苡仁：【功效】利水渗湿，健脾，舒筋，清热排脓。【应用】用于水湿所致的小便不利，泄泻、带下等证，风湿痹证，肺痈，肠痈。

4.附子与乌头来源相同，其功效和主治有何区别？答：川乌：【性味归经】辛、苦，热。有大毒。归肝、肾、脾经。【功效】祛风湿，散寒止痛。【应用】用于寒痹疼痛，寒凝疼痛证。

附子：【性味归经】辛、甘，热。有毒。归肾、心、肝、脾经。【功效】回阳救逆，补火助阳，散寒止痛。【应用】用于亡阳证，阳虚证，寒凝疼痛。.清热燥湿药、祛风湿药、芳香化湿药、利水渗湿药的功效和和适应证有何不同？ 答：相同点是：均为祛湿药

不同处：清热燥湿药治疗湿热性质的疾病；祛风湿药治疗风湿、类风湿性 疾病；化湿药治疗脾虚生湿，重点在于健脾；利水渗湿药治疗水湿内停证，重点在于利尿祛湿。

第12~14章作业

1.中药炮制的目的是什么？答：中药炮制的目的是降低或消除药物毒性或副作用，改变或缓和药性；提高疗效；改变或增加药物作用的部位和趋向；便于调剂和制剂；保证药物洁净度，利于贮藏；有利于服用。

2.山楂其味酸甘，具有收敛和补虚的作用吗？为什么答：：山楂虽有酸甘之味，但并不具有收敛固涩和补益正气的作用。因为山楂具有消食散瘀兼行气之功，主治食积证、血瘀、疝气等，山楂的效用显示其性散而不收。而其酸甘之味保留了山楂的口嚐滋味，同时也表示，山楂味酸可入肝经，味甘可入脾经，而达到消食化积，散瘀行气之功。

3.最适用于小儿蛔虫病的药物是什么，为什么。答：使君子。使君子可单独炒香，令小儿嚼服，一来小儿宜于服用，二来使君子驱杀蛔虫疗效确切。

4.木香、香附均可理气，其临床应用有何不同？答：木香：【功效】行气止痛。【应用】用于脾胃气滞腹痛证，大肠气滞、泻痢后重，肝胆气滞证。香附：【功效】疏肝理气，调经止痛。【应用】用于肝郁气滞证，月经不调、痛经、乳房胀痛。5.应用驱虫药时应注意哪些问题？

答：本类药物一般宜空腹时服用，使药物充分作用于虫体而保证疗效。应用毒性较大的驱虫药要注意用量、用法，以免中毒或损伤正气；同时孕妇、年老体弱者亦当慎用。虫证而腹痛剧烈者，通常以安虫为主，待疼痛缓解后，再行驱虫。对发热患者，亦宜先治其发热，待症状缓解或消失，再使用驱虫药物。6.香附醋制的作用？答：香附醋炙止痛力增强。第15~18章作业

1.举一个例子介绍既能活血，又能凉血，并能养血的药物。答;丹参既能活血调经，治疗瘀血阻滞之月经不调和其他病证，又能凉血消痈以治疮疡痈肿，并能养血安神以治热入营血，烦躁不寐，及血不养心之心悸失眠等。

2.化痰药因药性之不同而有何区别？各用于何证？答：药性有偏温，有的偏寒。而药味多根据药物的某些作用特点，并结合实际滋味来确定。如部分药物具有辛麻味，或兼有宣肺、利气之功则标辛味；部分药物来源于海生植物及动物贝壳，并有消痰散结之功，则标咸味。“肺为贮痰之器”，故本章药物主归肺经；部分药物因可主治心、肝、脾之证，则可兼归以上三经。少部分化痰药具有毒性。

3.陈皮有何功效？说明其作用机理？ 答：

1、陈皮有助于消化，因为陈皮含有类柠檬苦素，这种类柠檬苦素味平和，易溶解于水，此外，陈皮含有挥发油、橙皮甙、维生素B、C等成分，它所含的挥发油对胃肠道有温和刺激作用，可促进消化液的分泌，排除肠管内积气，增加食欲。

2、陈皮的苦味可以与其他味道相互协调，因此可以用于烹制菜肴改善味道，不但辟去鱼肉的膻腥味，且使菜肴特别可口;在凉果、食品方面，新会陈皮梅、陈皮鸭、陈皮酒，其色、香、味都具特色。此外，制作绿豆沙、红豆粥等甜品，若加入一点陈皮，味道分外芳香。

3、陈皮也是一味常用中药，味辛苦、性温，具有通气的健脾、燥湿化痰、解腻留香、降逆止呕的功效。中医的“陈皮半夏汤”、“二陈汤”是主要靠陈皮治病的，以陈皮为主要成分配制的中成药，如川贝陈皮、蛇胆陈皮、甘草陈皮、陈皮膏、陈皮末等，是化痰下气、消滞健胃的良药。适合胃部胀满、消化不良、食欲不振、咳嗽多痰等症状的人食用。4.简述瓜蒌的功效、主治证及注意事项。答：【功效】清热化痰，宽胸散结，润肠通便。【应用】用于热痰咳嗽、燥热痰咳之证，胸痹、痰热互结之胸脘胀满证，肠燥便秘。

5.百部的主治病证有哪些？答：用于多种咳嗽。外用用于蛲虫、阴道滴虫、头虱，疥癣等。蜜百部润肺止咳。用于阴虚劳嗽。6.止血药分为哪几类？其作用和适应证有何不同？答：1.收敛止血药以止血为主要功效，并兼能收涩，且性较平和的药物，称为收敛止血药。本类药物大多味涩。其性多平，或虽有微寒之性，但实无清热之功，可用于多种无明显邪气的失血证。然本类药物味涩收敛，易留淤恋邪，故应用当以出血而无明显邪气和血淤者为宜，且多与化淤止血药或活血化淤药配伍使用。属正气虚衰者，当配伍补虚药，以标本兼治。对于收兼治。对于收敛性较强的收敛止血药，有淤血及实邪者用之当慎。2.凉血止血药本类药物既能清热凉血，针对血热妄行的病因而收间接止血之效，又能直接止血。药性均为寒凉；味多苦、甘，若表示清泄，其甘多与滋味有关；因入血分凉血止血而归肝经。适用于血热妄行的出血证。原则上不宜于虚寒性出血证，但亦有某些药物，或通过炮制（炒炭），或通过配伍，亦可使用。本类药性寒凝滞，易凉遏伤阳而留淤，不宜过用。3.化淤止血药 既可止血，又能活血化淤的药物，称为化淤止血药。本类药物既能直接止血，又能活血化淤，以使血脉通畅，最适用于因淤血内阻而血不循经之出血证。此种出血，淤血不去则血不归经而出血不止，故宜以化淤止血药为主治之。亦可配伍其他各类止血药，用于各种内外出血证，同样有止血而不留淤的优点。又因其能化淤而消肿止痛，亦常用于跌打损伤及多种淤滞疼痛等。根据辛能行的理论，本类药多为辛味；其性可偏温，或偏寒；主要归肝、心二经。4.温经止血药 既可止血，又能温里散寒的药物，称为温经止血药。本类药药性温热，既能温通血脉，消散凝滞，促进血液循经运行，并扶助阳气，统摄血液，而有利于止血，又具独立的止血作用。主要适用于脾阳虚不能统血或冲脉失固之虚寒性出血证，症见出血日久，血色暗淡，且有全身虚寒表现者。本类药物又是温里之药，尚能温中以止泻、止呕，或温经散寒以调经、止痛等，故又可主治多种里寒证。

7.活血化瘀药配伍作用和适应证？ 答：功效与主治 活血化淤药均能促进血行，消散淤血，主治各种淤血证。对活血祛淤药，按其作用强度的不同常有不同的称谓。如“和血”、“和营”多指活血作用较弱，药力平和；“活血”、“化淤”、“祛淤”、“消淤”较“和血”、“和营”作用强，然力量强度又不及“破血”、“破淤”、“逐淤”等功效。后者活血化淤作用强，药力峻猛。当然，药物活血作用的强度是相对的，如剂量多少可改变其强度。由于本章药物数量较多，为了便于学习掌握，今按其作用特点和主治的不同，相对地将其分为活血止痛药、活血调经药、活血疗伤药及破血消癥药4类。配伍应用活血祛淤药的使用，应针对病情，并根据药物寒温、猛缓之性或止痛、通经、疗伤、消癥等专长，加以选择，并作适当的配伍。由于人体气血之间的密切关系，气滞可导致血淤，血淤也常兼气滞，故本类药物常需与行气药同用，以增强活血化淤的功效；寒凝血淤者，当配伍温里药以温通血脉，助活血化淤药以消散淤滞；若热灼营血而致血淤者，当配伍清热凉血药；痹证、疮痈，则应与祛风湿药或清热解毒药同用；癥瘕痞块，应同化痰软坚之品配伍；淤血而兼正虚，又当配伍相应的补虚药，以通补兼施。如淤血兼血虚或阴虚者，当同补血药或养阴药同用，阴血充足，则淤血易去；同样，若淤血而兼气虚者，当与补气药同用，气为血帅，气足则血易行，淤易去。淤血而出血者，宣配伍止血药，不可单纯止血或单纯活血。

第19~21章作业

1.简述安神药、平肝潜阳药、息风止痉药的含义和分类？答:安神药: 以宁心安神为主要作用，常用以治心神不宁之证的药物，称为安神药。平肝潜阳药: 以平抑上亢之肝阳为主要作用，常用以治疗肝阳上亢证的药物，称平肝潜阳药，或称平抑肝阳药，简称平肝药等。息风止痉药: 以平息肝风，制止痉挛抽搐为主要作用，常用以治肝风内动证的药物，称息风止痉药。可简称息风药或止痉药。

2.远志和酸枣仁均有安神之功，如何区别使用？ 答:酸枣仁: 【性味归经】甘、酸，平。归心、肝经。【功效】养心安神，敛汗。【应用】用于心神不宁之证，体虚多汗。远志: 【性味归经】苦、辛，微温。归心、肾、肺经。【功效】宁心安神，化痰开窍。【应用】用于心神不宁之证，癫狂、痫证，咳嗽痰多。

3.羚羊角、天麻与钩藤三药功效、主治病证，如何区别使用？答: 相同点：三药均能平肝息风、平抑肝阳，均可治肝风内动，肝阳上亢之证。不同点：羚羊角咸寒，清热力强，除善治热极生风证外，又能清心解毒，多用于高热神昏，热毒、发斑等。钩藤性凉，轻清透达，长于清热息风，用治高热惊风轻证为宜。天麻甘平柔润，清热之力不及羚羊角、钩藤，但治肝风内动、惊痫抽搐之证，虚实寒热皆可选用。又兼祛风通络，用治肢体麻木，手足不遂，风湿痹痛。4.牛黄简述的功效及适应证？答: 【功效】息风止痉，清心肝热，化痰开窍，清热解毒。

【应用】用于温热病热级生风、小儿肝热惊风等肝风内动证，温热病热入心包、中风等窍闭神昏证，咽喉肿痛，外科疮痈等。第22~23章作业

1.简述开窍药的性能特点及适应证？答：

一、含义 以开通心窍，启闭醒神为主要作用，常用以治疗闭证神昏的药物开窍药。

二、功效与主治 开窍药均具有开窍醒神功效，主治闭证神昏之证。闭证是指各种实邪阻闭心窍导致神志昏迷的一类证候。闭证神昏多由热邪内陷心包，或痰湿、秽浊、淤血等实邪阻闭心窍，致使心所主之神明失用，而见神志昏迷，不省人事，牙关紧闭，两手固握有力，或谵语等实证表现。主要用于温热病、中风、惊风、痫证、中暑、胸痹及食物不洁等病证之神志昏迷。

三、性能特点 本章药物的药性与主治病证间无明显对应关系，但历来将大多数药标温性，以表示其温通之效。开窍药大多具有浓郁的芳香之气，并能醒神复苏，故标辛味。“心主神明”，邪气闭阻心窍则神昏，本类药主归心经。开窍辛香走窜，而具升浮之性。除蟾酥、樟脑有毒外，其余药物在规定剂量范围内且短时间应用，一般视为无毒。

2.枸杞子的临床应用有哪些？答：枸杞子可调节机体免疫功能、能有效抑制肿瘤生长和细胞突变、具有延缓衰老、抗脂肪肝、调节血脂和血糖等方面的作用。因此，枸杞子对糖尿病、血脂异常症、肝功能异常、胃炎等都有一定的治疗作用。3.简述补阳药与温里药有何不同？答：同：药性都是温热的 异：凡能温里祛寒，用以治疗里寒症候的药物，称为温里药

补阳药就是能补益人体阳气，消除改善阳虚病证的药物。从概念上就有区别，但是通常补阳药均带有温里的作用，而温里药只要温里作用，没有补益阳气的作用。

4.简述熟地黄与生地黄功用的异同点？答：共同点：养阴，同可用治阴虚潮热，津伤口渴，消渴证。

不同点：生地黄又可清热、凉血、止血，常用治热入营血，舌绛烦渴，斑疹吐衄，及温病后期，余热未尽之夜热早凉，舌红脉数者医`学敎育网搜`集整理。而熟地黄又可养血，填精益髓，常用治血虚萎黄，眩晕，心悸，失眠及月经不调，崩中漏下，或精髓亏虚之腰膝酸软、遗精、盗汗、耳鸣、耳聋、须发早白及消渴者。

5.详细介绍人参的作用？答： 1 调节中枢神经系统：人参能调节中枢神经系统，改善大脑的兴奋与抑制过程，使之趋于平衡；能提高脑力与体力劳动的能力，提高工作效率，并有抗疲劳的作用。2 促进大脑对能量物质的利用，可以提高学习记忆能力人参中增强学习和记忆能力的有效成分为人参皂苷，其中人参皂苷Rb1和Rg1，对学习和记忆功能均有良好影响。人参根皂苷对正常大鼠学习、记忆过程有促进作用，而人参茎叶皂苷对电休克所致的大鼠记忆障碍有明显的改善作用。两者均使正常大鼠不同脑区的单胺类递质含量明显增多。这些研究工作，对合理应用人参植物资源有一定参考价值。3 改善心脏功能：人参能增加心肌收缩力，减慢心率，增加心输出量与冠脉血流量，可抗心肌缺血与心律失常。对心脏功能、心血管、血流都有一定的影响。人参有明显的耐缺氧作用，其制剂可有效地对抗窦性心率失常。人参皂苷可加快脂质代谢，并具有明显降低高胆固醇的作用。小剂量人参可使麻醉动物血压轻度上升，大剂量则使血压下降。不同的人参制剂对离体蟾蜍心脏及在体兔、猫、犬心脏皆有增强其功能的作用，并可改善其心室纤颤时的心肌无力。

羚羊角的作用：平肝息风，清肝明目，凉血解毒的功效，主治肝风内动，惊痫抽搐，谵语发狂，肝阳头痛眩晕，肝火目赤肿痛，血热出血，温病发斑等。杏仁和苏子的作用有什么区别：都能止咳平喘，润肠通便合作用，都可用于咳喘、肠燥便秘等症。但它们不下列不同之处：(1）杏仁：苦杏仁既能降气平喘，又可呈肺化湿，适用咳喘实证；甜杏仁又能滋阴，可月于肺阴虚久咳、燥咳。(2）苏子：性味辛温，偏于温中消痰，适用户肺寒多痰咳喘，如《 别录》 说：“主下气，除寒温户。”大明本草》 说：“止嗽，润心肺，消痰气。”