斯托克《计量经济学》Ch3

计量经济学斯托克沃森第三版答案1、盈余公积是企业从（）中提取的公积金。

[单选题] *A.税后净利润(正确答案)B.营业利润C.利润总额D.税前利润2、企业在转销已经确认无法支付的应付账款时，应贷记的会计科目是（）。

[单选题] *A.其他业务收入B.营业外收入(正确答案)C.盈余公积D.资本公积3、企业因解除与职工的劳动关系给予职工补偿而发生的职工薪酬，应借记的会计科目是（）。

[单选题] *A.管理费用(正确答案)B.计入存货成本或劳务成本C.营业外支出D.计入销售费用4、由投资者投资转入的无形资产，应按合同或协议约定的价值，借记“无形资产”科目，按其在注册资本所占的份额，贷记“实收资本”科目，按其差额记入（）科目。

[单选题] *A.“资本公积—资本溢价”(正确答案)B.“营业外收入”C.“资本公积—其它资本公积”D.“营业外支出”5、下列各项，不影响企业营业利润的项目是（）。

[单选题] *A.主营业务收入B.其他收益C.资产处置损益D.营业外收入(正确答案)6、2018年12月31日，甲公司某项固定资产计提减值准备前的账面价值为1 000万元，公允价值为980万元，预计处置费用为80万元，预计未来现金流量的现值为1 050万元。

2018年12月31日，甲公司应对该项固定资产计提的减值准备为（）万元。

[单选题] *A.0(正确答案)B.20C.50D.1007、．（年浙江省高职考）下列各项中，属于会计对经济活动进行事中核算的主要形式的是（）[单选题] *A预测B决策C计划D控制(正确答案)8、专利权有法定有效期限，一般发明专利的有效期限为（）。

[单选题] *A.5年B.10年C.15年D.20年(正确答案)9、．（年预测）下列属于货币资金转换为生产资金的经济活动的是（）[单选题] *A购买原材料B生产领用原材料C支付工资费用(正确答案)D销售产品10、．（年浙江省第一次联考）下列各项中，不属于会计核算的前提条件的是（）[单选题] *A持续经营B货币计量C权责发生制(正确答案)D会计主体11、下列各项税金中不影响企业损益的是（）。

Chapter 9. Assessing Studies Based on Multiple Regression9.1 Internal and External ValidityMultiple regression has some key virtues:•It provides an estimate of the effect on Y of arbitrarychanges ΔX.•It resolves the problem of omitted variable bias, if anomitted variable can be measured and included.•It can handle nonlinear relations (effects that varywith the X’s)Still, OLS might yield a biased estimator of the true causal effect.A Framework for Assessing Statistical Studies Internal and External Validity•Internal validity: The statistical inferences aboutcausal effects are valid for the population beingstudied.•External validity: The statistical inferences can be generalized from the population and setting studied to other populations and settings, where the “setting” refers to the legal, policy, and physical environment and related salient features.Threats to External ValidityHow far can we generalize class size results from California school districts?•Differences in populationso C alifornia in 2005?o M assachusetts in 2005?o M exico in 2005?•Differences in settingso Different legal requirements concerning specialeducationo Different treatment of bilingual educationo Differences in teacher characteristics9.2 Threats to Internal Validity of Multiple Regression AnalysisInternal validity: The statistical inferences about causal effects are valid for the population being studied.Five threats to the internal validity of regression studies:1.Omitted variable bias2.Wrong functional form3.Errors-in-variables bias4.Sample selection bias5.Simultaneous causality biasAll of these imply that E(u i |X1i, …, X ki) ≠ 0.1. Omitted variable biasArises if an omitted variable both (i) is a determinant of Y and (ii) is correlated with at least one included regressor.Potential solutions to omitted variable bias•If the variable can be measured, include it as aregressor in multiple regression;•Possibly, use panel data in which each entity(individual) is observed more than once;•If the variable cannot be measured, use instrumentalvariables regression;•Run a randomized controlled experiment.2. Wrong functional formArises if the functional form is incorrect – for example, an interaction term is incorrectly omitted; then inferences on causal effects will be biased.Potential solutions to functional form misspecification •Continuous dependent variable: Use the“appropriate” nonlinear specifications in X(logarithms, interactions, etc.)•Discrete (example: binary) dependent variable:Need an extension of multiple regression methods(“probit” or “logit” analysis for binary dependentvariables).3. Errors-in-variables biasSo far we have assumed that X is measured without error. In reality, economic data often have measurement error •Data entry errors in administrative data•Recollection（记忆）errors in surveys (when did you start your current job?)•Ambiguous questions problems (what was yourincome last year?)•Intentionally false response（故意假报）problems with surveys (What is the current value of your financialassets? How often do you drink and drive?)In general, measurement error in a regressor results in “errors-in-variables” bias.Illustration : SupposeY i = β0 + β1X i + u iis “correct” in the sense that the three least squares assumptions hold (in particular E (u i | X i ) = 0).LetX i = unmeasured true value of XiX = imprecisely measured version of XThenY i = β0 + β1X i + u i = β0 + β1i X + [β1(X i –i X ) + u i ]orY i = β0 + β1i X +i u , where i u = β1(X i –i X ) + u iIf i X is correlated with i u , then 1ˆβ will be biased: cov(i X ,i u ) = cov(i X , β1(X i –iX ) + u i ) = β1cov(i X , X i – i X ) + cov(iX , u i ) = β1[cov(i X , X i ) – var(iX )] + 0 ≠ 0because in general cov(i X , X i ) ≠ var(iX ).Y i = β0 + β1i X +i u , where i u = β1(X i –i X ) + u i• If X i is measured with error, iX is in general correlated with i u , so 1ˆβ is biased and inconsistent.• It is possible to derive formulas for this bias, but theyrequire making specific mathematical assumptions about the measurement error process (for example,that i u and X i are uncorrelated). Those formulas are special and particular, but the observation that measurement error in X results in bias is general.Potential solutions to errors-in-variables bias•Obtain better data.•Develop a specific model of the measurement error process.•This is only possible if a lot is known about thenature of the measurement error – for example asubsample of the data are cross-checked usingadministrative records and the discrepancies areanalyzed and modeled. (Very specialized; we won’t pursue this here.)•Instrumental variables regression.4. Sample selection biasSo far we have assumed simple random sampling of the population. In some cases, simple random sampling is thwarted because the sample, in effect, “selects itself.”Sample selection bias arises when a selection process (i) influences the availability of data and (ii) that process is related to the dependent variable.Example #1: Mutual funds•Do actively managed mutual funds outperform“hold-the-market” funds?•Empirical strategy:o Sampling scheme: Simple random sampling of mutual funds available to the public on a givendate.o Data: Returns for the preceding 10 years.o Estimator: Average ten-year return of the sample mutual funds, minus ten-year return on S&P500 o Is there sample selection bias?Sample selection bias induces correlation between a regressor and the error term.Mutual fund example:return i = β0 + β1managed_fund i + u iBeing a managed fund in the sample (managed_fund i = 1) means that your return was better than failed managed funds, which are not in the sample – so corr(managed_fund i, u i) ≠ 0.Example #2: Returns to education•What is the return to an additional year of education? •Empirical strategy:o S ampling scheme: Simple random sampling ofworkerso D ata: Earnings and years of educationo E stimator: regress ln(earnings) on years_education o I gnore issues of omitted variable bias andmeasurement error – is there sample selectionbias?Potential solutions to sample selection bias•Collect the sample in a way that avoids sample selection.o M utual funds example: Change the samplepopulation from those available at the end of theten-year period, to those available at thebeginning of the period (include failed funds)o R eturns to education example: Sample collegegraduates, not workers (include the unemployed) •Randomized controlled experiment.•Construct a model of the sample selection problem and estimate that model (we won’t do this).5. Simultaneous Causality BiasSo far we have assumed that X causes Y.What if Y causes X, too?Example: Class size effect•Low STR results in better test scores•But suppose districts with low test scores are given extra resources: as a result of a political process they also have low STR•What does this mean for a regression of TestScore on STR?Simultaneous causality bias in equations(a) Causal effect of X on Y: Y i = β0 + β1X i + u i(b) Causal effect of Y on X: X i = γ0 + γ1Y i + v i •Large u i means large Y i, which implies large X i (if γ1>0) •Thus corr(X i, u i) ≠ 0ˆβis biased and inconsistent.•Thus1•Example: A district with particularly bad test scores given the STR (negative u i) receives extra resources, thereby lowering its STR; so STR i and u i are correlated.Potential solutions to simultaneous causality bias•Randomized controlled experiment. Because X i ischosen at random by the experimenter, there is nofeedback from the outcome variable to Y i (assuming perfect compliance).•Develop and estimate a complete model of both directions of causality. This is the idea behind many large macro models (e.g. Federal Reserve Bank-US). This is extremely difficult in practice.•Use instrumental variables regression to estimate the causal effect of interest (effect of X on Y, ignoring effect of Y on X).9.3 Internal and External Validity When the Regression Is Used for ForecastingUp to now, we have focused on the estimation of causal effects.But regression models can also be used for forecasting. In this case, concerns about external validity are very important, but concerns about unbiased estimation of causal effects are not.例：“街上行人带伞”可以预测“下雨”，但前者并不构成后者的原因。

计量经济学斯托克答案【篇一：计量经济学教材推荐】txt>【计量经济学的内容体系】古扎拉蒂《计量经济学基础》白砂堤津耶《通过例题学习计量经济学》伍德里奇《计量经济学导论：现代观点》斯托克、沃森《计量经济学导论》林文夫（fumio hayashi）《计量经济学》雨宫健（takeshi amemiya ）《高级计量经济学》李子奈、潘文卿编著《计量经济学》【计量经济学的内容体系】狭义的计量经济学以揭示经济现象中的因果关系为目的，主要应用回归分析方法。

广义的计量经济学是利用经济理论、统计学和数学定量研究经济现象的经济计量方法，除了回归分析方法，还包括投入产出分析法、时间序列分析方法等。

把计量经济学分为初级、中级、高级三个层次，初级计量经济学一般包括计量经济学所必须的基础数理统计只是和矩阵代数只是、经典的线性计量经济学模型理论与方法（以单一方程模型为主）、单方程模型的应用等内容；中级计量经济学以经典的线性计量经济学模型理论与方法及其应用为主要内容，包括单一方程模型和联立方程模型。

在应用方面，主要讨论计量经济学模型在生产、需求、消费、投资、货币需求和宏观经济系统等传统领域的应用，注重于应用过程中实际问题的处理。

在描述方法上普遍运用矩阵描述；高级计量经济学以扩展的线性模型理论与方法、非线性模型理论与方法和动态模型理论与方法，以及它们的应用为主要内容。

从研究对象和侧重点的角度讲，理论计量经济学侧重于理论与方法的数学证明与推导，与数理统计联系极为密切；应用计量经济学则以建立与应用计量经济学模型为主要内容，强调应用模型的经济学和统计学基础，侧重于建立与应用模型过程中实际问题的处理。

纵观计量经济学发展史，20世纪70年代之前发展并广泛应用的计量经济学称为经典计量经济学，其理论特征是：以经济理论为导向建立因果分析的随机模型，模型具有明确的形式和参数，模型变量之间的关系多表现为线性关系，或者可以化为线性关系，以时间序列数据或者截面数据为样本，采用最小二乘方法或者极大似然方法估计模型。

斯托克计量经济学教材全文共四篇示例，供读者参考第一篇示例：斯托克计量经济学教材是一本经典的经济学教材，被广泛应用于大学本科和研究生阶段的经济学专业课程中。

该教材由文字严谨，内容深入浅出，涵盖了计量经济学的各个方面，为学生提供了全面的理论知识和实践技能。

一、教材内容《斯托克计量经济学》是由詹姆斯·斯托克（James Stock）和马克·沃森（Mark Watson）合著的一本经典教材，在经济学界享有盛誉。

该教材涵盖了计量经济学的基本理论、方法和实证研究，内容涉及回归分析、时间序列分析、面板数据分析、因果推断等多个方面，旨在帮助学生建立起对经济现象的客观量化分析能力。

二、教材特点1. 理论与实践相结合：《斯托克计量经济学》教材注重理论与实践相结合，既强调了基本的计量经济学理论框架，又通过大量实证案例展示了如何将理论运用到实际数据分析中。

2. 清晰易懂的讲解方式：该教材在文字表达上十分严谨清晰，避免了学术术语的过度使用，让学生更容易理解和掌握复杂的计量经济学理论。

3. 大量练习题和案例分析：为了帮助学生更好地掌握知识点，教材中设置了大量的练习题和案例分析，让学生通过实际操作来巩固所学知识。

4. 近年最新研究成果：《斯托克计量经济学》不仅汇总了经典的计量经济学研究成果，还尽可能地涵盖了最新的研究进展和方法，使学生对计量经济学领域的发展趋势有所了解。

三、教材在教学中的应用斯托克计量经济学教材以其深入浅出的讲解方式、丰富实例和案例、以及严谨的理论基础，成为了经济学领域不可或缺的经典教材，为学生们打开了通往计量经济学世界的大门，引导他们更好地理解和应用计量经济学知识，为未来的学习和研究提供了坚实的基础。

希望更多的学生能通过学习《斯托克计量经济学》，在经济学领域取得更为出色的成就。

第二篇示例：斯托克（Stock）是计量经济学领域内享有盛誉的学者，他的著作《计量经济学》（Introduction to Econometrics）广泛应用于全球各大高校的计量经济学课程中。

计量经济学复习笔记CH1导论1、计量经济学：以经济理论和经济数据的事实为依据，运用数学、统计学的方法，通过建立数学模型来研究经济数量关系和规律的一门经济学科。

研究主体是经济现象及其发展变化的规律。

2、运用计量分析研究步骤：模型设定——确定变量和数学关系式估计参数——分析变量间具体的数量关系模型检验——检验所得结论的可靠性模型应用——做经济分析和经济预测3、模型变量:解释变量:表示被解释变量变动原因的变量，也称自变量，回归元。

被解释变量:表示分析研究的对象，变动结果的变量，也成应变量。

内生变量:其数值由模型所决定的变量，是模型求解的结果。

外生变量:其数值由模型意外决定的变量。

外生变量数值的变化能够影响内生变量的变化，而内生变量却不能反过来影响外生变量。

前定内生变量：过去时期的、滞后的或更大范围的内生变量，不受本模型研究范围的内生变量的影响，但能够影响我们所研究的本期的内生变量。

前定变量：前定内生变量和外生变量的总称。

数据:时间序列数据：按照时间先后排列的统计数据。

截面数据：发生在同一时间截面上的调查数据。

面板数据：虚拟变量数据：表征政策，条件等，一般取0或1.4、估计评价统计性质的标准无偏：E（＾β）=β 随机变量，变量的函数？有效：最小方差性一致：N趋近无穷时，β估计越来越接近真实值5、检验经济意义检验：所估计的模型与经济理论是否相等统计推断检验：检验参数估计值是否抽样的偶然结果，是否显著计量经济检验：是否符合计量经济方法的基本假定预测检验：将模型预测的结果与经济运行的实际对比CH2 CH3 线性回归模型模型（假设）——估计参数——检验——拟合优度——预测1、模型（线性）（1）关于参数的线性 模型就变量而言是线性的；模型就参数而言是线性的。

Y i =β1+β2lnX i +u i线性影响 随机影响Y i =E （Y i |X i ）+u i E （Y i |X i ）=f(X i )=β1+β2lnX i引入随机扰动项，（3）古典假设A 零均值假定 E （u i |X i ）=0B 同方差假定 Var(u i |X i )=E(u i 2)=σ2C 无自相关假定 Cov(u i ,u j )=0D 随机扰动项与解释变量不相关假定 Cov(u i ,X i )=0E 正态性假定u i ~N(0,σ2)F 无多重共线性假定Rank(X)=k2、估计在古典假设下，经典框架，可以使用OLS方法：OLS 寻找min ∑e i2 ＾β1ols = (Y 均值)-＾β2(X 均值)＾β2ols = ∑x i y i /∑x i 23、性质OLS 回归线性质（数值性质）（1）回归线通过样本均值 （X 均值,Y 均值）（2）估计值＾Y i 的均值等于实际值Y i 的均值（3）剩余项e i 的均值为0（4）被解释变量估计值＾Y i 与剩余项e i 不相关 Cov(＾Y i ,e i )=0（5）解释变量X i 与剩余项e i 不相关 Cov(e i ,X i )=0在古典假设下，OLS 的统计性质是BLUE 统计 最佳线性无偏估计4、检验(1)Z 检验Ho:β2=0 原假设 验证β2是否显著不为0标准化： Z=（＾β2-β2）/SE （＾β2）~N （0,1） 在方差已知，样本充分大用Z 检验拒绝域在两侧，跟临界值判断，是否β2显著不为0(2)t 检验——回归系数的假设性检验方差未知，用方差估计量代替 ＾σ2=∑e i 2/(n-k) 重点记忆t =（＾β2-β2）/＾SE （＾β2）~t （n-2）拒绝域：|t|>=t 2/a (n-2)拒绝，认为对应解释变量对被解释变量有显著影响。

计量经济学EconometricsChapter 1. Economic Questions and Data计量经济学的定义：Econometrics is the science and art of using economic theory and statistical techniques to analyze economic data.其他定义：（1） The science of testing economic theory.（2） The set of tools used for forecasting future values of economic variables.（3） The process of fitting mathematical economic models to real-world data.（4） The science and art of using historical data to make quantitative policy recommendations in government and business.计量经济学的用途：经济学的各个领域；政治学；社会学等Why econometrics?Economics suggests interesting relations, often with policy implications, but virtually never suggests quantitative magnitudes of causal effects.•What is the price elasticity of cigarettes?•What is the effect of reducing class size on studentachievement?•What is the effect on earnings of a year of education? •What is the effect on output growth of a 1 percentage point increase in interest rates by the Fed?The focus of this course is the use of statistical and econometric methods to quantify causal effects.1.1 Economic Questions We ExamineQuestion #1: Does Reducing Class Size Improve Elementary School EducationThe marginal benefit and cost of reducing class sizeData: 420 California school districts in 1998Evidence: Students in districts with small class sizes tend to perform better on standardized tests than in districts with larger classes.Complications: While this fact is consistent with the idea that smaller classes produce better test scores, it might simply reflect many other advantages that students in districts with small classes have (e.g. wealthier residents, better teachers, more extra learning outside classroom).Solution: We use multiple regression（多元回归）analysis to isolate the effect of changes in class size from changes in other factors, such as the economic background of the students（引入足够多的控制变量）.Question #2: Is There Racial Discrimination in the Market for Home Loans?By law, U.S. lending institutions cannot take race intoaccount when deciding to grant or deny a request for a mortgage.美国的种族歧视VS 中国的户籍歧视Evidence: Federal Reserve Bank of Boston found (using data from the early 1990s) that 28% of black applicants are denied mortgages, while only 9% of white applicants are denied.Complication: Black and white applicants differ in many ways other than their races.Need to know: Is there a difference in the probability of being denied for otherwise identical applicants（除种族以外，其他方面都完全相同的贷款申请者）? How large is this difference?Solution: Hold constant other application characteristics, suchas their ability to repay the loan.Question #3: How Much Do Cigarette Taxes Reduce Smoking?由于吸烟的外部性（externality），故政府希望对香烟征税，以降低香烟消费。

Chapter 8. Nonlinear Regression Functions8.1 A General Strategy for Modeling Nonlinear Regression Functions•Everything so far has been linear in the X’s•The approximation that the regression function is linearmight be good for some variables, but not for others.•The multiple regression framework can be extended to handle regression functions that are nonlinear in one or more X.The TestScore – STR relation looks approximately linear…But the TestScore – average district income relation looks like it is nonlinear.If a relation between Y and X is nonlinear:•The effect on Y of a change in X depends on the value of X – that is, the marginal effect of X is not constant•A linear regression is mis-specified – the functional form is wrong•The estimator of the effect on Y of X is biased – it needn’t even be right on average. 遗漏高次项会带来遗漏变量偏差。