图书介绍
计量经济学导论 第4版PDF|Epub|txt|kindle电子书版本网盘下载
- JeffreyM·Wooldridge，王少平著 著
- 出版社： 北京：高等教育出版社
- ISBN：9787040395945
- 出版时间：2014
- 标注页数：481页
- 文件大小：74MB
- 文件页数：497页
- 主题词：计量经济学－高等学校－教材－英文
PDF下载
下载说明
计量经济学导论 第4版PDF格式电子书版下载
下载的文件为RAR压缩包。需要使用解压软件进行解压得到PDF格式图书。建议使用BT下载工具Free Download Manager进行下载,简称FDM(免费,没有广告,支持多平台）。本站资源全部打包为BT种子。所以需要使用专业的BT下载软件进行下载。如BitComet qBittorrent uTorrent等BT下载工具。迅雷目前由于本站不是热门资源。不推荐使用！后期资源热门了。安装了迅雷也可以迅雷进行下载！
（文件页数 要大于 标注页数，上中下等多册电子书除外）
注意：本站所有压缩包均有解压码： 点击下载压缩包解压工具
图书目录
Chapter 1 The Nature of Econometrics and Economic Data1
1.1 What Is Econometrics?1
1.2 Steps in Empirical Economic Analysis2
1.3 The Structure of Economic Data5
1.4 Causality and the Notion of Ceteris Paribus in Econometric Analysis12
Summary16
Key Terms17
Computer Exercises17
PART 1 Regression Analysis With Cross-Sectional Data19
Chapter 2 The Simple Regression Model20
2.1 Definition of the Simple Regression Model20
2.2 Deriving the Ordinary Least Squares Estimates25
2.3 Properties of OLS on Any Sample of Data34
2.4 Units of Measurement and Functional Form39
2.5 Expected Values and Variances of the OLS Estimators44
2.6 Regression through the Origin56
Summary57
Key Terms58
Computer Exercises59
Appendix 2A61
Chapter 3 Multiple Regression Analysis:Estimation63
3.1 Motivation for Multiple Regression63
3.2 Mechanics and Interpretation of Ordinary Least Squares68
3.3 The Expected Value of the OLS Estimators80
3.4 The Variance of the OLS Estimators90
3.5 Efficiency of OLS:The Gauss-Markov Theorem99
Summary100
Key Terms101
Computer Exercises102
Appendix 3A105
Chapter 4 Multiple Regression Analysis:Inference109
4.1 Sampling Distributions of the OLS Estimators109
4.2 Testing Hypotheses about a Single Population Parameter:The t Test112
4.3 Confidence Intervals130
4.4 Testing Hypotheses about a Single Linear Combination of the Parameters132
4.5 Testing Multiple Linear Restrictions:The F Test135
4.6 Reporting Regression Results146
Summary148
Key Terms150
Computer Exercises151
Chapter 5 Multiple Regression Analysis:OLS Asymptotics154
5.1 Consistency154
5.2 Asymptotic Normality and Large Sample Inference159
5.3 Asymptotic Efficiency of OLS166
Summary167
Key Terms168
Computer Exercises168
Appendix 5A169
Chapter 6 Multiple Regression Analysis with Qualitative Information:Binary(or Dummy)Variables171
6.1 Describing Qualitative Information171
6.2 A Single Dummy Independent Variable172
6.3 Using Dummy Variables for Multiple Categories179
6.4 Interactions Involving Dummy Variables184
6.5 A Binary Dependent Variable:The Linear Probability Model192
6.6 More on Policy Analysis and Program Evaluation197
Summary200
Key Terms201
Computer Exercises201
Chapter 7 HeterOskedasticity207
7.1 Consequences of Heteroskedasticity for OLS207
7.2 Heteroskedasticity-Robust Inference after OLS Estimation208
7.3 Testing for Heteroskedasticity212
7.4 Weighted Least Squares Estimation218
7.5 The Linear Probability Model Revisited231
Summary234
Key Terms234
Computer Exercises235
Chapter 8 More on Specification238
8.1 Functional Form Misspecification238
Summary244
Key Terms244
Computer Exercises244
PART 2 Regression Analysis with Tine Series Data245
Chapter 9 Basic Regression Analysis with Time Series Data246
9.1 The Nature of Time Series Data246
9.2 Examples of Time Series Regression Models248
9.3 Finite Sample Properties of OLS under Classical Assumptions251
9.4 Functional Form,Dummy Variables,and Index Numbers259
9.5 Trends and Seasonality266
Summary276
Key Terrns277
Computer Exercises277
Chapter 10 Further Issues in Using OLS with Time Series Data281
10.1 Stationary and Nonstationary Time Series281
10.2 Asymptotic Properties of OLS283
10.3 Using Highly Persistent Time Series in Regression Analysis289
Summary296
Key Terms297
Computer Exercises297
Chapter 11 Serial Correlation and Heteroskedasticity in Time Series Regressions302
11.1 Properties of OLS with Serially Correlated Errors302
11.2 Testing for Serial Correlation306
11.3 Correcting for Serial Correlation with Strictly Exogenous Regressors313
11.4 Differencing and Serial Correlation320
11.5 Serial Correlation-Robust Inference after OLS322
11.6 Heteroskedasticity in Time Series Regressions326
Summary331
Key Terms331
Computer Exercises332
PART 3 Advanced Topics337
Chapter 12 Advanced Panel Data Methods338
12.1 Fixed Effects Estimation338
12.2 Random Effects Models346
12.3 Applying Panel Data Methods to Other Data Structures351
Summary353
Key Terms353
Computer Exercises354
Appendix 12A357
Chapter 13 Instrumental Variables Estimation and Two Stage Least Squares360
13.1 Motivation：Omitted Variables in a Simple Regression Model361
13.2 IV Estimation of the Multiple Regression Model371
13.3 Two Stage Least Squares375
13.4 IV Solutions to Errors-in-Variables Problems379
13.5 Testing for Endogeneity and Testing Overidentifying Restrictions381
13.6 2SLS with Heteroskedasticity385
13.7 Applying 2SLS to Time Series Equations385
Summary388
Key Terms388
Computer Exercises388
Appendix 13A392
Chapter 14 Limited Dependent Variable Models395
14.1 Logit and Probit Models for Binary Response396
14.2 The Tobit Model for Corner Solution Responses408
Summary416
Key Terms417
Computer Exercises417
Chapter 15 Advanced Time Series Topics422
15.1 Infinite Distributed Lag Models423
15.2 Testing for Unit Roots429
15.3 Cointegration and Error Correction Models435
15.4 Forecasting441
Summary450
Key Terms451
Computer Exercises451
Appendix A The NormaI and Related Distributions455
Appendix B Answers to Chapter Questions463
Appendix C Statistical Tables470
References477