Availability: Introductory econometrics | Technische Universität München

Introductory econometrics: a modern approach

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Bibliographic Details
Main Author:	Wooldridge, Jeffrey M. 1960- (Author)
Format:	Book
Language:	English
Published:	[Mason, Ohio u.a.] South-Western Cengage Learning 2013
Edition:	5. ed., internat. ed.
Subjects:	Ökonometrie Lehrbuch
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024453638&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Physical Description:	XXV, 881 S. graph. Darst.
ISBN:	9781111531041 9781111534394 1111531048 111153439X

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adam_text	BRIEF CONTENTS Chapter 1 The Nature of Econometrics and Economic Data 1 PART 1 : Regression Analysis with Cross-Sectional Data Chapter 2 The Simple Regression Model Chapter 3 Multiple Regression Analysis: Estimation Chapter 4 Multiple Regression Analysis: Inference Chapter 5 Multiple Regression Analysis: OLS Asymptotics Chapter 6 Multiple Regression Analysis: Further Issues Chapter 7 Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables Chapter 8 Heteroskedasticity Chapter 9 More on Specification and Data Issues PART 2: Regression Analysis with Time Series Data Appendix 2A Appendix ЗА Appendix 5A Appendix 6A Appendix 13A Appendix 14A Appendix 15A Appendix 17A Appendix 17B 19 ~~ГО 64 110 160 178 217 258 293 331 332 368 398 431 Chapter 10 Basic Regression Analysis with Time Series Data Chapter 11 Further Issues in Using OLS with Time Series Data Chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions PART 3: Advanced Topics Chapter 13 Pooling Cross Sections Across Time: Simple Panel Data Methods 432 Chapter 14 Advanced Panel Data Methods 466 Chapter 15 Instrumental Variables Estimation and Two Stage Least Squares 490 Chapter 16 Simultaneous Equations Models 530 Chapter 17 Limited Dependent Variable Models and Sample Selection Corrections 559 Chapter 18 Advanced Time Series Topics 606 Chapter 19 Carrying Out an Empirical Project 650 CHAPTER APPENDICES 678 680 684 685 686 689 692 694 695 Brief Contents ши APPENDICES Appendix A Basic Mathematical Tools 697 Appendix В Fundamentals of Probability 716 Appendix С Fundamentals of Mathematical Statistics 749 Appendix D Summary of Matrix Algebra 790 Appendix E The Linear Regression Model in Matrix Form 801 Appendix F Answers to Chapter Questions 815 Appendix G Statistical Tables 825 References 832 Glossary 838 Index 856 j f Preface xvii About the Author xxvii CHAPTER 1 The Nature of Econometrics and Economic Data 1 1.1 What Is Econometrics? 1 1.2 Steps in Empirical Economic Analysis 2 1.3 The Structure of Economic Data 5 Cross-Sectional Data 5 Time Series Data 8 Pooled Cross Sections 9 Panel or Longitudinal Data 10 A Comment on Data Structures 11 1.4 Causality and the Notion of Ceteris Paribus in Econometric Analysis 12 Summary 16 Key Terms 17 Problems 17 Computer Exercises 17 PART 1 Regression Analysis with Cross-Sectional Data 19 CHAPTER 2 The Simple Regression Model 20 2.1 Definition of the Simple Regression Model 20 2.2 Deriving the Ordinary Least Squares Estimates 25 A Note on Terminology 32 2.3 Properties of OLS on Any Sample of Data 33 Fitted Values and Residuals 33 Algebraic Properties of OLS Statistics 34 Goodness-of-Fit 36 2.4 Units of Measurement and Functional Form 37 The Effects of Changing Units of Measurement on OLS Statistics 38 Incorporating Nonlinearities in Simple Regression 39 The Meaning of Linear Regression 42 2.5 Expected Values and Variances of the OLS Estimators 43 Unbiasedness of OLS 43 Variances of the OLS Estimators 48 Estimating the Error Variance 52 2.6 Regression through the Origin and Regression on a Constant 55 Summary 56 Key Terms 57 Problems 58 Computer Exercises 61 CHAPTER 3 Multiple Regression Analysis: Estimation 64 3.1 Motivation for Multiple Regression 65 The Model with Two Independent Variables 65 The Model with к Independent Variables 67 3.2 Mechanics and Interpretation of Ordinary Least Squares 68 Obtaining the OLS Estimates 68 Interpreting the OLS Regression Equation 70 On the Meaning of Holding Other Factors , Fixed in Multiple Regression 72 Changing More Than One Independent Variable Simultaneously 73 Contents OLS Fitted Values and Residuais 73 A Partialling Out Interpretation of Multiple Regression 74 Comparison of Simple and Multiple Regression Estimates 74 Goodness-of-Fit 76 Regression through the Origin 77 3.3 The Expected Value of the OLS Estimators 79 Including Irrelevant Variables in a Regression Model 84 Omitted Variable Bias: The Simple Case 84 Omitted Variable Bias: More General Cases 87 3.4 The Variance of the OLS Estimators 89 The Components of the OLS Variances: Multicollinearity 90 Variances in Misspecified Models 94 Estimating σ2: Standard Errors of the OLS Estimators 95 3.5 Efficiency of OLS: The Gauss-Markov Theorem 97 3.6 Some Comments on the Language of Multiple Regression Analysis 99 Summary 100 Key Terms 101 Problems 102 Computer Exercises 106 CHAPTER 4 Multiple Regression Analysis: Inference 110 4.1 Sampling Distributions of the OLS Estimators 110 4.2 Testing Hypotheses about a Single Population Parameter: The t Test 113 Testing against One-Sided Alternatives 115 Two-Sided Alternatives 120 Testing Other Hypotheses about ßj 122 Computing p-Values for t Tests 125 A Reminder on the Language of Classical Hypothesis Testing 127 Economic, or Practical, versus Statistical Significance 127 4.3 Confidence Intervals 130 4.4 Testing Hypotheses about a Single Linear Combination of the Parameters 132 4.5 Testing Multiple Linear Restrictions: The F Test 135 Testing Exclusion Restrictions 135 Relationship between F and t Statistics 141 The R-Squared Form of the F Statistic 142 Computing p-Values for F Tests 143 The F Statistic for Overall Significance of a Regression 144 Testing General Linear Restrictions 145 4.6 Reporting Regression Results 146 Summary 149 Key Terms 151 Problems 151 Computer Exercises 156 CHAPTER 5 Multiple Regression Analysis: OLS Asymptotics 160 5.1 Consistency 161 Deriving the Inconsistency in OLS 164 5.2 Asymptotic Normality and Large Sample Inference 165 Other Large Sample Tests: The Lagrange Multiplier Statistic 170 5.3 Asymptotic Efficiency of OLS 1 73 Summary 174 Key Terms 175 Problems 175 Computer Exercises 175 CHAPTER 6 Multiple Regression Analysis: Further Issues 178 6.1 Effects of Data Scaling on OLS Statistics 178 Beta Coefficients 181 6.2 More on Functional Form 183 More on Using Logarithmic Functional Forms 183 Models with Quadratics 186 Models with Interaction Terms 190 6.3 More on Goodness-of-Fit and Selection of Regressors 192 Adjusted R-Squared 194 Using Adjusted R-Squared to Choose between Nonnested Models 195 Contents Controlling for Too Many Factors in Regression Analysis 197 Adding Regressers to Reduce the Error Variance 198 6.4 Prediction and Residual Analysis 199 Confidence Intervals for Predictions 199 Residual Analysis 203 Predicting y When log(y) Is the Dependent Variable 204 Summary 208 Key Terms 209 Problems 210 Computer Exercises 212 CHAPTER 7 Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables 217 7.1 Describing Qualitative Information 217 7.2 A Single Dummy Independent Variable 218 Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y) 223 7.3 Using Dummy Variables for Multiple Categories 225 Incorporating Ordinal Information by Using Dummy Variables 227 7.4 Interactions Involving Dummy Variables 230 Interactions among Dummy Variables 230 Allowing for Different Slopes 231 Testing for Differences in Regression Functions across Groups 235 7.5 A Binary Dependent Variable: The Linear Probability Model 238 7.6 More on Policy Analysis and Program Evaluation 243 7.7 Interpreting Regression Results with Discrete Dependent Variables 246 Summary 247 Key Terms 248 Problems 248 Computer Exercises 251 CHAPTER 8 Heteroskedasticity 258 8.1 Consequences of Heteroskedasticity for OLS 258 8.2 Heteroskedasticity-Robust Inference after OLS Estimation 259 Computing Heteroskedasticity-Robust LM Tests 264 8.3 Testing for Heteroskedasticity 265 The White Test for Heteroskedasticity 269 8.4 Weighted Least Squares Estimation 270 The Heteroskedasticity Is Known up to a Multiplicative Constant 271 The Heteroskedasticity Function Must Be Estimated: Feasible GLS 276 What If the Assumed Heteroskedasticity Function Is Wrong? 280 Prediction and Prediction Intervals with Heteroskedasticity 282 8.5 The Linear Probability Model Revisited 284 Summary 286 Key Terms 287 Problems 287 Computer Exercises 289 CHAPTER 9 More on Specification and Data Issues 293 9.1 Functional Form Misspecification 294 RESET as a General Test for Functional Form Misspecification 296 Tests against Nonnested Alternatives 297 9.2 Using Proxy Variables for Unobserved Explanatory Variables 298 Using Lagged Dependent Variables as Proxy Variables 303 A Different Slant on Multiple Regression 304 9.3 Models with Random Slopes 305 9.4 Properties of OLS under Measurement Error 307 Measurement Error in the Dependent Variable 308 Measurement Error in an Explanatory Variable 310 9.5 Missing Data, Nonrandom Samples, and Outlying Observations 314 Contents Missing Data 314 Nonrandom Samples 314 Outliers and Influential Observations 316 9.6 Least Absolute Deviations Estimation 321 Summary 324 Key Terms 325 Problems 325 Computer Exercises 327 PART 2 Regression Analysis with Time Series Data 331 CHAPTER 10 Basic Regression Analysis with Time Series Data 332 10.1 The Nature of Time Series Data 332 10.2 Examples of Time Series Regression Models 333 Static Models 334 Finite Distributed Lag Models 334 A Convention about the Time Index 337 10.3 Finite Sample Properties of OLS under Classical Assumptions 337 Unbiasedness of OLS 337 The Variances of the OLS Estimators and the Gauss-Markov Theorem 340 Inference under the Classical Linear Model Assumptions 343 10.4 Functional Form, Dummy Variables, and Index Numbers 344 10.5 Trends and Seasonality 351 Characterizing Trending Time Series 351 Using Trending Variables in Regression Analysis 354 A Detrending Interpretation of Regressions with a Time Trend 356 Computing R-Squared when the Dependent Variable Is Trending 358 Seasonality 359 Summary 361 Key Terms 362 Problems 363 Computer Exercises 364 CHAPTER 11 Further Issues in Using OLS with Time Series Data 368 11.1 Stationary and Weakly Dependent Time Series 369 Stationary and Nonstationary Time Series 369 Weakly Dependent Time Series 370 11.2 Asymptotic Properties of OLS 372 11.3 Using Highly Persistent Time Series in Regression Analysis 379 Highly Persistent Time Series 379 Transformations on Highly Persistent Time Series 383 Deciding Whether a Time Series Is 1(1) 384 11.4 Dynamically Complete Models and the Absence of Serial Correlation 387 11.5 The Homoskedasticity Assumption for Time Series Models 390 Summary 390 Key Terms 392 Problems 392 Computer Exercises 394 CHAPTER 12 Serial Correlation and Heteroskedasticity in Time Series Regressions 398 12.1 Properties of OLS with Serially Correlated Errors 398 Unbiasedness and Consistency 398 Efficiency and Inference 399 Goodness-of-Fit 400 Serial Correlation in the Presence of Lagged Dependent Variables 401 12.2 Testing for Serial Correlation 402 A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressers 402 The Durbin-Watson Test under Classical Assumptions 404 Testing for AR( 1 ) Serial Correlation without Strictly Exogenous Regressers 406 Testing for Higher Order Serial Correlation 407 12.3 Correcting for Serial Correlation with Strictly Exogenous Regressors 409 Obtaining the Best Linear Unbiased Estimator in the AR(1) Model 409 Contents Feasible GLS Estimation with AR(1) Errors 411 Comparing OLS and FGLS 413 Correcting for Higher Order Serial Correlation 414 12.4 Differencing and Serial Correlation 415 12.5 Serial Correlation-Robust Inference after OLS 417 12.6 Heteroskedasticity in Time Series Regressions 420 Heteroskedasticity-Robust Statistics 421 Testing for Heteroskedasticity 421 Autoregressive Conditional Heteroskedasticity 422 Heteroskedasticity and Serial Correlation in Regression Models 424 Summary 425 Key Terms 426 Problems 426 Computer Exercises 427 PART 3 Advanced Topics 431 CHAPTER 13 Pooling Cross Sections across Time: Simple Panel Data Methods 432 13.1 Pooling Independent Cross Sections across Time 433 The Chow Test for Structural Change across Time 437 13.2 Policy Analysis with Pooled Cross Sections 438 13.3 Two-Period Panel Data Analysis 443 Organizing Panel Data 449 13.4 Policy Analysis with Two-Period Panel Data 449 13.5 Differencing with More Than Two Time Periods 452 Potential Pitfalls in First Differencing Panel Data 457 Summary 458 Key Terms 458 Problems 458 Computer Exercises 460 CHAPTER 14 Advanced Panel Data Methods 466 14.1 Fixed Effects Estimation 466 The Dummy Variable Regression 470 Fixed Effects or First Differencing? 471 Fixed Effects with Unbalanced Panels 473 14.2 Random Effects Models 474 Random Effects or Fixed Effects? 477 14.3 The Correlated Random Effects Approach 479 14.4 Applying Panel Data Methods to Other Data Structures 481 Summary 483 Key Terms 484 Problems 484 Computer Exercises 485 CHAPTER 15 Instrumental Variables Estimation and Two Stage Least Squares 490 15.1 Motivation: Omitted Variables in a Simple Regression Model 491 Statistical Inference with the IV Estimator 495 Properties of IV with a Poor Instrumental Variable 499 Computing R-Squared after IV Estimation 501 15.2 IV Estimation of the Multiple Regression Model 502 15.3 Two Stage Least Squares 506 A Single Endogenous Explanatory Variable 506 Multicollinearity and 2SLS 508 Multiple Endogenous Explanatory Variables 509 Testing Multiple Hypotheses after 2SLS Estimation 510 15.4 IV Solutions to Errors-in-Variables Problems 510 15.5 Testing for Endogeneity and Testing Overidentifying Restrictions 512 Contents Testing for Endogeneity 512 Testing Overidentification Restrictions 513 15.6 2SLS with Heteroskedasticity 516 15.7 Applying 2SLS to Time Series Equations 516 15.8 Applying 2SLS to Pooled Cross Sections and Panel Data 518 Summary 520 Key Terms 521 Problems 521 Computer Exercises 524 CHAPTER 16 Simultaneous Equations Models 530 16.1 The Nature of Simultaneous Equations Models 531 16.2 Simultaneity Bias in OLS 534 16.3 Identifying and Estimating a Structural Equation 536 Identification in a Two-Equation System 536 Estimation by 2SLS 541 16.4 Systems with More Than Two Equations 543 Identification in Systems with Three or More Equations 543 Estimation 544 16.5 Simultaneous Equations Models with Time Series 544 16.6 Simultaneous Equations Models with Panel Data 548 Summary 550 Key Terms 551 Problems 551 Computer Exercises 554 CHAPTER 17 Limited Dependent Variable Models and Sample Selection Corrections 559 17.1 Logit and Probit Models for Binary Response 560 Specifying Logit and Probit Models 560 Maximum Likelihood Estimation of Logit and Probit Models 563 Testing Multiple Hypotheses 564 Interpreting the Logit and Probit Estimates 565 17.2 The Tobit Model for Corner Solution Responses 572 Interpreting the Tobit Estimates 574 Specification Issues in Tobit Models 579 17.3 The Poisson Regression Model 580 17.4 Censored and Truncated Regression Models 585 Censored Regression Models 585 Truncated Regression Models 589 17.5 Sample Selection Corrections 591 When Is OLS on the Selected Sample Consistent? 591 Incidental Truncation 593 Summary 597 Key Terms 598 Problems 598 Computer Exercises 600 CHAPTER 18 Advanced Time Series Topics 606 18.1 Infinite Distributed Lag Models 607 The Geometric (or Koyck) Distributed Lag 609 Rational Distributed Lag Models 611 18.2 Testing for Unit Roots 613 18.3 Spurious Regression 618 18.4 Cointegration and Error Correction Models 620 Cointegration 620 Error Correction Models 625 58. S Forecasting 626 Types of Regression Models Used for Forecasting 628 One-Step-Ahead Forecasting 629 Comparing One-Step-Ahead Forecasts 632 Multiple-Step-Ahead Forecasts 634 Forecasting Trending, Seasonal, and Integrated Processes 636 Contents Summary 641 Key Terms 643 Problems 643 Computer Exercises 645 CHAPTER 19 Carrying Out an Empirical Project 650 19.1 Posing a Question 650 19.2 Literature Review 652 19.3 Data Collection 653 Deciding on the Appropriate Data Set 653 Entering and Storing Your Data 654 Inspecting, Cleaning, and Summarizing Your Data 656 19.4 Econometric Analysis 657 19.5 Writing an Empirical Paper 660 Introduction 660 Conceptual (or Theoretical) Framework 661 Econometric Models and Estimation Methods 661 The Data 664 Results 664 Conclusions 665 Style Hints 666 Summary 668 Key Terms 668 Sample Empirical Projects 668 List of Journals 674 Data Sources 675 CHAPTER APPENDICES Appendix 2A 678 Appendix ЗА 680 Appendix 5A 684 Appendix 6A 685 Appendix 13A 686 Appendix 14A 689 Appendix 15A 692 Appendix 17A 694 Appendix 17B 695 APPENDIX A Basic Mathematical Tools 697 A.1 The Summation Operator and Descriptive Statistics 697 A. 2 Properties of Linear Functions 699 A.3 Proportions and Percentages 701 A. 4 Some Special Functions and Their Properties 704 Quadratic Functions 704 The Natural Logarithm 706 The Exponential Function 710 A.5 Differential Calculus 711 Summary 713 Key Terms 713 Problems 713 APPENDIX В Fundamentals of Probability 716 B.1 Random Variables and Their Probability Distributions 716 Discrete Random Variables 717 Continuous Random Variables 719 B.2 Joint Distributions, Conditional Distributions, and Independence 721 Joint Distributions and Independence 721 Conditional Distributions 723 B.3 Features of Probability Distributions 724 A Measure of Central Tendency: The Expected Value 724 Properties of Expected Values 725 Another Measure of Central Tendency: The Median 727 Measures of Variability: Variance and Standard Deviation 728 Variance 728 Standard Deviation 730 Standardizing a Random Variable 730 Skewness and Kurtosis 731 B.4 Features of Joint and Conditional Distributions 731 Contents Measures of Association: Covariance and Correlation 731 Covariance 731 Correlation Coefficient 733 Variance of Sums of Random Variables 734 Conditional Expectation 735 Properties of Conditional Expectation 736 Conditional Variance 738 B.5 The Normal and Related Distributions 739 The Normal Distribution 739 The Standard Normal Distribution 740 Additional Properties of the Normal Distribution 742 The Chi-Square Distribution 743 The t Distribution 743 The F Distribution 744 Summary 746 Key Terms 746 Problems 746 APPENDIX С Fundamentals of Mathematical Statistics 749 C.1 Populations, Parameters, and Random Sampling 749 Sampling 750 C.2 Finite Sample Properties of Estimators 750 Estimators and Estimates 751 Unbiasedness 752 The Sampling Variance of Estimators 754 Efficiency 756 C.3 Asymptotic or Large Sample Properties of Estimators 757 Consistency 757 Asymptotic Normality 760 C.4 General Approaches to Parameter Estimation 762 Method of Moments 762 Maximum Likelihood 763 Least Squares 764 C.5 Interval Estimation and Confidence Intervals 764 The Nature of Interval Estimation 764 Confidence Intervals for the Mean from a Normally Distributed Population 766 A Simple Rule of Thumb for a 95 fo Confidence Interval 769 Asymptotic Confidence Intervals for Nonnormal Populations 770 C.6 Hypothesis Testing 771 Fundamentals of Hypothesis Testing 772 Testing Hypotheses about the Mean in a Normal Population 774 Asymptotic Tests for Nonnormal Populations 777 Computing and Using p- Values 778 The Relationship between Confidence Intervals and Hypothesis Testing 781 Practical versus Statistical Significance 782 C.7 Remarks on Notation 783 Summary 784 Key Terms 784 Problems 785 APPENDIX D Summary of Matrix Algebra 790 D.1 Basic Definitions 790 D.2 Matrix Operations 791 Matrix Addition 791 Scalar Multiplication 792 Matrix Multiplication 792 Transpose 793 Partitioned Matrix Multiplication 794 Trace 794 Inverse 795 D.3 Linear Independence and Rank of a Matrix 795 D.4 Quadratic Forms and Positive Definite Matrices 796 D.5 Idempotent Matrices 796 D.6 Differentiation of Linear and Quadratic Forms 797 D.7 Moments and Distributions of Random Vectors 797 Contents Expected Value 797 Variance-Covariance Matrix 797 Multivariate Normal Distribution 798 Chi-Square Distribution 798 t Distribution 799 F Distribution 799 Summary 799 Key Terms 799 Problems 800 APPENDIX E The Linear Regression Model in Matrix Form 801 E.1 The Model and Ordinary Least Squares Estimation 801 E.2 Finite Sample Properties of OLS 803 E.3 Statistical Inference 807 E.4 Some Asymptotic Analysis 809 Wald Statistics for Testing Multiple Hypotheses 812 Summary 813 Key Terms 813 Problems 813 APPENDIX F Answers to Chapter Questions 815 APPENDIX G Statistical Tables 825 References 832 Glossary 838 Index 856
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isbn	9781111531041 9781111534394 1111531048 111153439X
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-024453638
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spellingShingle	Wooldridge, Jeffrey M. 1960- Introductory econometrics a modern approach Ökonometrie (DE-588)4132280-0 gnd
subject_GND	(DE-588)4132280-0 (DE-588)4123623-3
title	Introductory econometrics a modern approach
title_auth	Introductory econometrics a modern approach
title_exact_search	Introductory econometrics a modern approach
title_full	Introductory econometrics a modern approach Jeffrey M. Wooldridge
title_fullStr	Introductory econometrics a modern approach Jeffrey M. Wooldridge
title_full_unstemmed	Introductory econometrics a modern approach Jeffrey M. Wooldridge
title_short	Introductory econometrics
title_sort	introductory econometrics a modern approach
title_sub	a modern approach
topic	Ökonometrie (DE-588)4132280-0 gnd
topic_facet	Ökonometrie Lehrbuch
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024453638&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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