Buch, Englisch, 712 Seiten, Format (B × H): 188 mm x 255 mm, Gewicht: 1454 g
Buch, Englisch, 712 Seiten, Format (B × H): 188 mm x 255 mm, Gewicht: 1454 g
ISBN: 978-0-691-01018-2
Verlag: Princeton University Press
Hayashi's Econometrics promises to be the next great synthesis of modern econometrics. It introduces first year Ph.D. students to standard graduate econometrics material from a modern perspective. It covers all the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. The book is also distinctive in developing both time-series and cross-section analysis fully, giving the reader a unified framework for understanding and integrating results.Econometrics has many useful features and covers all the important topics in econometrics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models (such as probit and tobit) are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient manner. Eight of the ten chapters include a serious empirical application drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises at the end of each chapter provide students a hands-on experience applying the techniques covered in the chapter. The exposition is rigorous yet accessible to students who have a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions, so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text.For those who intend to write a thesis on applied topics, the empirical applications of the book are a good way to learn how to conduct empirical research. For the theoretically inclined, the no-compromise treatment of the basic techniques is a good preparation for more advanced theory courses.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
List of Figures xvii
Preface xix
1 Finite-Sample Properties of OLS 3
1.1 The Classical Linear Regression Model 3
The Linearity Assumption 4
Matrix Notation 6
The Strict Exogeneity Assumption 7
Implications of Strict Exogeneity 8
Strict Exogeneity in Time-Series Models 9
Other Assumptions of the Model 10
The Classical Regression Model for Random Samples 12
"Fixed" Regressors 13
1.2 The Algebra of Least Squares 15
OLS Minimizes the Sum of Squared Residuals 15
Normal Equations 16
Two Expressions for the OLS Estimator 18
More Concepts and Algebra 18
Influential Analysis (optional) 21
A Note on the Computation of OLS Estimates 23
1.3 Finite-Sample Properties of OLS 27
Finite-Sample Distribution of b 27
Finite-Sample Properties of s2 30
Estimate of Var(b X) 31
1.4 Hypothesis Testing under Normality 33
Normally Distributed Error Terms 33
Testing Hypotheses about Individual Regression Coefficients 35
Decision Rule for the t-Test 37
Confidence Interval 38
p-Value 38
Linear Hypotheses 39
The F-Test 40
A More Convenient Expression for F 42
t versus F 43
An Example of a Test Statistic Whose Distribution Depends on X 45
1.5 Relation to Maximum Likelihood 47
The Maximum Likelihood Principle 47
Conditional versus Unconditional Likelihood 47
The Log Likelihood for the Regression Model 48
ML via Concentrated Likelihood 48
Cramer-Rao Bound for the Classical Regression Model 49
The F-Test as a Likelihood Ratio Test 52
Quasi-Maximum Likelihood 53
1.6 Generalized Least Squares (GLS) 54
Consequence of Relaxing Assumption 1.4 55
Efficient Estimation with Known V 55
A Special Case: Weighted Least Squares (WLS) 58
Limiting Nature of GLS 58
1.7 Application: Returns to Scale in Electricity Supply 60
The Electricity Supply Industry 60
The Data 60
Why Do We Need Econometrics? 61
The Cobb-Douglas Technology 62
How Do We Know Things Are Cobb-Douglas? 63
Are the OLS Assumptions Satisfied? 64
Restricted Least Squares 65
Testing the Homogeneity of the Cost Function 65
Detour: A Cautionary Note on R2 67
Testing Constant Returns to Scale 67
Importance of Plotting Residuals 68
Subsequent Developments 68
Problem Set 71
Answers to Selected Questions 84
2 Large-Sample Theory 88
2.1 Review of Limit Theorems for Sequences of Random Variables 88
Various Modes of Convergence 89
Three Useful Results 92
Viewing Estimators as Sequences of Random Variables 94
Laws of Large Numbers and Central Limit Theorems 95
2.2 Fundamental Concepts in Time-Series Analysis 97
Need for Ergodic Stationarity 97
Various Classes of Stochastic Processes 98
Different Formulation of Lack of Serial Dependence 106
The CLT for Ergodic Stationary Martingale Differences Sequences 106
2.3 Large-Sample Distribution of the OLS Estimator 109
The Model 109
Asymptotic Distribution of the OLS Estimator 113
s2 Is Consistent 115
2.4 Hypothesis Testing 117
Testing Linear Hypotheses 117
The Test Is Consistent 119
Asymptotic Power 120
Testing Nonlinear Hypotheses 121
2.5 Estimating E([not displayable]) Consistently 123
Using Residuals for the Errors 123
Data Matrix Representation of S 125
Finite-Sample Considerations 125
2.6 Implications of Conditional Homoskedasticity 126
Conditional versus Unconditional Homoskedasticity 126
Reduction to Finite-Sample Formulas 127
Large-Sample Distribution of t and F Statistics 128
Variations of Asymptotic Tests under Conditional Homoskedasticity 129
2.7 Testing Conditional Homoskedasticity 131
2.8 Estimation with Parameterized Conditional Heteroskedasticity (optional) 133
The Functional Form 133
WLS with Known [alpha] 134
Regression of e2i on zi Provides a Consistent Estimate of [alpha] 135
