{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Hayashi_2000_Econometrics - Contents List of Figures...

Info icon This preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 2
Image of page 3

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 4
Image of page 5

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 6
Contents List of Figures xvii Preface xix 1 Finite-Sample Properties of OLS 1.1 The Classical Linear Regression Model The Linearity Assumption Matrix Notation The Strict Exogeneity Assumption Implications of Strict Exogeneity Strict Exogeneity in Time-Series Models Other Assumptions of the Model The Classical Regression Model for Random Samples "Fixed" Regressors 1.2 The Algebra of Least Squares OLS Minimizes the Sum of Squared Residuals Normal Equations Two Expressions for the OLS Estimator More Concepts and Algebra Influential Analysis (optional) A Note on the Computation of OLS Estimates 1.3 Finite-Sample Properties of OLS Finite-Sample Distribution of b Finite-Sample Properties of s2 Estimate of Var(b 1 X) 1.4 Hypothesis Testing under Normality Normally Distributed Error Terms Testing Hypotheses about Individual Regression Coefficients Decision Rule for the t-Test Confidence Interval
Image of page 7

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
vi Contents p-Value 3 8 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 Cobh-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 R~ 67 Testing Constant Returns to Scale 67 Importance of Plotting Residuals 68 Subsequent Developments 68 Problem Set 7 1 Answers to Selected Questions 84 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
Image of page 8
Contents vii Viewing Estimators as Sequences of Random Variables Laws of Large Numbers and Central Limit Theorems 2.2 Fundamental Concepts in Time-Series Analysis Need for Ergodic Stationarity Various Classes of Stochastic Processes Different Formulation of Lack of Serial Dependence The CLT for Ergodic Stationary Martingale Differences Sequences 2.3 Large-Sample Distribution of the OLS Estimator The Model Asymptotic Distribution of the OLS Estimator s2 IS Consistent 2.4 Hypothesis Testing Testing Linear Hypotheses The Test Is Consistent Asymptotic Power Testing Nonlinear Hypotheses 2.5 Estimating E(E?x~x;) Consistently Using Residuals for the Errors Data Matrix Representation of S Finite-Sample Considerations 2.6 Implications of Conditional Homoskedasticity Conditional versus Unconditional Homoskedasticity
Image of page 9

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 10
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern