A Course in EconometricsThis text prepares first-year graduate students and advanced undergraduates for empirical research in economics, and also equips them for specialization in econometric theory, business, and sociology. A Course in Econometrics is likely to be the text most thoroughly attuned to the needs of your students. Derived from the course taught by Arthur S. Goldberger at the University of Wisconsin-Madison and at Stanford University, it is specifically designed for use over two semesters, offers students the most thorough grounding in introductory statistical inference, and offers a substantial amount of interpretive material. The text brims with insights, strikes a balance between rigor and intuition, and provokes students to form their own critical opinions. A Course in Econometrics thoroughly covers the fundamentals--classical regression and simultaneous equations--and offers clear and logical explorations of asymptotic theory and nonlinear regression. To accommodate students with various levels of preparation, the text opens with a thorough review of statistical concepts and methods, then proceeds to the regression model and its variants. Bold subheadings introduce and highlight key concepts throughout each chapter. Each chapter concludes with a set of exercises specifically designed to reinforce and extend the material covered. Many of the exercises include real microdata analyses, and all are ideally suited to use as homework and test questions. |
Contents
Relations | 1 |
Univariate Probability Distributions | 11 |
Regression Algebra | 17 |
Exercises | 32 |
Exercises | 41 |
Exercises | 54 |
Normal Distributions | 68 |
Univariate Case | 80 |
Issues in Hypothesis Testing | 233 |
Exercises | 243 |
Multicollinearity | 245 |
Regression Strategies | 254 |
Regression with X Random | 264 |
Exercises | 273 |
Generalized Classical Regression | 292 |
Exercises | 299 |
Asymptotic Distribution Theory | 94 |
Exercises | 104 |
Advanced Estimation Theory | 128 |
Estimating a Population Relation | 138 |
Classical Regression | 144 |
Exercises | 158 |
Exercises | 168 |
Classical Normal Regression | 189 |
Exercises | 202 |
Exercises | 213 |
Exercises | 220 |
Heteroskedasticity and Autocorrelation | 300 |
Nonlinear Regression | 308 |
Regression Systems | 323 |
Structural Equation Models | 337 |
SimultaneousEquation Model | 349 |
Exercises | 363 |
Estimation in the SimultaneousEquation Model | 365 |
Appendix A Statistical and Data Tables | 381 |
Appendix B Getting Started in GAUSS | 391 |
397 | |
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Common terms and phrases
asymptotic distribution b₁ b₂ bivariate normal bivariate population c₁ calculate CEF's coefficient columns conditional expectation conditional expectation function confidence interval Consider consistent estimator constant converges converges in probability covariance CR model defined denote E(U² E(X² equations event example Exercises explanatory variables FGLS given income joint distribution joint pdf linear function linear regression marginal pdf mean in random mean-independent minimize ML estimator nonlinear nonnegative normal distribution null hypothesis observations pdf or pmf pdf's population mean Pr(A Pr(X Pr(Y prediction predictor probability distribution Proof random sampling random variable random vector rank(X roof distribution sample mean sample moments sample statistics sample variance Section slope standard error standard normal stochastically independent sum of squared Suppose Theorem U₁ unbiased estimator uncorrelated univariate X₁ Y₁ Y₂ Z₁ zero σ²