Introduction to Econometrics

by Dougherty, Christopher

• ISBN13: 9780198776437
• ISBN10: 0198776438
• Edition: 2nd
• Format: Paperback
• Copyright: 2002-03-21
• Publisher: Oxford University Press

Summary

Introduction to Econometrics,2/e offers a step-by-step introductory guide to the core areas of econometrics. Accessible to readers with limited mathematical backgrounds, the book provides an analytical and an intuitive understanding of the classical linear regression model. This new edition has been substantially updated and revised with the inclusion of new material on specification tests, binary choice models, tobit analysis, sample selection bias, nonstationary time series, and unit root tests and cointegration.

Table of Contents

Review: Random variables and sampling theory	1	(387)
Covariance, variance, and correlation
Sample covariance	30	(4)
Some basic covariance rules	34	(1)
Alternative expression for sample covariance	35	(2)
Population covariance	37	(1)
Sample variance	38	(1)
Variance rules	39	(2)
Population variance of the sample mean	41	(1)
The correlation coefficient	41	(4)
Why covariance is not a good measure of association	45	(3)
Simple regression analysis
The simple linear model	48	(2)
Least squares regression	50	(2)
Least squares regression: two examples	52	(4)
Least squares regression with one explanatory variable	56	(3)
Two decompositions of the dependent variable	59	(1)
Interpretation of a regression equation	60	(5)
Goodness of fit: R2	65	(5)
Properties of the regression coefficients and hypothesis testing
The random components of the regression coefficients	70	(1)
A Monte Carlo experiment	71	(5)
Assumptions concerning the disturbance term	76	(3)
Unbiasedness of the regression coefficients	79	(1)
Precision of the regression coefficients	80	(7)
The Gauss-Markov theorem	87	(2)
Testing hypotheses relating to the regression coefficients	89	(13)
Confidence intervals	102	(3)
One-tailed t tests	105	(4)
The F test of goodness of fit	109	(3)
Relationship between the F test of goodness of fit and the t test on the slope coefficient in simple regression analysis	112	(2)
Multiple regression analysis
Illustration: a model with two explanatory variables	114	(2)
Derivation and interpretation of the multiple regression Coefficients	116	(6)
Properties of the multiple regression coefficients	122	(6)
Multicollinearity	128	(11)
Goodness of fit: R2	139	(10)
Transformations of variables
Basic procedure	149	(4)
Logarithmic transformations	153	(8)
The disturbance term	161	(2)
Nonlinear regression	163	(2)
Choice of function: Box-Cox tests	165	(5)
Dummy variables
Illustration of the use of a dummy variable	170	(6)
Extension to more than two categories and to multiple sets of dummy variables	176	(10)
Slope dummy variables	186	(5)
The Chow test	191	(5)
Specification of regression variables: a preliminary skirmish
Model specification	196	(1)
The effect of omitting a variable that ought to be included	197	(8)
The effect of including a variable that ought not to be included	205	(3)
Proxy variables	208	(5)
Testing a linear restriction	213	(6)
Getting the most out of your residuals	219	(1)
Heteroscedasticity
Heteroscedasticity and its implications	220	(5)
Detection of heteroscedasticity	225	(5)
What can you do about heteroscedasticity?	230	(8)
Stochastic regressors and measurement errors
Stochastic regressors	238	(2)
The consequences of measurement errors	240	(7)
Friedman's critique of the conventional consumption function	247	(5)
Instrumental variables	252	(9)
Simultaneous equations estimation
Simultaneous equations models: structural and reduced form equations	261	(2)
Simultaneous equations bias	263	(4)
Instrumental variables estimation	267	(13)
Binary choice and limited dependent models, and maximum likelihood estimation
The linear probability model	280	(3)
Logit analysis	283	(6)
Probit analysis	289	(3)
Censored regressions: tobit analysis	292	(5)
Sample selection bias	297	(5)
An introduction to maximum likelihood estimation	302	(9)
Models using time-series data
Static models	311	(4)
Dynamic models	315	(2)
The adaptive expectations model	317	(8)
The partial adjustment model	325	(4)
Prediction	329	(5)
Stability tests	334	(3)
Autocorrelation
Definition of autocorrelation	337	(1)
Detection of first-order autocorrelation: the Durbin-Watson test	338	(4)
What can you do about autocorrelation?	342	(4)
Autocorrelation with a lagged dependent variable	346	(4)
The common factor test	350	(5)
Apparent autocorrelation	355	(3)
Model specification: specific-to-general versus general-to-specific	358	(6)
Introduction to nonstationary time series
Stationarity and nonstationarity	364	(5)
Consequences of nonstationarity	369	(3)
Detection of nonstationarity	372	(8)
Cointegration	380	(4)
Fitting models with nonstationary time series	384	(3)
Conclusion	387	(1)
Appendix A: Statistical tables	388	(8)
Appendix B: Data sets	396	(2)
Bibliography	398	(3)
Author index	401	(1)
Subject index	402