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Introduction to Econometrics

by: Dougherty, Christopher

Introduction to Econometrics

by: Dougherty, Christopher

ISBN 13:
9780198776437
ISBN 10:
0198776438
Edition: 2nd
Format: Paperback
Copyright: 03/21/2002
Publisher: Oxford University Press
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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.

Review: Random variables and sampling theory

(387)

Covariance, variance, and correlation

Sample covariance

(4)

Some basic covariance rules

(1)

Alternative expression for sample covariance

(2)

Population covariance

(1)

Sample variance

(1)

Variance rules

(2)

Population variance of the sample mean

(1)

The correlation coefficient

(4)

Why covariance is not a good measure of association

(3)

Simple regression analysis

The simple linear model

(2)

Least squares regression

(2)

Least squares regression: two examples

(4)

Least squares regression with one explanatory variable

(3)

Two decompositions of the dependent variable

(1)

Interpretation of a regression equation

(5)

Goodness of fit: R2

(5)

Properties of the regression coefficients and hypothesis testing

The random components of the regression coefficients

(1)

A Monte Carlo experiment

(5)

Assumptions concerning the disturbance term

(3)

Unbiasedness of the regression coefficients

(1)

Precision of the regression coefficients

(7)

The Gauss-Markov theorem

(2)

Testing hypotheses relating to the regression coefficients

(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

Amazon no longer offers textbook rentals. We do!

Introduction to Econometrics

Introduction to Econometrics

9780198776437

0198776438

Summary

Table of Contents

Supplemental Materials

Write a Review

Amazon no longer offers textbook rentals. We do!

Introduction to Econometrics

Introduction to Econometrics

9780198776437

0198776438

Summary

Table of Contents

Supplemental Materials

Write a Review

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