Introductory Econometrics with Applications
Offers an ideal combination of econometric theory and hands-on practical training for undergraduate and graduate courses. The authors ambition is to provide realistic applications without sacrificing theoretical underpinnings. He uses a logical step-by-step approach to walk readers through numerous real-world examples of model specification, estimation, and hypothesis testing. The book also succeeds at being self-contained. By including background information on mathematics, probability, statistics, and software applications, readers have all the information they need in one place.
76 pages matching period in this book
Results 1-3 of 76
What people are saying - Write a review
We haven't found any reviews in the usual places.
Review of Calculus Probability and Statistics
A The Chisquare Distribution
27 other sections not shown
Other editions - View all
Added variable Adjusted R-squared alternative Assumption autocorrelation auxiliary regression average BEDRMS Chapter chi-square distribution compute consistent constant term consumption correlogram covariance degrees of freedom dependent variable derivative double-log dummy variables econometric econometric model Economic ECSLIB elasticity equation error sum error term estimate the model example expected explanatory variables F-distribution F-statistic F-test forecasts formulation function genr hence heteroscedasticity homoscedasticity income independent Lagrange multiplier least squares level of significance linear LM test logarithm measured method model selection MODEL SELECTION STATISTICS multicollinearity nonlinear normal distribution null hypothesis number of observations obtain OLS estimates omitted p-value parameters percent level period population predicted probability Property R-squared random variable reduced form regression coefficients regression model reject the null relation residuals Section serial correlation SGMASQ SQFT standard errors Step sum of squares Table test statistic unbiased estimator unrestricted model variance Wald test WLFP zero