Gujarati's Basic Econometrics provides an elementary but comprehensive introduction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level. Because of the way the book is organized, it may be used at a variety of levels of rigor. For example, if matrix algebra is used, theoretical exercises may be omitted. A CD of data sets is provided with the text.
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SingleEquation Regression Models
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adaptive expectations Appendix assume assumption autocorrelation autoregressive average Chapter chi-square collinearity computed confidence interval consider the following constant consumption expenditure data given demand function dependent variable discussed disturbance term dollars dummy variable Durbin-Watson Econometrics economic equation error term example exercise expected explanatory variables F test following model following regression given in Table heteroscedasticity homoscedastic income increases intercept term lagged least-squares level of significance linear regression linear regression model logit matrix mean value measure method multicollinearity normally distributed Note null hypothesis obtain the following OLS estimators output parameters percent period preceding probability probit problem procedure R2 value random ratio reduced-form regressand regression analysis regression coefficients regression line regression model regressors relationship residuals sample Section serial correlation series data shows slope coefficient specification errors squares standard errors statistically significant Suppose tion true two-variable unit variance zero