Econometric Models and Economic ForecastsActing as a first course in Econometrics in Economics Departments, this work also includes Economic/Business Forecasting. It is at a slightly higher level and more comprehensive than Gujarati (M-H, 1996). It has statistics as a prerequisite, but not calculus. P-R covers more time series and forecasting. P-R coverage requires no matrix algebra. |
Contents
Introduction to the Regression Model | 1 |
A Review | 19 |
3 | 57 |
Copyright | |
18 other sections not shown
Other editions - View all
Econometric Models and Economic Forecasts Robert S. Pindyck,Daniel L. Rubinfeld No preview available - 1998 |
Common terms and phrases
2SLS a₂ actual ARIMA model associated assume assumption autocorrelation function autoregressive B₁ B₂ C₁ calculate Chapter coefficients confidence intervals consider consistent estimator consumption covariance degrees of freedom demand dependent variable econometric endogenous variables equal error term error variance example exogenous explanatory variables F distribution FIGURE follows forecast error heteroscedasticity income independent intercept interest rate least-squares estimation linear regression matrix mean measure moving average nonlinear nonstationary normally distributed null hypothesis observations obtain ordinary least squares parameter estimates percent level period predict procedure random variable random walk reduced form regression equation regression model reject the null residuals sample autocorrelation function serial correlation significant single-equation slope specification standard deviation standard error stationary statistic stochastic sum of squares time-series model unbiased uncorrelated w₁ X₁ Y₁ ŶT+1 zero ΣΧ