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. |
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Econometric Models and Economic Forecasts Robert S. Pindyck,Daniel L. Rubinfeld No preview available - 1998 |
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2SLS a₂ actual ARIMA model associated assume assumption autocorrelation function autoregressive B₁ B₂ C₁ calculate Chapter coefficients confidence interval 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 P₁ 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 uncorrelated w₁ X₁ Y₁ zero στ ΣΧ