Econometric Models and Economic ForecastsSingle-equation regression models (introduccion to the regression model; elementary statistics: a review; two-variable regression model; multiple regression model; using the multiple regression model; serial correlation and heteroscedasticity; instrumental variables and model specification; forecasting with a single-equation regression model; single-equation estimation: advanced topics; models of quantitative choice); Multi-equation simulation models (simultaneous-equation estimation; introduction to simulation models; dinamic behavior of simulation models); The-series models (smoothing and extrapolation of timer series; properties of stochastic time series; linear time-series models; estimating and checking time-series models; forecasting with time-series models; applications of time-series models). |
Contents
1 The Use of Summation Operators | 1 |
3 | 22 |
The TwoVariable Regression Model | 46 |
Copyright | |
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2SLS a₂ ARIMA model associated assume assumption autocorrelation function autoregressive B₁ b₂ C₁ calculate Chapter coefficients confidence intervals consider consistent estimator consumption covariance CRUZ The University degrees of freedom dependent variable deviations dynamic econometric endogenous variables 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 regression equation regression model reject the null residuals sample autocorrelation function serial correlation shown in Fig simulation model single-equation slope specification standard error stationary statistic stochastic sum of squares time-series model tion unbiased uncorrelated w₁ Y₁ ŶT+1 zero ει ΣΧ