Econometric Models and Economic Forecasts
First course in Econometrics in Economics Departments at better schools, also Economic/Business Forecasting. Statistics prerequisite but no calculus. Slightly higher level and more comprehensive than Gujarati (M-H, 1996) . P-R covers more time series and forecasting. P-R coverage is notch below Johnston-DiNardo (M-H, 97) and requires no matrix algebra. Includes data disk.
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THE BASICS OF REGRESSION ANALYSIS
The TwoVariable Regression Model
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2SLS 95 percent conﬁdence ARIMA model associated assume assumption autocorrelation function autoregressive behavior calculate Chapter coefﬁcients conﬁdence interval consistent estimator consumption covariance critical value deﬁned degrees of freedom demand dependent variable difﬁcult dynamic econometric efﬁcient endogenous variables equal error term error variance example exogenous explanatory variables F distribution FIGURE ﬁnd ﬁrst ﬁrst-order ﬁt ﬁtted follows forecast error given heteroscedasticity income independent individual 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 probit procedure random variable random walk reduced form regression equation regression model reject the null relationship residuals sample autocorrelation function serial correlation shown in Fig single-equation slope speciﬁcation standard deviation standard error stationary statistic stochastic sufﬁcient sum of squares time-series model uncorrelated zero