Econometric models and economic forecasts
This well known text helps students understand the art of model building - what type of model to build, building the appropriate model, testing it statistically, and applying the model to practical problems in forecasting and analysis.
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THE BASICS OF REGRESSION ANALYSIS
The TwoVariable Regression Model
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2SLS alternative ARIMA model associated assume assumption autocorrelation function autoregressive behavior calculate Chapter coefficients confidence intervals consider consistent estimator consumption covariance critical value degrees of freedom demand dependent variable determine dynamic econometric endogenous variables equal error term error variance example exogenous explanatory variables F distribution F statistic F test FIGURE follows forecast error given heteroscedasticity income independent individual intercept interest rate least-squares estimation linear regression matrix maximum-likelihood estimation mean moving average nonlinear nonstationary normally distributed null hypothesis observations obtain ordinary least squares parameter estimates percent level period predetermined variables predict probit problem procedure random variable random walk reduced form regression equation regression model reject the null relationship residuals sample autocorrelation function serial correlation shown in Fig significant simulation model single-equation slope specification standard deviation standard error stationary statistic stochastic sum of squares techniques time-series model uncorrelated zero