For regression models with first-order autocorrelated disturbances, the traditional prescription of econometricians is to correct for serial correlation by using appropriate estimation techniques such as the Cochrane-Orcutt, Hildreth-Lu, or Prais-Winsten procedures. This suggestion, however, does not take into account the fact that in most economic applications the independent variables contain measurement errors. When there are errors in the variables, ordinary least squares, without any attempt to correct for serial correlation, may, in several cases, yield less unreliable and misleading results (in terms of biases and mean squared errors of the estimators). This paradoxical finding is demonstrated analytically for the asymptotic case and illustrated for finite samples by Monte Carlo experiments, for the regression model with a single stationary explanatory variable. The Monte Carlo experiments also suggest that the same conclusion holds if the independent variable follows a random walk, at least for medium-size samples.
Our primary aim is to forecast, rather than explain, presidential election results, using aggregate time series data from the post-World War II period. More particularly, we seek prediction of the presidential winner well before the election actually occurs. After comparing the performance of several naive blvariate models based on economic performance, international involvement, political experience, and presidential popularity, we go on to formulate a multivariate model. This economy-popularity regression model rather accurately forecasts the winner 6 months in advance of the election, by employing spring measures of presidential popularity and the growth rate in real GNP per capita. Furthermore, the model's performance, both ex post facto and prior to the election, compares favorably with the Gallup final preelection poll taken only a few days before the election.
Pindyck And Rubinfeld Econometric Models And Economic Forecasts Pdf Download
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