Estimation and Inference in Predictive Regressions
AbstractThis paper proposes new point estimates for predictive regressions. Our estimates are easily obtained by the least squares and the instrumental variable methods. Our estimates, called the plug-in estimates, have nice asymptotic properties such as median unbiasedness and the approximated normality of the associated t-statistics. In addition, the plug-in estimates are shown to have good finite sample properties via Monte Carlo simulations. Using the new estimates, we investigate U.S. stock returns and find that some variables, which have not been statistically detected as useful predictors in the literature, are able to predict stock returns. Because of their nice properties, our methods complement the existing statistical tests for predictability to investigate the relations between stock returns and economic variables.
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Bibliographic InfoPaper provided by Institute of Economic Research, Hitotsubashi University in its series Global COE Hi-Stat Discussion Paper Series with number gd11-192.
Date of creation: May 2011
Date of revision:
unit root; near unit root; bias; median unbiased; stock return;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-08-09 (All new papers)
- NEP-ECM-2011-08-09 (Econometrics)
- NEP-ETS-2011-08-09 (Econometric Time Series)
- NEP-FOR-2011-08-09 (Forecasting)
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