IS MORE INFORMATION A GOOD THING? BIAS NONMONOTONICITY IN STOCHASTIC DIFFERENCE EQUATIONS.

It is shown that the bias of estimated parameters in autoregressive models can increase as the sample size grows. This bias is also a nonmonotonic function of the largest autoregressive root, contrary to what asymptotic approximations had indicated so far in the literature. These unusual results are due to the effect of the initial sample observations that are typically neglected in theoretical asymptotic analysis, in spite of their empirical relevance. Implications for practical economic modelling are considered, including a comparison of the likely inaccuracies of parameter estimates in alternative models based on competing macroeconomic theories. [ABSTRACT FROM AUTHOR]

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