Martin J. Bailey’s research while affiliated with University of Chicago and other places

Past work on prediction formulas for time series, e.g., that of Wiener and Yaglom, has used powerful and general spectral techniques that result in integral formulas difficult to evaluate in practical cases. In this paper the more direct, less powerful approach developed by Muth produces useful and specific results for a broad class of cases that includes virtually every case of a higher order Markov process observed with error. The results provide plausible theoretical support for the expectations model widely and successfully applied by economists, although the distributions of the estimators of the prediction parameters (when the latter are unknown) are still unknown. The two-hundred-year series of sunspot data provides an illustrative application of the theoretical results, which bears comparison with the previous analyses of these data by Yule and others.

Quality differences make estimation of price indexes for real properties difficult, but these can be largely avoided by basing an index on sales prices of the same property at different times. The problem of combining price relatives of repeat sales of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. This method of estimation is more efficient than others for combining price relatives in that it utilizes information about the price index for earlier periods contained in sales prices in later periods. Standard errors of the estimated index numbers can be readily computed using the regression method, and it permits certain effects on the value of real properties to be eliminated from the index.