John B. Guerard
Drexel Burnham Lambert
Publications 4
#1Dimitrios D. Thomakos (FIU: Florida International University)H-Index: 11
#2John B. GuerardH-Index: 3
We examine the forecasting performance of parametric and nonparametric models based on a training-validation sample approach and the use of rolling short-term forecasts to compute root mean-squared errors,We find that the performance of these models is better than that of the naive, no-change model. The use of bivariate models (like VAR and transfer functions) provides additional root mean-squared error reductions. In many cases the nonparametric models forecast as well or better than the parame...
32 CitationsSource
The purpose of this study is to investigate the forecasting efficiency of an expert system, an automatic time series modeling system, when applied to a quarterly earnings per share series. The Bethlehem steel quarterly earnings series has a severe outlier problem and the intervention analysis which specifically models the outlier may enhance forecasting efficiency. The purpose of this study is to re-examine the intervention analysis of Bethlehem Steel's quarterly earnings per share series behavi...
20 CitationsSource
#1Alden S. Bean (Lehigh University)H-Index: 9
#2John B. Guerard (Drexel Burnham Lambert)H-Index: 3
Abstract This paper reports the results of a project in which R&D data drawn from National Science Foundation and Census Bureau studies were substituted in several financial models for R&D data drawn from the 1975–1982 Compustat tapes. The result using the Compustat data did not differ significantly from that based on the NSF/Census data in the aggregate, but significant differences were observed for certain industries and certain years. The purpose of this paper is to discuss the financial mode...
#1Robert T. Clemen (UO: University of Oregon)H-Index: 27
#2John B. Guerard (Drexel Burnham Lambert)H-Index: 3
Abstract Recent studies of macroeconomic forecasts have focused primarily on the relative performance of individual forecasts and combinations thereof. We suggest that these forecasts be evaluated in terms of the incremental information that they provide relative to a simple extrapolation forecast. Using a Bayesian approach, we measure the incremental information contained in econometric forecasts of U.S. GNP relative to a random-walk-with-drift time series forecast. The results indicate that (1...
15 CitationsSource