The results of forecasting experiments based on an error correction mechanism (ECM) model and various types of vector autoregressive (VAR) and Bayesian vector autoregressive (BVAR) models are presented. A Bayesian error correction mechanism (BECM) model is also tested. This model represents a hybrid of the BVAR and ECM models. The results from experiments using fifty industries and monthly Ohio labor market data demonstrate that the ECM model produces forecasts with much lower errors than any of the alternative VAR or BVAR models when the variables used in the model pass the statistical tests for cointegration. The findings confirm many of the beliefs expressed by Granger (1986) and Engle and Yoo (1987) based on theoretical consideration of the ECM model versus the VAR model. A result contradictory to the contentions of Engle and Yoo is that the BECM model performs well at the longer forecast horizons for both cointegrated and non-cointegrated industries.

This Monte Carlo study compares the performance of a recently proposed multiprocess mixture model and a more traditional random walk time-varying parameter model in the face of structural shifts and outliers. The mixture model performs well and the latter model performs poorly. This finding is of general interest since investigators often adopt random-walk time-varying parameter models to accommodate potential regime shifts in regression relationships. The findings suggest that the time-varying parameter estimation procedure is unlikely to find abrupt shifts, since the time-varying parameter estimates are contaminated by the outliers and regime shifts.

Elizabeth S. Cooperman, Winson B. Lee, James. P. Lesage, Geographical Integration and the Retail CD-Pricing Decisions of Large Depository Institutions, The Review of Economics and Statistics, Vol. 73, No. 3 (Aug., 1991), pp. 546-552

We present a spatial econometrics framework for estimating peer effects in capital structure. This approach exploits the heterogeneous and intransitive nature of peer networks to identify economically informative structural coefficients. In models of leverage levels, we detect significant peer-effect leverage coefficients that are on the order of 0.20, indicating a moderate but substantive level of strategic complementarity in capital structure decisions. We argue that prior estimates in the literature substantially overstate the magnitude of the underlying relation. Our evidence is robust to a wide variety of model modifications and supports the hypothesis that leverage is an important strategic choice variable.