Small sample properties of quarterly forecast errors

There are two basic approaches used in the comparative evaluation of forecasters: (1) Statistical tests of significance of differences in error measures, (2) Ordinal rankings of forecasters. To use the first approach of statistical tests, the forecast error data must satisfy the assumptions underlying those tests. This paper examines the validity of those assumptions by enquiring into the small sample properties of the forecast error data of quarterly forecasts of the U.S. economy.     

An integrated bayesian vector auto regression and error correction model for forecasting electricity consumption and prices

The analysis and forecasting of electricity consumption and prices has received considerable attention over the past forty years. In the 1950s and 1960s most of these forecasts and analyses were generated by simultaneous equation econometric models. Beginning in the 1970s, there was a shift in the modeling of economic variables from the structural equations approach with strong identifying restrictions towards a joint time-series model with very few restrictions. One such model is the vector auto regression (VAR) model. It was soon discovered that the unrestricted VAR models do not forecast well. The Bayesian vector auto regression (BVAR) approach as well the error correction model (ECM) and models based on the theory of co integration have been offered as alternatives to the simple VAR model. This paper argues that the BVAF., ECM, and co integration models are simply VAR models with various restrictions placed on the coefficients. Based on this notion of a restricted VAR model, a four-step procedure for specifying VAR forecasting models is presented and then applied to monthly data on US electricity consumption and prices.     

Lead-lag relationship between spot index and futures price of the nikkei stock average

This paper examines the lead-lag relationship between the spot index and futures price of the Nikkei Stock Average. Using daily data in the post-crash period we investigate the interaction between the spot and futures series through the error correction model. Two versions of error correction models are considered, depending on the postulated long-run equilibrium relationship. It is found that lagged changes in the futures price affect the short-term adjustment in the spot index, but not vice versa. Forecasting models for the spot index are also constructed using the univariate time series approach and the vector autoregressive method. For the post-sample forecast comparison the error correction models produce the best results. The vector autoregressive method performs better than the martingale model, while the univariate time series method gives the poorest forecasts.     

Forecasting an aggregate of cointegrated disaggregates

This study examines the problem of forecasting an aggregate of cointegrated disaggregates. It first establishes conditions under which forecasts of an aggregate variable obtained from a disaggregate VECM will be equal to those from an aggregate, univariate time series model, and develops a simple procedure for testing those conditions. The paper then uses Monte Carlo simulations to show, for a finite sample, that the proposed test has good size and power properties and that whether a model satisfies the aggregation conditions is closely related to out‐of‐sample forecast performance. The paper then shows that ignoring cointegration and specifying the disaggregate model as a VAR in differences can significantly affect analyses of aggregation, with the VAR‐based test for aggregation possibly leading to faulty inference and the differenced VAR forecasts potentially understating the benefits of disaggregate information. Finally, analysis of an empirical problem confirms the basic results. Copyright © 2000 John Wiley & Sons, Ltd.     

Real‐time or current vintage: does the type of data matter for forecasting and model selection?

In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real‐time monetary aggregate M3 (1977–2000) and residential mortgage credit (1975–1998). The forecasting method we use is multi‐step‐ahead non‐adaptive forecasting. Copyright © 2008 John Wiley & Sons, Ltd.