Generic Conditions for Forecast Dominance

Recent studies have analyzed whether one forecast method dominates another under a class of consistent scoring functions. While the existing literature focuses on empirical tests of forecast dominance, little is known about the theoretical conditions under which one forecast dominates another. To address this question, we derive a new characterization of dominance among forecasts of the mean functional. We present various scenarios under which dominance occurs. Unlike existing results, our results allow for the case that the forecasts' underlying information sets are not nested, and allow for uncalibrated forecasts that suffer, e.g., from model misspecification or parameter estimation error. We illustrate the empirical relevance of our results via data examples from finance and economics

Relative Accuracy of Judgmental and Extrapolative Methods in Forecasting Annual Earnings

This paper identifies and analyses previously published studies on annual earnings forecasts. Comparisons of forecasts produced by management, analysts, and extrapolative techniques indicated that: (1) management forecasts were superior to professional analyst forecasts (the mean absolute percentage errors were 15.9 and 17.7, respectively, based on five studies using data from 1967-1974) and (2) judgmental forecasts (both management and analysts) were superior to extrapolation forecasts on 14 of 17 comparisons from 13 studies using data from 1964- 1979 (the mean absolute percentage errors were 21.0 and 28.4 for judgment and extrapolation, respectively). These conclusions, based on recent research, differ from those reported in previous reviews, which commented on less than half of the studies identified here.Annual, financial forecasts, Judgment vs. extrapolation, Management vs. analyst Amalgamated forecasts

Weather forecasting for weather derivatives : [revised version: January 2, 2004]

We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals conditional mean dynamics, and crucially, strong conditional variance dynamics, in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time-series weather forecasting methods will likely prove useful in weather derivatives contexts

Business Survey Data: Do They Help in Forecasting the Macro Economy?

In this paper we examine whether data from business tendency surveys are useful for forecasting the macro economy in the short run. Our analyses primarily concern the growth rates of real GDP but we also evaluate forecasts of other variables such as unemployment, price and wage inflation, interest rates, and exchange-rate changes. The starting point is a so-called dynamic factor model (DFM), which is used both as a framework for dimension reduction in forecasting and as a procedure for filtering out unimportant idiosyncratic noise in the underlying survey data. In this way, it is possible to model a rather large number of noise-reduced survey variables in a parsimoniously parameterised vector autoregression (VAR). To assess the forecasting performance of the procedure, comparisons are made with VARs that either use the survey variables directly, are based on macro variables only, or use other popular summary indices of economic activity. As concerns forecasts of GDP growth, the procedure turns out to outperform the competing alternatives in most cases. For the other macro variables, the evidence is more mixed, suggesting in particular that there often is little difference between the DFM-based indicators and the popular summary indices of economic activity.Business survey data; Dynamic factor models; Macroeconomic forecasting

Empirical comparisons of credit and monetary aggregates using vector autoregressive methods

An abstract for this article is not available.Monetary policy

Uncovering predictability in the evolution of the WTI oil futures curve

Accurately forecasting the price of oil, the world's most actively traded commodity, is of great importance to both academics and practitioners. We contribute by proposing a functional time series based method to model and forecast oil futures. Our approach boasts a number of theoretical and practical advantages including effectively exploiting underlying process dynamics missed by classical discrete approaches. We evaluate the finite-sample performance against established benchmarks using a model confidence set test. A realistic out-of-sample exercise provides strong support for the adoption of our approach with it residing in the superior set of models in all considered instances.Comment: 28 pages, 4 figures, to appear in European Financial Managemen