In this paper, we investigate the out-of-sample forecasting ability of a genetic program to approach the dynamic evolution of the Yen/US$ and Pound Sterling/US$ exchange rates, and verify whether the method can beat the random walk model. Later on, we use the predicted values to generate a...

Recent work has found that, without the benefit of hindsight, it can prove difficult for policy-makers to pin down accurately the current position of the output gap; real-time estimates are unreliable. However, attention primarily has focused on output gap point estimates alone. But point...

Data in which each observation is a curve occur in many applied problems. This paper explores prediction in time series in which the data is generated by a curve-valued autoregression process. It develops a novel technique, the predictive factor decomposition, for estimation of the...

This paper documents that inflation forecasts of the Federal Reserve systematically under-predicted inflation before Volker's appointment as Chairman and systematically over-predicted it afterward. It also documents that, under quadratic loss, commercial forecasts have information not contained...

In this paper we analyse the effect of model uncertainty on the wealth and utility outcomes of an investment decision. We compute optimal portfolio weights for domestic and foreign assets and using these weights we construct end investment horizon wealth and utility ratios. Model uncertainty is...

We emphasize using our solutions to the problems of omitted variables, measurement errors, and unknown functional forms to improve model specification, and to estimate the mean square error of an empirical best linear unbiased predictor of an individual drawing of the dependent variable of an...

This paper introduces model uncertainty into a simple Lucas-type monetary model. Inflation depends on agents' expectations and a vector of exogenous random variables. Following (Branch and Evans 2004) agents are assumed to underparameterize their forecasting models. A Misspecification...

We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over forecasting the disaggregates and aggregating those forecasts, or using only aggregate information in forecasting the aggregate. An implication...

This paper presents a forecast tracker that can help bridge the wide gap between formal econometric forecasting methods and the common practice of judgmental forecasting. Traditionally, out-of-sample forecast errors have been widely used to improve the accuracy of econometric models, but track...

This paper proposes a new way of modeling and forecasting intraday returns. We decompose the volatility of high frequency asset returns into components that may be easily interpreted and estimated. The conditional variance is expressed as a product of daily, diurnal and stochastic intraday...