David E. Rapach and Mark E. Wohar
We thank the Simon Center for Regional Forecasting at the John Cook School of Business at Saint Louis University – especially Jack Strauss, Director of the Simon Center and Ellen…
Abstract
We thank the Simon Center for Regional Forecasting at the John Cook School of Business at Saint Louis University – especially Jack Strauss, Director of the Simon Center and Ellen Harshman, Dean of the Cook School – for its generosity and hospitality in hosting a conference during the summer of 2006 where many of the chapters appearing in this volume were presented. The conference provided a forum for discussing many important issues relating to forecasting in the presence of structural breaks and model uncertainty, and participants viewed the conference as helping to significantly improve the quality of the research appearing in the chapters of this volume.3 This volume is part of Elsevier's new series, Frontiers of Economics and Globalization, and we also thank Hamid Beladi for his support as an Editor of the series.
Jennifer L. Castle and David F. Hendry
Structural models' inflation forecasts are often inferior to those of naïve devices. This chapter theoretically and empirically assesses this for UK annual and quarterly…
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Structural models' inflation forecasts are often inferior to those of naïve devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, helping to interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.
Massimo Guidolin and Carrie Fangzhou Na
We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence…
Abstract
We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.
Richard C.K. Burdekin and Mark E. Wohar
The relative impacts of the monetised and non‐monetised deficit onoutput and inflation in the United States are assessed using annual datafor the 1923‐1982 period. With Federal…
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The relative impacts of the monetised and non‐monetised deficit on output and inflation in the United States are assessed using annual data for the 1923‐1982 period. With Federal Reserve purchases of government debt serving as a measure of monetisation, the results of Granger causality tests suggest that for the period 1923‐1960 neither deficit growth nor monetisation affected real GNP growth, nominal GNP growth or inflation. For the period 1961‐1982, monetisation is found to have fuelled inflation with no effect on real GNP. Non‐monetised deficits provided a negative short‐run impact on the rate of inflation over this latter period.
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Vivek Mande, Mark E. Wohar and Richard F. Ortman
A number of U.S. studies have documented an optimistic bias in analysts’ forecasts of earnings. This study investigates whether the optimistic bias and asymmetric behavior of…
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A number of U.S. studies have documented an optimistic bias in analysts’ forecasts of earnings. This study investigates whether the optimistic bias and asymmetric behavior of forecast errors found in most U.S. studies exists in Japan. We find that for firms reporting profits, Japanese analysts’ forecasts have much greater accuracy and exhibit a small pessimistic bias in comparison to firms reporting losses, where analysts’ forecasts exhibit extremely poor accuracy and an extremely significant optimistic bias. The lack of ability to forecast losses is due to their transitory nature and not due to earnings management. Forecast accuracy and bias are not related to firm size, but are related to the magnitude of reported lossess and profits.
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David E. Rapach, Jack K. Strauss and Mark E. Wohar
We examine the role of structural breaks in forecasting stock return volatility. We begin by testing for structural breaks in the unconditional variance of daily returns for the…
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We examine the role of structural breaks in forecasting stock return volatility. We begin by testing for structural breaks in the unconditional variance of daily returns for the S&P 500 market index and ten sectoral stock indices for 9/12/1989–1/19/2006 using an iterative cumulative sum of squares procedure. We find evidence of multiple variance breaks in almost all of the return series, indicating that structural breaks are an empirically relevant feature of return volatility. We then undertake an out-of-sample forecasting exercise to analyze how instabilities in unconditional variance affect the forecasting performance of asymmetric volatility models, focusing on procedures that employ a variety of estimation window sizes designed to accommodate potential structural breaks. The exercise demonstrates that structural breaks present important challenges to forecasting stock return volatility. We find that averaging across volatility forecasts generated by individual forecasting models estimated using different window sizes performs well in many cases and appears to offer a useful approach to forecasting stock return volatility in the presence of structural breaks.