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on Forecasting |
| By: | David F. Hendry; Grayham E. Mizon |
| Abstract: | We develop forecast-error taxonomies when there are unmodeled variables, forecast ‘off-line’. We establish three surprising results. Even when an open system is correctly specified in-sample with zero intercepts, despite known future values of strongly exogenous variables, changes in dynamics can induce forecast failure when they have non-zero means. The additional impact on forecast failure of incorrectly omitting such variables depends only on shifts in their means. With no such shifts, there is no reduction in forecast failure from forecasting unmodeled variables relative to omitting them in 1-step or multi-step forecasts. Simulation illustrations confirm these results. |
| Keywords: | Forecasting, Forecast-error taxonomies, Location shifts, Open models |
| JEL: | C51 C22 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:552&r=for |
| By: | Barbara Rossi; Atsushi Inoue |
| Abstract: | This paper proposes new methodologies for evaluating out-of-sample forecasting performance that are robust to the choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. We show that the tests proposed in the literature may lack power to detect predictive ability, and might be subject to data snooping across different window sizes if used repeatedly. An empirical application shows the usefulness of the methodologies for evaluating exchange rate models' forecasting ability. |
| Keywords: | Predictive Ability Testing, Forecast Evaluation, Estimation Window |
| JEL: | C22 C52 C53 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:duk:dukeec:11-04&r=for |
| By: | Cathy W. S. Chen (College of Business, Feng Chia University); Richard Gerlach (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics), Erasmus Universiteit); Bruce B. K. Hwang (Graduate Institute of Statistics and Actuarial Science, Feng Chia University); Michael McAleer (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics) Erasmus Universiteit, Tinbergen Instituut (Tinbergen Institute).) |
| Abstract: | Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We pro- pose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis aects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more eficiently than other models, across the series considered, which should be useful for financial practitioners. |
| Keywords: | Individual forecasts, mean forecasts, efficient estimation, generated regressors, replicable forecasts, non-replicable forecasts, expert intuition. |
| JEL: | C53 C22 E27 E37 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1116&r=for |
| By: | Chia-Lin Chang (Department of Applied Economics, Department of Finance, National Chung Hsing University, Taichung, Taiwan); Philip Hans Franses (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics), Erasmus Universiteit); Michael McAleer (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics) Erasmus Universiteit, Tinbergen Instituut (Tinbergen Institute).) |
| Abstract: | Macroeconomic forecasts are often based on the interaction between econometric models and experts. A forecast that is based only on an econometric model is replicable and may be unbiased, whereas a forecast that is not based only on an econometric model, but also incorporates expert intuition, is non-replicable and is typically biased. In this paper we propose a methodology to analyze the qualities of individual and means of non-replicable forecasts. One part of the methodology seeks to retrieve a replicable component from the non-replicable forecasts, and compares this component against the actual data. A second part modifies the estimation routine due to the assumption that the difference between a replicable and a non-replicable forecast involves measurement error. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the methodological approach using both individuals and mean forecasts. |
| Keywords: | Individual forecasts, mean forecasts, efficient estimation, generated regressors, replicable forecasts, non-replicable forecasts, expert intuition. |
| JEL: | C53 C22 E27 E37 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1115&r=for |
| By: | Domenico Ferraro; Ken Rogoff; Barbara Rossi |
| Abstract: | This paper investigates whether oil price shocks have a reliable and stable out-of-sample relationship with the Canadian/U.S Dollar nominal exchange rate. Despite state-of-the-art methodologies and clean data, we find paradoxically little systematic relation between oil prices and the exchange rate, especially if one takes the monthly and quarterly frequencies into account. In contrast, the very short term relationship between oil prices and exchange rates at the daily frequency is rather robust, and holds no matter whether we use contemporaneous (realized) or lagged oil price shocks in our regression. However, the short-term out-of-sample predictive ability is ephemeral, and it mostly appears after time variation in the forecasting ability of the models has been appropriately taken into account. We show that a similar results hold for other currencies and commodity price shocks. |
| JEL: | F31 F37 C22 C53 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:duk:dukeec:11-05&r=for |
| By: | J. Isaac Miller (Department of Economics, University of Missouri-Columbia) |
| Abstract: | This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of the error term both serially and with the regressors, I find that nonlinear least squares consistently estimates the minimum mean-squared forecast error parameter vector. The exact asymptotic distribution of the difference may be non-standard. I propose a novel testing strategy for nonlinear MiDaS and CoMiDaS regressions against a general but possibly infeasible linear alternative. An empirical application to nowcasting global real economic activity using monthly covariates illustrates the utility of the approach. |
| Keywords: | cointegration, mixed-frequency series, mixed data sampling |
| JEL: | C12 C13 C22 |
| Date: | 2011–06–14 |
| URL: | http://d.repec.org/n?u=RePEc:umc:wpaper:1104&r=for |