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on Forecasting |
| By: | Andrea Carriero (Queen Mary, University of London); George Kapetanios (Queen Mary, University of London); Massimiliano Marcellino (IEP-Bocconi University, IGIER and CEPR) |
| Abstract: | The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance with the most promising existing alternatives, namely, factor models, large scale bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two-step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank bayesian VAR of Geweke (1996). As a result, we found that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast, and for key variables such as industrial production growth, inflation, and the federal funds rate. |
| Keywords: | Bayesian VARs, Factor models, Forecasting, Reduced rank |
| JEL: | C11 C13 C33 C53 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:wp617&r=for |
| By: | John M. Maheu (University of Toronto, Canada and The Rimini Centre for Economics Analysis, Rimini, Italy.); Thomas H. McCurdy (University of Toronto, Canada) |
| Abstract: | We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different historyof data. The paper illustrates the importance of uncertainty about structural breaks and the value of modeling higher-order moments of excess returns when forecasting the return distribution and its moments. The shape of the long-run distribution and the dynamics of the higher-order moments are quite different from those generated by forecasts which cannot capture structural breaks. The empirical results strongly reject ignoring structural change in favor of our forecasts which weight historical data to accommodate uncertainty about structural breaks. We also strongly reject the common practice of using a fixed-length moving window. These differences in long-run forecasts have implications for many financial decisions, particularly for risk management and long-run investment decisions. |
| Keywords: | density forecasts, structural change, model risk, parameter uncertainty, Bayesian learning, market returns |
| JEL: | F22 J24 J61 |
| Date: | 2007–07 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:19-07&r=for |
| By: | Axel Dreher (KOF Swiss Economic Institute, ETH Zurich); Silvia Marchesi (University of Milano-Bicocca, Department of Economics); James Raymond Vreeland (Yale University, Department of Political Science, USA) |
| Abstract: | Using panel data for 157 countries over the period 1999-2005 we empirically investigate the politics involved in IMF economic forecasts. We find a systematic bias in growth and inflation forecasts. Our results indicate that countries voting in line with the US in the UN General Assembly receive lower inflation forecasts. As the US is the Fund’s major shareholder, this result supports the hypothesis that the Fund’s forecasts are not purely based on economic considerations. We further find inflation forecasts are systematically biased downwards for countries with greater IMF loans outstanding relative to GDP, indicating that the IMF engages in “defensive forecasting.” Countries with a fixed exchange rate regime also receive low inflation forecasts. Considering the detrimental effects that inflation can have under such an exchange rate regime, we consider this evidence consistent with the Fund’s desire to preserve economic stability. |
| Keywords: | IMF, Economic Forecasts, Political Influence |
| JEL: | C23 D72 F33 F34 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:07-176&r=for |
| By: | Knüppel, Malte; Tödter, Karl-Heinz |
| Abstract: | This paper discusses methods to quantify risk and uncertainty in macroeconomic forecasts. Both, parametric and non-parametric procedures are developed. The former are based on a class of asymmetrically weighted normal distributions whereas the latter employ asymmetric bootstrap simulations. Both procedures are closely related. The bootstrap is applied to the structural macroeconometric model of the Bundesbank for Germany. Forecast intervals that integrate judgement on risk and uncertainty are obtained. |
| Keywords: | Macroeconomic forecasts, stochastic forecast intervals, risk, uncertainty, asymmetrically weighted normal distribution, asymmetric bootstrap |
| JEL: | C14 C53 E37 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdp1:6341&r=for |