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on Forecasting |
| By: | Luc Bauwens; Gary Koop; Dimitris Korobilis; Jeroen Rombouts |
| Abstract: | This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well. <P> |
| Keywords: | Forecasting, change-points, Markov switching, Bayesian inference., |
| JEL: | C11 C22 C53 |
| Date: | 2011–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2011s-13&r=for |
| By: | Edward Herbst; Frank Schorfheide |
| Abstract: | This paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. The authors construct posterior predictive checks to evaluate the calibration of conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. They find that the additional features incorporated into the Smets-Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates. |
| Keywords: | Econometric models ; Forecasting |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:11-5&r=for |
| By: | Ivan Savin; Peter Winker |
| Abstract: | Business tendency survey indicators are widely recognized as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full–specified VAR models with subset models obtained using a Genetic Algorithm enabling ’holes’ in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both |
| Keywords: | Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms. |
| Date: | 2011–01–27 |
| URL: | http://d.repec.org/n?u=RePEc:com:wpaper:046&r=for |
| By: | Greg Duffee |
| Abstract: | No-arbitrage term structure models impose cross-sectional restrictions among yields and can be used to impose dynamic restrictions on risk compensation. This paper evaluates the importance of these restrictions when using the term structure to forecast future bond yields. It concludes that no cross-sectional restrictions are helpful, because cross-sectional properties of yields are easy to infer with high precision. Dynamic restrictions are useful, but can be imposed without relying on the no-arbitrage structure. In practice, the most important dynamic restriction is that the first principal component of Treasury yields follows a random walk. A simple model built around this assumption produces out-of-sample forecasts that are more accurate than those of a variety of alternative dynamic models. |
| Date: | 2011–01 |
| URL: | http://d.repec.org/n?u=RePEc:jhu:papers:576&r=for |
| By: | Jonung, Lars (School of Economics and Management); Lindén, Staffan (Directorate-General Economic and Financial Affairs) |
| Abstract: | The standard way today to obtain measures of inflationary expectations is to use questionnaires to ask a representative group of respondents about their beliefs of the future rate of inflation during the coming 12 months. This type of data on inflationary expectations as well as on inflationary perceptions has been collected in a unified way on an EU-wide basis for several years. By now, probably the largest database on inflationary expectations has been built up in this way. We use this database to explore the forecasting horizons implicitly used by the respondents to questions about the expected rate of inflation during the coming 12 months. The analysis covers all EU member states that have relevant data. We examine the forecast errors, the mean error and the RMSEs, to study if the forecast horizon is truly 12 months as implied by the questionnaires. Our working hypothesis is that the forecast error has a U-shaped pattern, reaching its lowest value on the 12-month horizon. We also study the "backcast" error for inflationary perceptions in a similar way. Our exploratory study reveals large differences across countries. For most countries, we get the expected U-shaped outcome for the forecast errors. The horizon implicitly used by respondents when answering the questions is not related to the explicit time horizon of the questionnaire. On average respondents use the same horizon when answering both questions, e.g. when respondents use a 12-month forecast horizon answering to the question on future inflation, they use the same forward looking horizon when answering to the question on past inflation. We suggest possible explanations for the differences observed. |
| Keywords: | Inflationary expectations; inflationary perceptions; forecasting error; forecasting horizon; EU; euro |
| JEL: | C33 E31 E32 E37 E58 |
| Date: | 2011–01–26 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:lunewp:2011_005&r=for |
| By: | McAleer, M.J.; Jimenez-Martin, J-A.; Perez-Amaral, T. |
| Abstract: | A risk management strategy that is designed to be robust to the Global Financial Crisis (GFC), in the sense of selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models, was proposed in McAleer et al. (2010c). The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models. Such a risk management strategy is robust to the GFC in the sense that, while maintaining the same risk management strategy before, during and after a financial crisis, it will lead to comparatively low daily capital charges and violation penalties for the entire period. This paper presents evidence to support the claim that the median point forecast of VaR is generally GFC-robust. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria. In the empirical analysis, we choose several major indexes, namely French CAC, German DAX, US Dow Jones, UK FTSE100, Hong Kong Hang Seng, Spanish Ibex35, Japanese Nikkei, Swiss SMI and US S&P500. The GARCH, EGARCH, GJR and Riskmetrics models, as well as several other strategies, are used in the comparison. Backtesting is performed on each of these indexes using the Basel II Accord regulations for 2008-10 to examine the performance of the Median strategy in terms of the number of violations and daily capital charges, among other criteria. The Median is shown to be a profitable and safe strategy for risk management, both in calm and turbulent periods, as it provides a reasonable number of violations and daily capital charges. The Median also performs well when both total losses and the asymmetric linear tick loss function are considered |
| Keywords: | median strategy;Value-at-Risk (VaR);daily capital charges;robust forecasts;violation penalties;optimizing strategy;aggressive risk management;conservative risk management;basel II Accord,;global financial crisis (GFC);G32;G11;G17;C53;C22 |
| Date: | 2011–01–25 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureir:1765022237&r=for |
| By: | Gunnar Bårdsen, Ard den Reijer, Patrik Jonasson and Ragnar Nymoen (Department of Economics, Norwegian University of Science and Technology) |
| Abstract: | MOSES is an aggregate econometric model for Sweden, estimated on quarterly data, and intended for short-term forecasting and policy simulations. After a presentation of qualitative model properties, the econometric methodology is summarized. The model properties, within sample simulations, and examples of dynamic simulation (model forecasts) for the period 2009q2-2012q4 are presented. We address practical issues relating to operational use and maintenance of a macro model of this type. The detailed econometric equations are reported in an appendix. |
| Date: | 2011–01–28 |
| URL: | http://d.repec.org/n?u=RePEc:nst:samfok:11011&r=for |
| By: | Fredy Alejandro Gamboa Estrada |
| Abstract: | This research studies the forecasting performance of conventional and more recent exchange rate models in Colombia. The purpose is to explain which have been the main exchange rate determinants under an Inflation Targeting regime and a completely floating exchange rate scheme. Compared to similar studies, this paper includes conventional specifications and Taylor rule approaches that assume exogenous and endogenous monetary policy respectively. Based on the Johansen multivariate cointegration methodology, the results provide evidence for the existence of cointegration in all specifications except in the Sticky-Price Monetary Model and the Taylor Rule model that includes the real exchange rate. In addition, out of sample forecasting performance is analyzed in order to compare if all specifications outperform the drift less random walk model. All models outperform the random walk at one month horizon. However, the Flexible Price Monetary Model and the Uncovered Interest Parity Condition have superior predictive power for longer horizons. |
| Date: | 2011–01–23 |
| URL: | http://d.repec.org/n?u=RePEc:col:000094:007870&r=for |