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on Forecasting |
| By: | Jana Eklund (Bank of England); George Kapetanios (Queen Mary, University of London) |
| Abstract: | This paper provides a review which focuses on forecasting using statistical/econometric methods designed for dealing with large data sets. |
| Keywords: | Macroeconomic forecasting, Factor models, Forecast combination, Principal components |
| JEL: | C22 C53 E37 E47 |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:wp625&r=for |
| By: | Wang, Mu-Chun |
| Abstract: | In this paper, we put DSGE forecasts in competition with factor forecasts. We focus on these two models since they represent nicely the two opposing forecasting philosophies. The DSGE model on the one hand has a strong theoretical economic background; the factor model on the other hand is mainly data-driven. We show that by incooperating large information set using factor analysis can indeed improve the short horizon predictive ability, as claimed by manyresearchers. The micro founded DSGE model can provide reasonable forecasts for inflation, especially with growing forecast horizons. To a certain extent, our results are consistent with the prevailling view that simple time series models should be used in short-horizon forecasting and structural models should be used in long-horizon forecasting. Our paper compareds both state-of-the art data-driven and theory-based modelling in a rigorous manner. |
| Keywords: | DSGE models, factor models, forecasting, forecastevaluation |
| JEL: | C2 C3 C53 E37 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdp1:7115&r=for |
| By: | Ralf Becker; Adam Clements; Andrew McClelland |
| Abstract: | Much research has investigated the differences between option implied volatilities and econometric model-based forecasts in terms of forecast accuracy and relative informational content. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing to generate forecasts. Therefore, implied volatility has the potential to reflect information that a model-based forecast could not. Specifically, this paper considers two issues relating to the informational content of the S&P 500 VIX implied volatility index. First, whether it subsumes information on how historical jump activity contributed to the price volatility, followed by whether the VIX reflects any incremental information relative to model based forecasts pertaining to future jumps. It is found that the VIX index both subsumes information relating to past jump contributions to volatility and reflects incremental information pertaining to future jump activity, relative to modelbased forecasts. This is an issue that has not been examined previously in the literature and expands our understanding of how option markets form their volatility forecasts. |
| Keywords: | Implied volatility, VIX, volatility forecasts, informational efficiency, jumps |
| JEL: | C12 C22 G00 G14 |
| Date: | 2008–03–17 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2008-99&r=for |
| By: | Kenneth B. Petersen (Laffer Associates and University of Connecticut); Vladimir Pozdnyakov (University of Connecticut) |
| Abstract: | Predicting the federal funds rate and beating the federal funds futures market: mission impossible? Not so. We employ a Markov transition process and show that this model outperforms the federal funds futures market in predicting the target federal funds rate. Thus, by using purely historical data we are able to better explain future monetary policy than a forward looking measure like the federal funds futures rate. The fact that the federal funds futures market can be beaten by a statistical model, suggests that the federal funds futures market lacks eciency. The mar- ket allocates too much weight to current Federal Reserve communication and other real-time macro events, and allocates too little weight to past monetary policy behavior. |
| Keywords: | Monetary policy, Federal funds futures market, Markov modeling |
| JEL: | E44 E47 E52 E58 G13 |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2008-07&r=for |
| By: | Alejandro Rodriguez; Esther Ruiz |
| Abstract: | Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty due to parameter estimation. Second, the Gaussianity assumption of future innovations may be inaccurate. To overcome these drawbacks, Wall and Stoffer (2002) propose to obtain prediction intervals by using a bootstrap procedure that requires the backward representation of the model. Obtaining this representation increases the complexity of the procedure and limits its implementation to models for which it exists. The bootstrap procedure proposed by Wall and Stoffer (2002) is further complicated by fact that the intervals are obtained for the prediction errors instead of for the observations. In this paper, we propose a bootstrap procedure for constructing prediction intervals in State Space models that does not need the backward representation of the model and is based on obtaining the intervals directly for the observations. Therefore, its application is much simpler, without loosing the good behavior of bootstrap prediction intervals. We study its finite sample properties and compare them with those of the standard and the Wall and Stoffer (2002) procedures for the Local Level Model. Finally, we illustrate the results by implementing the new procedure to obtain prediction intervals for future values of a real time series. |
| Keywords: | Backward representation, Kalman filter, Local Level Model, Unobserved Components |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws081104&r=for |