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on Forecasting |
| By: | Adam Elbourne; Henk Kranendonk; Rob Luginbuhl; Bert Smid; Martin Vromans |
| Abstract: | We compare the accuracy of our published GDP growth forecasts from our large macro model, SAFFIER, to those produced by VAR based models using both classical and Bayesian estimation techniques. We employ a data driven methodology for selecting variables to include in our VAR models and we find that a randomly selected classical VAR model performs worse in most cases than the Bayesian equivalent, which performs worse than our published forecasts in most cases. However, when we pool forecasts across many VARs we can produce more accurate forecasts than we published. A review of the literature suggests that forecast accuracy is likely irrelevant for the non-forecasting activities the model is used for at CPB because they are fundamentally different activities. |
| Keywords: | SEMs; VAR models; Forecast combination; Bayesian methods; Real time |
| JEL: | C52 C53 E37 |
| Date: | 2008–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpb:docmnt:172&r=for |
| By: | Clements, Michael P. (Department of Economics,University of Warwick) |
| Abstract: | We consider the possibility that respondents to the Survey of Professional Forecasters round their probability forecasts of the event that real output will decline in the future. We make various assumptions about how forecasters round their forecasts, including that individuals have constant patterns of responses across forecasts. Our primary interests are the impact of rounding on assessments of the internal consistency of the probability forecasts of a decline in real output and the histograms for annual real output growth, and on the relationship between the probability forecasts and the point forecasts of quarterly output growth. |
| Keywords: | Rounding ; probability forecasts ; probability distributions |
| JEL: | C53 E32 E37 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:warwec:869&r=for |
| By: | Debby Lanser; Henk Kranendonk |
| Abstract: | Uncertainty is an inherent attribute of any forecast. In this paper, we investigate four sources of uncertainty with CPB’s macroeconomic model SAFFIER: provisional data, exogenous variables, model parameters and residuals of behavioural equations. We apply a Monte Carlo simulation technique to calculate standard errors for the short-term and medium-term horizon for GDP and eight other macroeconomic variables. The results demonstrate that the main contribution to the total variance of a medium-term forecast, emanates from the uncertainty in the exogenous variables. For the short-term forecast both exogenous variables and provisional data are most relevant. |
| Keywords: | Monte Carlo simulation; Macro economic forecasting; Model uncertainty |
| JEL: | C15 C53 E20 E27 |
| Date: | 2008–09 |
| URL: | http://d.repec.org/n?u=RePEc:cpb:discus:112&r=for |
| By: | Clements, Michael P. (Department of Economics,University of Warwick) |
| Abstract: | A comparison of the point forecasts and the central tendencies of probability distributions of inflation and output growth of the SPF indicates that the point forecasts are sometimes optimistic relative to the probability distributions. We consider and evaluate a number of possible explanations for this finding, including the degree of uncertainty concerning the future, computational costs, delayed updating, and asymmetric loss. We also consider the relative accuracy of the two sets of forecasts. |
| Keywords: | Rationality ; point forecasts ; probability distributions |
| JEL: | C53 E32 E37 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:warwec:870&r=for |
| By: | Monica Billio; Roberto Casarin |
| Abstract: | We apply sequential Monte Carlo (SMC) to the detection of turning points in the business cycle and to the evaluation of useful statistics employed in business cycle analysis. The proposed nonlinear filtering method is very useful for sequentially estimating the latent variables and the parameters of nonlinear and non-Gaussian time-series models, such as the Markov-switching (MS) models studied in this work. We show how to combine SMC with Monte Carlo Markov Chain for estimating time series models with MS latent factors. We illustrate the effectiveness of the methodology and measure, in a full Bayesian and realtime context, the ability of a pool of MS models to identify turning points in the European economic activity. We also compare our results with the business cycle datation existing in the literature and provide a sequential evaluation of the forecast accuracy of the competing MS models. |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:ubs:wpaper:0815&r=for |