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on Forecasting |
| By: | Ippei Fujiwara; Hibiki Ichiue; Yoshiyuki Nakazono; Yosuke Shigemi |
| Abstract: | We test whether professional forecasters forecast rationally or behaviorally using a unique database, QSS Database, which is the monthly panel of forecasts on Japanese stock prices and bond yields. The estimation results show that (i) professional forecasts are behavioral, namely, significantly influenced by past forecasts, (ii) there exists a stock-bond dissonance: while forecasting behavior in the stock market seems to be herding, that in the bond market seems to be bold in the sense that their current forecasts tend to be negatively related to past forecasts, and (iii) the dissonance is due, at least partially, to the individual forecasters' behavior that is influenced by their own past forecasts rather than others. Even in the same country, forecasting behavior is quite different by market. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:106&r=for |
| By: | Travis J. Berge |
| Abstract: | Catalyzed by the work of Meese and Rogoff (1983), a large literature has documented the inability of empirical models to accurately forecast exchange rates out-of-sample. This paper extends the literature by introducing an empirical strategy that endogenously builds forecast models from a broad set of conventional exchange rate signals. The method is extremely flexible, allowing for potentially nonlinear models for each currency and forecast horizon that evolve over time. Analysis of the models selected by the procedure sheds light on the erratic behavior of exchange rates and their apparent disconnect from macroeconomic fundamentals. In terms of forecast ability, the Meese-Rogoff result remains intact. At short horizons, the method cannot outperform a random walk, although at longer horizons the method does outperform the random walk null. These findings are found consistently across currencies and forecast evaluation methods. |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:rwp11-12&r=for |
| By: | Andrea Bastianin (Department of Statistics, University of Milan-Bicocca and Fondazione Eni Enrico Mattei); Matteo Manera (Department of Statistics, University of Milan-Bicocca and Fondazione Eni Enrico Mattei); Anil Markandya (BC3 Basque Centre for Climate Change); Elisa Scarpa (Edison Trading) |
| Abstract: | The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings suggest that, irrespective of the shape of the loss function, the class of financial models is to be preferred to time series models. Both financial and time series models are better than mixed and structural models. Results of the Diebold and Mariano test are not conclusive, for the loss differential seems to be statistically insignificant in the large majority of cases. Although the random walk model is not statistically outperformed by any of the alternative models, the empirical findings seem to suggest that theoretically well-grounded financial models are valid instruments for producing accurate forecasts of the WTI spot price. |
| Keywords: | Oil Price, WTI Spot and Futures Prices, Forecasting, Econometric Models |
| JEL: | C52 C53 Q32 Q43 |
| Date: | 2011–12 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2011.91&r=for |
| By: | James Mitchell; Richard J. Smith; Martin R. Weale |
| Abstract: | Qualitative business survey data are used widely to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report “up” and “down”. This paper examines disaggregate or firm-level survey responses. It considers how the responses of the individual firms should be quantified and combined if the aim is to produce an early indication of official output data. Having linked firms’ categorical responses to official data using ordered discrete choice models, the paper proposes a statistically efficient means of combining the disparate estimates of aggregate output growth which can be constructed from the responses of individual firms. An application to firm-level survey data from the Confederation of British Industry shows that the proposed indicator can provide early estimates of output growth more accurately than traditional indicators. |
| Keywords: | Survey Data; Indicators; Quantification; Forecasting; Forecast Combination |
| JEL: | C35 C53 C80 |
| Date: | 2011–12 |
| URL: | http://d.repec.org/n?u=RePEc:lec:leecon:11/53&r=for |
| By: | Travis J. Berge; Òscar Jordà |
| Abstract: | This paper codifies in a systematic and transparent way a historical chronology of business cycle turning points for Spain reaching back to 1850 at annual frequency, and 1939 at monthly frequency. Such an exercise would be incomplete without assessing the new chronology itself and against others —this we do with modern statistical tools of signal detection theory. We also use these tools to determine which of several existing economic activity indexes provide a better signal on the underlying state of the economy. We conclude by evaluating candidate leading indicators and hence construct recession probability forecasts up to 12 months in the future. |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:rwp11-14&r=for |
| By: | Rogers, Todd (Harvard University and Analyst Institute, Washington, DC); Aida, Masa (Greenberg Quinlan Rosner Research, Washington, DC) |
| Abstract: | How accurate are people when predicting whether they will vote? These self-predictions are used by political scientists to proxy for political motivation, and by public opinion researcher to predict election outcomes. Phone surveys from three elections, including one survey experiment, are analyzed to compare respondents' pre-election vote intention with their actual voting behavior using administrative records (N=29,403). Unsurprisingly, many who predict that they will vote actually do not vote. More surprisingly, many who predict that they will not vote actually do vote (29% to 56%). Records of past voting behavior predicts turnout substantially better than self-prediction. Self-prediction inaccuracy is not caused by lack of cognitive salience of past voting, or by inability to recall past voting. Moreover, self-reported recall of turnout in one past election predicts future turnout just as well as self-prediction. We discuss implications for political science research, behavioral prediction, election administration policy, and public opinion. |
| Date: | 2012–01 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:harjfk:rwp12-001&r=for |
| By: | Feinberg, Yossi (Stanford University); Lambert, Nicolas S. (Stanford University) |
| Abstract: | Prequential testing of a forecaster is known to be manipulable if the test must pass an informed forecaster for all possible true distributions. Stewart (2011) provides a non-manipulable prequential likelihood test that only fails an informed forecaster on a small, category I, set of distributions. We present a prequential test based on calibration that also fails the informed forecaster on at most a category I set of true distributions and is non-manipulable. Our construction sheds light on the relationship between likelihood and calibration with respect to the distributions they reject. |
| Date: | 2011–12 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:stabus:2090&r=for |
| By: | Reitz, Stefan; Rülke, Jan-Christoph; Stadtmann, Georg |
| Abstract: | Chartist and fundamentalist models have proven to be capable of replicating stylized facts on speculative markets. In general, this is achieved by specifying nonlinear interactions of otherwise linear asset price expectations of the respective trader groups. This paper investigates whether or not regressive and extrapolative expectations themselves exhibit significant nonlinear dynamics. The empirical results are based on a new data set from the European Central Bank Survey of Professional Forecasters on oil price expectations. In particular, we find that forecasters form destabilizing expectations in the neighborhood of the fundamental value, whereas expectations tend to be stabilizing in the presence of substantial oil price misalignment. -- |
| Keywords: | agent based models,nonlinear expectations,survey data |
| JEL: | F31 D84 C33 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:euvwdp:311&r=for |