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on Forecasting |
| By: | Antoine Mandel (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics); Amir Sani (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics) |
| Abstract: | Non-parametric forecast combination methods choose a set of static weights to combine over candidate forecasts as opposed to traditional forecasting approaches, such as ordinary least squares, that combine over information (e.g. exogenous variables). While they are robust to noise, structural breaks, inconsistent predictors and changing dynamics in the target variable, sophisticated combination methods fail to outperform the simple mean. Time-varying weights have been suggested as a way forward. Here we address the challenge to “develop methods better geared to the intermittent and evolving nature of predictive relations” in Stock and Watson (2001) and propose a data driven machine learning approach to learn time-varying forecast combinations for output, inflation or any macroeconomic time series of interest. Further, the proposed procedure “hedges” combination weights against poor performance to the mean, while optimizing weights to minimize the performance gap to the best candidate forecast in hindsight. Theoretical results are reported along with empirical performance on a standard macroeconomic dataset for predicting output and inflation. |
| Abstract: | Les méthodes non-paramétriques de combinaison de prédicteurs déterminent un vecteur statique de poids pour combiner les prédicteurs. Elles différent ainsi des méthodes de prévision traditionnelles qui visent à combiner l'information (i.e. les variables exogènes). Bien qu'elles soient très robustes, notamment au bruit, aux changements structurels ou à la présence de prédicteurs inconsistants, les méthodes de combinaison complexes n'offrent généralement pas une performance supérieure à celle de la simple moyenne. L'usage de poids variables dans le temps est considéré comme une nouvelle voie de recherche prometteuse face à ce dilemme. Dans cet article, nous développons cette approche en proposant une approche par l'apprentissage statistique du problème de la détermination de combinaisons de prédicteurs évoluant dans le temps pour l'inflation, le PIB ou tout autre série macro-économique. La méthode proposée permet en particulier de garantir, au pire, une performance proche de celle de la moyenne tout en optimisant les poids de telle sorte que la performance soit proche de celle de la meilleure combinaison à posteriori. Nous reportons à cet effet des résultats théoriques et empiriques sur un ensemble de données standard pour la prédiction macro-économique. |
| Keywords: | Forecast combinations,Machine Learning,Econometrics,Forecasting,Forecast Combination Puzzle,Apprentissage statistique,Combinaison de prédicteurs,Econométrie |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-01317974&r=for |
| By: | Hyeongwoo Kim; Wen Shi; Hyun Hak Kim |
| Abstract: | We propose factor-based out-of-sample forecast models for Korea's financial stress index and its 4 sub-indices that are developed by the Bank of Korea. We extract latent common factors by employing the method of the principal components for a panel of 198 monthly frequency macroeconomic data after differencing them. We augment an autoregressive-type model of the financial stress index with estimated common factors to formulate out-of-sample forecasts of the index. Our models overall outperform both the stationary and the nonstationary benchmark models in forecasting the financial stress indices for up to 12-month forecast horizons. The first common factor that represents not only financial market but also real activity variables seems to play a dominantly important role in predicting the vulnerability in the financial markets in Korea. |
| Keywords: | Financial Stress Index; Principal Component Analysis; PANIC; In-Sample Fit; Out-of-Sample Forecast; Diebold-Mariano-West Statistic |
| JEL: | E44 E47 G01 G17 |
| Date: | 2016–09 |
| URL: | http://d.repec.org/n?u=RePEc:abn:wpaper:auwp2016-10&r=for |
| By: | Andrew Binning (Norges Bank (Central Bank of Norway)); Junior Maih (Norges Bank (Central Bank of Norway) and BI Norwegian Business School) |
| Abstract: | The lower bound on interest rates has restricted the impact of conventional monetary policies over recent years and could continue to do so in the near future, with the decline in natural real rates not predicted to reverse any time soon. A binding lower bound on interest rates has consequences not only for point forecasts but also for the entire model forecast distribution. In this paper we investigate the ramifications of the lower bound constraint on the forecast distributions from DSGE models and the implications for risk and uncertainty. To that end we start out by making the case for regime-switching as a framework for imposing the lower bound constraint on interest rates in DSGE models. We then use the framework to investigate the implications of the lower bound constraint on the forecast distributions and try to answer the question of how much asymmetry we should expect when the lower bound binds. The results suggest that: i) a lower bound constraint need not in itself imply asymmetric fan charts, ii) the degree of asymmetry of fan charts depends on various factors such as the degree of interest rate smoothing and the degree of price rigidity, and iii) different approaches to imposing the lower bound yield different results for both the width of the fan charts and their asymmetry. |
| Keywords: | Effective Lower Bound, Regime-Switching, DSGE, Forecast Uncertainty, Fan Charts |
| Date: | 2016–09–06 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2016_13&r=for |
| By: | Beckmann, Joscha; Czudaj, Robert |
| Abstract: | This paper analyzes the role of uncertainty on both exchange rate expectations and forecast errors of professionals for four major currencies based on survey data provided by FX4casts. We consider economic policy, macroeconomic, and financial uncertainty as well as disagreement among CPI inflation forecasters to account for different dimensions of uncertainty. Based on a Bayesian VAR approach, we observe that effects on forecast errors of professionals turn out to be more significant compared to the adjustment of exchange rate expectations. Our findings are robust to different forecasting horizons and point to an unpredictable link between exchange rates and fundamentals. Furthermore, we illustrate the importance of considering common unpredictable components for a large number of variables. We also focus on the post-crisis period and the relationship between uncertainty and disagreement among exchange rate forecasters and identify a strong relationship between them. |
| Abstract: | Dieser Beitrag analysiert die Bedeutung von Unsicherheit für professionelle Wechselkurserwartungen und die resultierenden Prognosefehler für vier große Währungen auf Basis von Umfragedaten von FX4casts. Wir betrachten politische, makroökonomische und finanzielle Unsicherheit sowie die Inflationsunsicherheit als unterschiedliche Dimensionen von Unsicherheit. Basierend auf einem Bayesianischen VAR-Modell finden wir, dass eine Steigerung von Unsicherheit oftmals den Prognosefehler erhöht. Die Auswirkungen auf die Prognosefehler sind im Vergleich zu der Anpassung der Wechselkurserwartungen wesentlich bedeutsamer. Die Ergebnisse sind robust über unterschiedliche Prognosehorizonte und bestätigen einen nicht prognostizierbaren Zusammenhang zwischen Wechselkursen und Fundamentaldaten. Erwartungsunsicherheit ist zudem insbesondere nach der Finanzkrise stark mit den Unsicherheitsmaßen korreliert. |
| Keywords: | Bayesian VAR,exchange rates,expectations,forecast,uncertainty |
| JEL: | F31 F37 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:637&r=for |
| By: | Herman Stekler (Department of Economics, George Washington University); Yongchen Zhao (Department of Economics, Towson University) |
| Abstract: | This paper considers the issue of predicting cyclical turning points using real-time diffusion indexes constructed using a large data set from March 2005 to September 2014. We construct diffusion indexes at the monthly frequency, compare several smoothing and signal extraction methods, and evaluate predictions based on the indexes. Our finding suggest that diffusion indexes are still effective tools in predicting turning points. Using diffusion indexes, along with good judgement, one would have successfully predicted or at least identified the 2008 recession in real time. |
| Keywords: | Forecasting recession, real-time data, probability forecast. |
| JEL: | C43 C53 C55 E37 |
| Date: | 2016–09 |
| URL: | http://d.repec.org/n?u=RePEc:tow:wpaper:2016-15&r=for |