This paper introduces a new regression model-Markov-switching mixed data sampling (MS-MIDAS)-that incorporates regime changes in the parameters of the mixed data sampling (MIDAS) models and allows for the use of mixed-frequency data in Markov-switching models. After a discussion of estimation and inference for MS-MIDAS, and a small sample simulation based evaluation, the MS-MIDAS model is applied to the prediction of the US and UK economic activity, in terms both of quantitative forecasts of the aggregate economic activity and of the prediction of the business cycle regimes. Both simulation and empirical results indicate that MS-MIDAS is a very useful specification.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AcknowledgementsWe would like to thank Robert Hodrick and an anonymous referee for helpful comments on a previous version of this paper. The first author acknowledges support of a Marie Curie FP7-PEOPLE-2010-IIF grant.iii AbstractThis paper deals with the estimation of the risk-return trade-off. We use a MIDAS model for the conditional variance and allow for possible switches in the risk-return relation through a Markov-switching specification. We find strong evidence for regime changes in the risk-return relation. This finding is robust to a large range of specifications. In the first regime characterized by low ex-post returns and high volatility, the risk-return relation is reversed, whereas the intuitive positive risk-return trade-off holds in the second regime. The first regime is interpreted as a "flight-to-quality" regime. JEL classification: G10, G12 Bank classification: Economic and statistical models; Financial markets RésuméNotre étude porte sur l'estimation de la relation entre le risque et le rendement. À cette fin, nous estimons cette relation avec un modèle à changements de régimes markoviens en utilisant un modèle MIDAS pour la variance conditionnelle. Les résultats obtenus à partir de nombreuses spécifications militent fortement en faveur de changements de régimes dans la relation entre le risque et le rendement. Dans le premier régime, caractérisé par de faibles rendements ex post et une forte volatilité, la relation entre le risque et le rendement est négative; à l'inverse, la relation de ces deux éléments est positive dans le second régime comme le prévoit le modèle théorique. Le premier régime constitue selon nous un mouvement de report vers la qualité.

We analyze how to incorporate low frequency information in models for predicting high frequency variables. In doing so, we introduce a new model, the reverse unrestricted MIDAS (RU-MIDAS), which has a periodic structure but can be estimated by simple least squares methods and used to produce forecasts of high frequency variables that also incorporate low frequency information. We compare this model with two versions of the mixed frequency VAR, which so far had been only applied to study the reverse problem, that is, using the high frequency information for predicting low frequency variables. We then implement a simulation study to evaluate the relative forecasting ability of the alternative models in finite samples. Finally, we conduct several empirical applications to assess the relevance of quarterly survey data for forecasting a set of monthly macroeconomic indicators. Overall, it turns out that low frequency information is important, particularly so when it is just released.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AcknowledgementsWe thank Frank Schorfheide for providing access to his MF-VAR code, Cynthia Wu for explaining the conventions used in constructing the weekly data used in Hamilton and Wu (2013), and Dmitri Tchebotarev for excellent research assistance.iii AbstractThe substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial and energy market data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models can be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred mixed-data sampling (MIDAS) model reduces the mean-squared prediction error by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 80 percent. This MIDAS forecast also is more accurate than a mixed-frequency real-time vector autoregressive forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil. JEL classification: C53, G14, Q43 Bank classification: Econometric and statistical methods; International topics RésuméLa variation considérable des prix réels du pétrole depuis 2003 a ravivé l'intérêt porté aux méthodes de prévision des cours mensuels et trimestriels de ce produit. On a aussi observé un regain d'intérêt pour l'étude du lien entre les marchés financiers et pétroliers : à ce titre, les chercheurs se sont demandé si l'information en provenance des marchés financiers aide à prédire les prix réels du pétrole sur les marchés au comptant. Les données des marchés financiers et énergétiques présentent un avantage évident pour prévoir les cours du pétrole : elles sont accessibles en temps réel selon une fréquence quotidienne ou hebdomadaire. Nous cherchons donc à déterminer le pouvoir de prévision de ce...

Summary This paper evaluates the effects of high‐frequency uncertainty shocks on a set of low‐frequency macroeconomic variables representative of the US economy. Rather than estimating models at the same common low frequency, we use recently developed econometric models, which allow us to deal with data of different sampling frequencies. We find that credit and labor market variables react the most to uncertainty shocks in that they exhibit a prolonged negative response to such shocks. When looking at detailed investment subcategories, our estimates suggest that the most irreversible investment projects are the most affected by uncertainty shocks. We also find that the responses of macroeconomic variables to uncertainty shocks are relatively similar across single‐frequency and mixed‐frequency data models, suggesting that the temporal aggregation bias is not acute in this context.