Markov-Switching MIDAS Models

Guérin, Pierre; Marcellino, Massimiliano

doi:10.1080/07350015.2012.727721

Cited by 89 publications

(60 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The period t average of each monthly indicator is obtained with data that are available within the quarter and forecasts for other months of the quarter (obtained typically from an AR model for the monthly indicator). MIDAS-based models, which were developed in Ghysels et al (2004) for financial applications and applied to macroeconomic forecasting by, for example, Clements and Galvao (2008) and Guerin and Marcellino (2013), relate the period t value of the quarterly variable of interest to a constrained distributed lag of monthly or weekly or even daily data on the predictors of interest. The resulting model is then estimated by non-linear least squares and used to forecast the variable of interest from constrained distributed lags of the available data.…”

Section: Introductionmentioning

confidence: 99%

Realtime Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility

Carriero

Clark

Marcellino

2015

Journal of the Royal Statistical Society Series A: Statistics in Society

Self Cite

View full text Add to dashboard Cite

SummaryThe paper develops a method for producing current quarter forecasts of gross domestic product growth with a (possibly large) range of available within‐the‐quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to gross domestic product, we consider versions of the model with both constant variances and stochastic volatility. We use Bayesian methods to estimate the model, to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of realtime gross domestic product growth in the USA from 1985 through 2011. In terms of point forecasts, our proposal improves significantly on auto‐regressive models and performs comparably with survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful.

show abstract

Section: Introductionmentioning

confidence: 99%

Realtime Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility

Carriero

Clark

Marcellino

2015

Journal of the Royal Statistical Society Series A: Statistics in Society

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility

Carriero¹,

Clark²,

Marcellino³

2012

Working Paper (Federal Reserve Bank of Cleveland)

Self Cite

View full text Add to dashboard Cite

Summary. The paper develops a method for producing current quarter forecasts of gross domestic product growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to gross domestic product, we consider versions of the model with both constant variances and stochastic volatility. We use Bayesian methods to estimate the model, to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of realtime gross domestic product growth in the USA from 1985 through 2011. In terms of point forecasts, our proposal improves significantly on auto-regressive models and performs comparably with survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful.

show abstract

“…The model is estimated by maximum likelihood via the EM algorithm, since this algorithm performs well for estimating non-linear models (see, e.g., Hamilton (1990) and Guérin and Marcellino (2013)). …”

Section: Introductionmentioning

confidence: 99%

Regime switches in the risk–return trade-off

Ghysels

Guérin

Marcellino

2014

Journal of Empirical Finance

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Markov-Switching MIDAS Models

Cited by 89 publications

References 43 publications

Realtime Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility

Realtime Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility

Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility

Regime switches in the risk–return trade-off

Contact Info

Product

Resources

About