TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables

Bonino-Gayoso, Nicolás and García-Hiernaux, Alfredo (2019): TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables.

Abstract

This paper tackles the mixed-frequency modeling problem from a new perspective. Instead of drawing upon the common distributed lag polynomial model, we use a transfer function representation to develop a new type of models, named TF-MIDAS. We derive the theoretical TF-MIDAS implied by the high-frequency VARMA family models and as a function of the aggregation scheme (flow and stock). This exact correspondence leads to potential gains in terms of nowcasting and forecasting performance against the current alternatives. A Monte Carlo simulation exercise confirms that TF-MIDAS beats UMIDAS models in terms of out-of-sample nowcasting performance for several data generating high-frequency processes.

• Item Type: MPRA Paper
• Language: English
• Keywords: Mixed-Frequency models, TF-MIDAS, U-MIDAS, Nowcasting, Forecasting
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C18 - Methodological Issues: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods

References

Bai, J., Ghysels, E., and Wright, J. H. (2013). State space models and MIDAS regressions. Econometric Reviews, 32(7):779–813.

Box, G. E. P. and Jenkins, G. M. (1976). Time series analysis: forecasting and control. Holden-Day.

Casals, J., Garc ́ıa-Hiernaux, A., Jerez, M., and Sotoca, S. (2016). State-space methods for time series analysis: theory, applications and software. Chapman & Hall.

Castle, J. and Hendry, D. (2013). Forecasting and nowcasting macroeconomic variables: A methodological overview. Economics Series Working Paper 674, University of Oxford, Department of Economics.

Clements, M. P. and Galva ̃o, A. B. (2008). Macroeconomic forecasting with mixed-frequency data: Forecasting output growth in the United States. Journal of Business & Economic Statistics, 26(4):546–554.

Clements, M. P. and Galv ̃ao, A. B. (2009). Forecasting US output growth using leading indicators: an appraisal using MIDAS models. Journal of Applied Econometrics, 24(7):1187–1206.

Duarte, C., Rodrigues, P. M. M., and Rua, A. (2017). A mixed frequency approach to the forecasting of private consumption with ATM/POS data. International Journal of Forecasting, 33(1):61–75.

Foroni, C., Marcellino, M., and Schumacher, C. (2012). U-MIDAS: MIDAS regressions with unrestricted lag polynomials. CEPR Discussion Paper 8828.

Foroni, C., Marcellino, M., and Schumacher, C. (2015). Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society Series A, 178(1):57–82.

Foroni, C. and Marcellino, M. G. (2013). A survey of econometric methods for mixed-frequency data. Norges Bank Working Paper.

Ghysels, E. (2014). Matlab toolbox for mixed sampling frequency data analysis using MIDAS regression models. Technical report.

Ghysels, E., Santa-Clara, P., and Valkanov, R. (2002). The MIDAS touch: Mixed data sampling regression models. Working paper, UNC and UCLA.

Ghysels, E., Santa-Clara, P., and Valkanov, R. (2003). Predicting volatility: Getting the most out of return data sampled at different frequencies. Anderson School of Management working paper and UNC Department of Economics working paper.

Ghysels, E., Santa-Clara, P., and Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3):509 – 548.

Ghysels, E., Santa-Clara, P., and Valkanov, R. (2006). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131(1):59–95.

Ghysels, E., Sinko, A., and Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1):53–90.

Ghysels, E. and Valkanov, R. (2006). Linear time series processes with mixed data sampling and MIDAS regression models. Mimeo.

Schumacher, C. (2014). MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the euro area. Annual conference 2014 (Hamburg): Evidence-based economic policy, Verein für Socialpolitik / German Economic Association.