U-Midas: Midas Regressions with Unrestricted Lag Polynomials

38 Pages Posted: 1 Mar 2012

See all articles by Claudia Foroni

Claudia Foroni

Independent

Massimiliano Giuseppe Marcellino

Bocconi University - Department of Economics; Centre for Economic Policy Research (CEPR)

Christian Schumacher

Deutsche Bundesbank

Multiple version iconThere are 2 versions of this paper

Date Written: February 2012

Abstract

Mixed-data sampling (MIDAS) regressions allow to estimate dynamic equations that explain a low-frequency variable by high-frequency variables and their lags. When the difference in sampling frequencies between the regressand and the regressors is large, distributed lag functions are typically employed to model dynamics avoiding parameter proliferation. In macroeconomic applications, however, differences in sampling frequencies are often small. In such a case, it might not be necessary to employ distributed lag functions. In this paper, we discuss the pros and cons of unrestricted lag polynomials in MIDAS regressions. We derive unrestricted MIDAS regressions (U-MIDAS) from linear high-frequency models, discuss identification issues, and show that their parameters can be estimated by OLS. In Monte Carlo experiments, we compare U-MIDAS to MIDAS with functional distributed lags estimated by NLS. We show that U-MIDAS performs better than MIDAS for small differences in sampling frequencies. On the other hand, with large differing sampling frequencies, distributed lag-functions outperform unrestricted polynomials. The good performance of U-MIDAS for small differences in frequency is confirmed in an empirical application on nowcasting Euro area and US GDP using monthly indicators.

Keywords: distributed lag polynomals, Mixed data sampling, nowcasting, time aggregation

JEL Classification: C53, E37

Suggested Citation

Foroni, Claudia and Marcellino, Massimiliano and Schumacher, Christian, U-Midas: Midas Regressions with Unrestricted Lag Polynomials (February 2012). CEPR Discussion Paper No. DP8828, Available at SSRN: https://ssrn.com/abstract=2013819

Massimiliano Marcellino

Bocconi University - Department of Economics ( email )

Via Gobbi 5
Milan, 20136
Italy

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

Christian Schumacher

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany