U-MIDAS: MIDAS regressions with unrestricted lag polynomials

Christian Schumacher, Massimiliano Marcellino and Claudia Foroni

No 8828, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 (search for similar items in EconPapers)
JEL-codes: C53 E37 (search for similar items in EconPapers)
Date: 2012-02
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (35)

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Working Paper: U-MIDAS: MIDAS regressions with unrestricted lag polynomials (2011)
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