U-MIDAS: MIDAS regressions with unrestricted lag polynomial

Mixed data sampling (MIDAS) regressions allow us to estimate dynamic equations that explain a low 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 ofetn small. In such a case, it might not be necessary to employ distributed lag functions. We discuss the pros and cons of unrestricted lag polynomials in MIDAS regressions. We derive unrestricted-MIDAS (U-MIDAS) regressions from linear high frequency models, duscuss identification issues and show that their parameters can be estimated by ordinary least squares. In Monte Carlo experiments, we compare U-MIDAS with MIDAS fuctional distributed lags estimated by non-linear least squares. We show that U-MIDAS performs better than MIDAS for small differences in smal frequencies. However, with large differing sampling frequencies, distributed lag functions outperfom unrestricted polynomials. The good perfomarce of U-MIDAS for small differences in frequency is confirmed in empirical applications on nowcasting and short-term forecasting euro area and US gross domestic product growth by using monthly indicators.