Cointegrating MiDaS Regressions and a MiDaS Test
AbstractThis paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of the error term both serially and with the regressors, I find that nonlinear least squares consistently estimates the minimum mean-squared forecast error parameter vector. The exact asymptotic distribution of the difference may be non-standard. I propose a novel testing strategy for nonlinear MiDaS and CoMiDaS regressions against a general but possibly infeasible linear alternative. An empirical application to nowcasting global real economic activity using monthly covariates illustrates the utility of the approach.
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Bibliographic InfoPaper provided by Department of Economics, University of Missouri in its series Working Papers with number 1104.
Length: 38 pgs.
Date of creation: 14 Jun 2011
Date of revision:
cointegration; mixed-frequency series; mixed data sampling;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-06-25 (All new papers)
- NEP-ECM-2011-06-25 (Econometrics)
- NEP-ETS-2011-06-25 (Econometric Time Series)
- NEP-FOR-2011-06-25 (Forecasting)
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- GÃ¶tz Thomas & Hecq Alain & Urbain Jean-Pierre, 2012.
"Forecasting Mixed Frequency Time Series with ECM-MIDAS Models,"
012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Thomas B. GÃ¶tz & Alain Hecq & Jeanâ€Pierre Urbain, 2014. "Forecasting Mixedâ€Frequency Time Series with ECMâ€MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, 04.
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