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Cointegrating MiDaS Regressions and a MiDaS Test

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Abstract

This 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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File URL: http://economics.missouri.edu/working-papers/2011/WP1104_miller.pdf
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Bibliographic Info

Paper provided by Department of Economics, University of Missouri in its series Working Papers with number 1104.

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Length: 38 pgs.
Date of creation: 14 Jun 2011
Date of revision:
Handle: RePEc:umc:wpaper:1104

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Keywords: cointegration; mixed-frequency series; mixed data sampling;

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Cited by:
  1. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, 04.

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