Cointegrating MiDaS Regressions and a MiDaS Test

J. Miller

No 1104, Working Papers from Department of Economics, University of Missouri

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.

Keywords: cointegration; mixed-frequency series; mixed data sampling (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Pages: 38 pgs.
Date: 2011-06-14
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
Note: Substantially revised and updated as WP 12-11
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:umc:wpaper:1104

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