publication . Preprint . 2011

Cointegrating MiDaS Regressions and a MiDaS Test

J. Isaac Miller;
Open Access
  • Published: 14 Jun 2011
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 str...
arXiv: Statistics::Methodology
free text keywords: cointegration, mixed-frequency series, mixed data sampling, jel:C12, jel:C13, jel:C22

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