 Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag StructuresNo 1211, Working Papers from Department of Economics, University of Missouri Abstract: Parsimoniously specified distributed lag models have enjoyed a resurgence under the MiDaS moniker (Mixed Data Sampling) as a feasible way to model time series observed at very different sampling frequencies. I introduce cointegrating mixed data sampling (CoMiDaS) regressions. I derive asymptotic limits under substantially more general conditions than the extant theoretical literature allows. In addition to the possibility of cointegrated series, I allow for regressors and an error term with general correlation patterns, both serially and mutually. The nonlinear least squares estimator still obtains consistency to the minimum mean-squared forecast error parameter vector, and the asymptotic distribution of the coefficient vector is Gaussian with a possibly singular variance. I propose a novel test of a MiDaS null against a more general and possibly infeasible alternative mixed- frequency specification. An empirical application to nowcasting global real economic activity using monthly financial covariates illustrates the utility of the approach. Keywords: cointegration; mixed-frequency time series; mixed data sampling (MiDaS); autoregressive distributed lag; GDP forecasts (search for similar items in EconPapers) Published in Journal of Financial Econometrics 2014 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:umc:wpaper:1211 Access Statistics for this paper More papers in Working Papers from Department of Economics, University of Missouri Contact information at EDIRC. |
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