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Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures

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.

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Paper provided by Department of Economics, University of Missouri in its series Working Papers with number 1211.

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Length: 35 pgs.
Date of creation: 27 Aug 2012
Date of revision:
Publication status: Published in Journal of Financial Econometrics 2014
Handle: RePEc:umc:wpaper:1211
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  4. J. Isaac Miller, 2011. "Conditionally Efficient Estimation of Long-run Relationships Using Mixed-frequency Time Series," Working Papers 1103, Department of Economics, University of Missouri, revised 30 May 2012.
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  12. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," Economics Working Papers ECO2009/32, European University Institute.
  13. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
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  17. Anthony S. Tay, 2007. "Financial Variables as Predictors of Real Output Growth," Development Economics Working Papers 22482, East Asian Bureau of Economic Research.
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  24. Robert B. Barsky & Lutz Kilian, 2004. "Oil and the Macroeconomy Since the 1970s," Journal of Economic Perspectives, American Economic Association, vol. 18(4), pages 115-134, Fall.
  25. Chambers, Marcus J., 2009. "Discrete Time Representations Of Cointegrated Continuous Time Models With Mixed Sample Data," Econometric Theory, Cambridge University Press, vol. 25(04), pages 1030-1049, August.
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