Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures
AbstractParsimoniously 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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Bibliographic InfoPaper provided by Department of Economics, University of Missouri in its series Working Papers with number 1211.
Length: 35 pgs.
Date of creation: 27 Aug 2012
Date of revision:
cointegration; mixed-frequency time series; mixed data sampling (MiDaS); autoregressive distributed lag; GDP forecasts;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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- Eric Ghysels & J. Isaac Miller, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," Working Papers 1307, Department of Economics, University of Missouri.
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