Estimation of Vector Error Correction Models with Mixed‐Frequency Data

12 Pages Posted: 22 Feb 2013

See all articles by Byeongchan Seong

Byeongchan Seong

Chung-Ang University

Sung Ahn

affiliation not provided to SSRN

Peter A. Zadrozny

U.S. Bureau of Labor Statistics - Department of Labor; CESifo (Center for Economic Studies and Ifo Institute)

Date Written: March 2013

Abstract

Vector autoregressive (VAR) models with error‐correction structures (VECMs) that account for cointegrated variables have been studied extensively and used for further analyses such as forecasting, but only with single‐frequency data. Both unstructured and structured VAR models have been estimated and used with mixed‐frequency data. However, VECMs have not been studied or used with mixed‐frequency data. The article aims partly to fill this gap by estimating a VECM using the expectation‐maximization (EM) algorithm and US data on four monthly coincident indicators and quarterly real GDP and, then, using the estimated model to compute in‐sample monthly smoothed estimates and out‐of‐sample monthly forecasts of GDP. Because the model is treated as operating at the highest monthly frequency and the monthly‐quarterly data are used as given (neither interpolated to all‐monthly data, nor aggregated to all‐quarterly data), the application is expected to be unbiased and efficient. A Monte Carlo analysis compares the accuracy of VECMs estimated with the given mixed‐frequency data vs. with their single‐frequency temporal aggregate.

Keywords: Missing data, cointegration, state‐space model, Kalman filter, expectation maximization algorithm, smoothing

JEL Classification: C13, C22, C32

Suggested Citation

Seong, Byeongchan and Ahn, Sung and Zadrozny, Peter A., Estimation of Vector Error Correction Models with Mixed‐Frequency Data (March 2013). Journal of Time Series Analysis, Vol. 34, Issue 2, pp. 194-205, 2013. Available at SSRN: https://ssrn.com/abstract=2222465 or http://dx.doi.org/10.1111/jtsa.12001

Byeongchan Seong (Contact Author)

Chung-Ang University ( email )

221 Heuksuk-dong
Dongjak-gu
Seoul, 156-756
Korea, Republic of (South Korea)

Sung Ahn

affiliation not provided to SSRN

Peter A. Zadrozny

U.S. Bureau of Labor Statistics - Department of Labor ( email )

2 Massachusetts Avenue, NE
Washington, DC 20212
United States

CESifo (Center for Economic Studies and Ifo Institute)

Poschinger Str. 5
Munich, DE-81679
Germany

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