Estimation of vector error correction models with mixed-frequency data
- Authors
- Seong, Byeongchan; Ahn, Sung K.; Zadrozny, Peter A.
- Issue Date
- Mar-2013
- Publisher
- WILEY-BLACKWELL
- Keywords
- Missing data; cointegration; state-space model; Kalman filter; expectation maximization algorithm; smoothing
- Citation
- JOURNAL OF TIME SERIES ANALYSIS, v.34, no.2, pp 194 - 205
- Pages
- 12
- Journal Title
- JOURNAL OF TIME SERIES ANALYSIS
- Volume
- 34
- Number
- 2
- Start Page
- 194
- End Page
- 205
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/14795
- DOI
- 10.1111/jtsa.12001
- ISSN
- 0143-9782
1467-9892
- 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.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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