期刊
JOURNAL OF ECONOMETRICS
卷 192, 期 1, 页码 207-230出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2015.07.007
关键词
Granger causality test; Local asymptotic power; Mixed data sampling (MIDAS); Temporal aggregation; Vector autoregression (VAR)
资金
- Marie Curie
- Federal Reserve Bank of New York through the Resident Scholar Program
- European Central Bank through the Duisenberg Scholar Program
We develop Granger causality tests that apply directly to data sampled at different frequencies. We show that taking advantage of mixed frequency data allows us to better recover causal relationships when compared to the conventional common low frequency approach. We also show that the new causality tests have higher local asymptotic power as well as more power in finite samples compared to conventional tests. In an empirical application involving U.S. macroeconomic indicators, we show that the mixed frequency approach and the low frequency approach produce very different causal implications, with the former yielding more intuitively appealing result. (C) 2016 Elsevier B.V. All rights reserved.
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