Identifying Causal Relationships Between Nonstationary Stochastic Processes: An Examination Of Alternative Approaches In Small Samples
AbstractA Monte Carlo investigation is used to examine the performance of two commonly used tests for Granger causality for univariate and bivariate nonstationary ARMA (p,q) processes. Tests are applied to raw data, first differences of the raw data, and detrended versions of the series. The results indicate that for independent series the tests are robust regardless of sample size. With bivariate series and nonstationarity, the tests results are sensitive to the ARMA specification, whether the data are filtered and the type of filter used, and the sample size.
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Bibliographic InfoArticle provided by Western Agricultural Economics Association in its journal Western Journal of Agricultural Economics.
Volume (Year): 13 (1988)
Issue (Month): 02 (December)
Research Methods/ Statistical Methods;
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- Bailey, DeeVon & Brorsen, B. Wade, 1985. "Dynamics Of Regional Fed Cattle Prices," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 10(01), July.
- Geweke, John & Meese, Richard & Dent, Warren, 1983. "Comparing alternative tests of causality in temporal systems : Analytic results and experimental evidence," Journal of Econometrics, Elsevier, vol. 21(2), pages 161-194, February.
- Emerick, Paula A. & Willett, Lois Schertz & Novakovic, Andrew M., 1993. "Incorporating Price Regulation in Causality Tests for Dairy Markets," Staff Papers 121338, Cornell University, Department of Applied Economics and Management.
- Zapata, Hector O. & Gil, Jose M., 1999. "Cointegration and causality in international agricultural economics research," Agricultural Economics: The Journal of the International Association of Agricultural Economists, International Association of Agricultural Economists, vol. 20(1), January.
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