Granger Causality Test Là Gì
What is Granger causality?
Granger causality is a way lớn investigate causality between two variables in a time series. The method is a probabilistic tài khoản of causality; it uses empirical data sets khổng lồ find patterns of correlation.
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Causality is closely related lớn the idea of cause-and-effect, although it isn’t exactly the same. A variable X is causal to variable Y if X is the cause of Y or Y is the cause of X. However, with Granger causality, you aren’t testing a true cause-and-effect relationship; What you want to know is if a particular variable comes before another in the time series. In other words, if you find Granger causality in your data there isn’t a causal liên kết in the true sense of the word (for example, sales of Easter baskets Granger-cause Easter!). Note: When econometricians say “cause,” what they mean is “Granger-cause,” although a more appropriate word might be “precedence” (Leamer, 1985).
Bottom Up / Top Down
Granger causality is a “bottom up” procedure, where the assumption is that the data-generating processes in any time series are independent variables; then the data sets are analyzed to see if they are correlated. The opposite is a “top down” method which assumes the processes are not independent; the data sets are then analyzed to see if they are generated independently from each other.
Running the Test
The null hypothesis for the demo is that lagged x-values vì not explain the variation in y. In other words, it assumes that x(t) doesn’t Granger-cause y(t). Theoretically, you can run the Granger Test lớn find out if two variables are related at an instantaneous moment in time. However, that version of the thử nghiệm is seldom used because it’s not very useful, so I have sầu not included the steps here.
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Steps for the F-Test
The procedure can get complex because of the large number of options, including choosing from a mix of equations for the f-value calculations. You can skip the vast majority of the intermediate steps by using software. The Granger causality chạy thử is part of many popular economics software packages, including E-Views & PC-Give. Any number of lags can be selected with a few clicks.
Make sure your time series is stationary before proceeding. Data should be transformed khổng lồ eliminate the possibility of autocorrelation. You should also make sure your Model doesn’t have any unit roots, as these will skew the chạy thử results.
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The basic steps for running the thử nghiệm are:
Alternative Test
If you have sầu a large number of variables và lags, your F-kiểm tra can thua power. An alternative would be to lớn run a chi-square demo, constructed with likelihood ratio or Wald tests. Although both versions give sầu practically the same result, the F-chạy thử is much easier khổng lồ run.
References Cromwell, J. et. al. (1994) Multivariate Tests for Time Series Models, Issue 100. Sage University.Granger, C. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, Volume 37, Issue 3 (Aug). Available from here.Hoover, K. (2001) Causality in Macroeconomics. Cambridge University Press.Leamer, E. (1985) Vector Autoregressions for Causal Inference?, in K. Brunner – A.H. Meltzer (a cura di), Understanding Monetary Regime, Carnegie-Rochester Conference Series on Public Policy, 22, pp. 255-304.
CITE THIS AS:Stephanie Glen. "Granger Causality: Definition, Running the Test" From thanglon77.com: Elementary Statistics for the rest of us! https://www.thanglon77.com/granger-causality/
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