[This article was first published on Yet Another Blog in Statistical Computing » S+/R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. # READ QUARTERLY DATA FROM CSV library(zoo) ts1 <- read.zoo('Documents/data/macros.csv', header = T, sep = ",", FUN = as.yearqtr) # CONVERT THE DATA TO STATIONARY TIME SERIES ts1$hpi_rate <- log(ts1$hpi / lag(ts1$hpi)) ts1$unemp_rate <- log(ts1$unemp / lag(ts1$unemp)) ts2 <- ts1[1:nrow(ts1) - 1, c(3, 4)] # METHOD 1: LMTEST PACKAGE library(lmtest) grangertest(unemp_rate ~ hpi_rate, order = 1, data = ts2) # Granger causality test # # Model 1: unemp_rate ~ Lags(unemp_rate, 1:1) + Lags(hpi_rate, 1:1) # Model 2: unemp_rate ~ Lags(unemp_rate, 1:1) # Res.Df Df F Pr(>F) # 1 55 # 2 56 -1 4.5419 0.03756 * # --- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # METHOD 2: VARS PACKAGE library(vars) var <- VAR(ts2, p = 1, type = "const") causality(var, cause = "hpi_rate")$Granger # Granger causality H0: hpi_rate do not Granger-cause unemp_rate # # data: VAR object var # F-Test = 4.5419, [...]
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