Last updated: 2020-05-19
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| Rmd | 27ba057 | KaranSShakya | 2020-05-17 | update ggplots + data for it |
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| Rmd | 5adafbc | KaranSShakya | 2020-05-16 | panel regression + numeric/character |
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Library: stargazer, knitr, sandwitch, lmtest, AER, forecast, plm. These contain more than necessary packages required for panel regression. The first step is to make your data frame into a panel form.
panel <- pdata.frame(data, index=c("Country", "Year"))For fixed effects model (two-way - country and year)
fixed.effect <- plm(y~x1+I(x1^2)+x2+x3,
data=panel, model="within", effect = "twoway")For random effects model
random.effect <- plm(y~x1+I(x1^2)+x2+x3,
data=panel, model="random")To view these models without and with standard error.
stargazer(random.effect, type='text')
stargazer(coeftest(random.effect, vcovHC), type="text")Hausman test to verify if to use fixed effect or random effect. If p < 0.05, use fixed effects model.
phtest(fixed.effect, random.effect)First convert the dataset into time-series form. We can see this through the plot().
temp.ts <- ts(temp, start=1998, end=2017, frequency = 1)
plot(temp.ts)The basic steps for the ARIMA prediction are as follows. Do not know the detail, but this looks at historical data to predict future trends. It is one variable dependent, meaning interactions are harder to integrate.
arima.temp <- arima(temp.ts, order=c(3,1,1))
summary(arima.temp)
plot(forecast(arima.temp, 5))
forecast(arima.temp, 5)
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 rprojroot_1.3-2 digest_0.6.25 later_1.0.0
[5] R6_2.4.1 backports_1.1.6 git2r_0.27.1 magrittr_1.5
[9] evaluate_0.14 stringi_1.4.6 rlang_0.4.6 fs_1.4.1
[13] promises_1.1.0 whisker_0.4 rmarkdown_2.1 tools_4.0.0
[17] stringr_1.4.0 glue_1.4.1 httpuv_1.5.2 xfun_0.13
[21] yaml_2.2.1 compiler_4.0.0 htmltools_0.4.0 knitr_1.28