Last updated: 2020-05-25

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Correlation

One of the first things to do before performing a regression is correlation. Correlation is between -1 to 1.

r <- galton_height %>% 
  summarize(r = cor(father, childHeight))
r
          r
1 0.2660385

Panel Regression

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)

ARIMA Forecast

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] HistData_0.8-6  forcats_0.5.0   stringr_1.4.0   dplyr_0.8.5    
 [5] purrr_0.3.4     readr_1.3.1     tidyr_1.0.3     tibble_3.0.1   
 [9] ggplot2_3.3.0   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.13        haven_2.2.0      lattice_0.20-41 
 [5] colorspace_1.4-1 vctrs_0.3.0      generics_0.0.2   htmltools_0.4.0 
 [9] yaml_2.2.1       rlang_0.4.6      later_1.0.0      pillar_1.4.4    
[13] withr_2.2.0      glue_1.4.1       DBI_1.1.0        dbplyr_1.4.3    
[17] modelr_0.1.7     readxl_1.3.1     lifecycle_0.2.0  munsell_0.5.0   
[21] gtable_0.3.0     cellranger_1.1.0 rvest_0.3.5      evaluate_0.14   
[25] knitr_1.28       httpuv_1.5.2     fansi_0.4.1      broom_0.5.6     
[29] Rcpp_1.0.4.6     promises_1.1.0   backports_1.1.6  scales_1.1.1    
[33] jsonlite_1.6.1   fs_1.4.1         hms_0.5.3        digest_0.6.25   
[37] stringi_1.4.6    grid_4.0.0       rprojroot_1.3-2  cli_2.0.2       
[41] tools_4.0.0      magrittr_1.5     crayon_1.3.4     whisker_0.4     
[45] pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.3.2       reprex_0.3.0    
[49] lubridate_1.7.8  rstudioapi_0.11  assertthat_0.2.1 rmarkdown_2.1   
[53] httr_1.4.1       R6_2.4.1         nlme_3.1-147     git2r_0.27.1    
[57] compiler_4.0.0