Quasi-Experimental Designs

We are aimed to find solution for following reasearch questions:

  1. which government measure is most important to combat COVID-19 affected the economy (Covariates)

Canonical Correlation

The government support factors which are meant to combat COVID-19 and offer supports to affected economy. Thus, we have graph following three correlation charts based on each individual index: Strigency index, government response index, and economic support index.

main[is.na(main)] <- 0
main_str = as.data.frame(c(main[,16],main[,19:20],main[,22]))
main_gov = as.data.frame(c(main[,17],main[,19:20],main[,22]))
main_eco = as.data.frame(c(main[,18:20],main[,22]))

X = cor(main_str)
Y = cor(main_gov)
Z = cor(main_eco)

corrplot(X, method ="square")

corrplot(Y, method = "square")

corrplot(Z, method = "square")

str_gov <- matcor(X,Y)
str_eco <- matcor(X,Z)
gov_eco <- matcor(Y,Z)


img.matcor(str_gov, type = 2)

img.matcor(str_eco, type = 2)

img.matcor(gov_eco, type = 2)

  1. Is there any difference in the effectiveness of measures taken by governments in countries with differing levels of per capita income in their effect on the economy (Factor)

#Hypothesis: H0: There’s no significant difference on economic factors before and after applying government measures H1:There’s significant difference on economic factors before and after applying government measures

Mixed Linear Effect Model

##Not sure how to interpret this model….

m1 = lmer(StringencyIndex ~ PMI_PCT  + (1|PCI_Type) + (1 | Period), data = main)
summary(m1)
## Linear mixed model fit by REML ['lmerMod']
## Formula: StringencyIndex ~ PMI_PCT + (1 | PCI_Type) + (1 | Period)
##    Data: main
## 
## REML criterion at convergence: 1609.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7784 -0.7609 -0.2138  0.5636  2.9661 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  PCI_Type (Intercept) 142.6    11.94   
##  Period   (Intercept) 667.9    25.84   
##  Residual             483.6    21.99   
## Number of obs: 178, groups:  PCI_Type, 3; Period, 3
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   55.144     16.557   3.331
## PMI_PCT        3.624      4.464   0.812
## 
## Correlation of Fixed Effects:
##         (Intr)
## PMI_PCT -0.008
confint(m1)
## Computing profile confidence intervals ...
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
## profile: using minstep
## Warning in profile.merMod(object, which = parm, signames = oldNames, ...): non-
## monotonic profile for (Intercept)
## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## (Intercept): falling back to linear interpolation
##                 2.5 %   97.5 %
## .sig01       4.931264 38.26194
## .sig02      11.148046 65.37566
## .sigma      19.805556 24.45829
## (Intercept) 17.648412 92.82467
## PMI_PCT     -5.139266 12.40322
ranef(m1) %>% head(5)
## $PCI_Type
##        (Intercept)
## High     -9.287724
## Low      12.996499
## Medium   -3.708775
## 
## $Period
##             (Intercept)
## COVID Month    19.00773
## Post COVID     10.11656
## Pre COVID     -29.12429