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ap3.Rnw
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% Appendix 3 Rnw File
<<parent_ap3, include = F>>=
set_parent('main.Rnw') # Set the parent document preamble
@
\section{Appendix C: Other empirical models for Table \ref{tab:complexmod}}
\label{app:third}
Table \ref{tab:complexmodprob} shows probit estimations of the complex model as seen in Equation \ref{eqn:genmod}. Additionally, LPM estimates are displayed for the same models. The coefficients of the probit estimation are similar in sign and significance to the ones of the logit estimation of Table \ref{tab:complexmod}. Average partial effects for these models are also shown below. These are also similar in magnitude to those in Table \ref{tab:apescomp}.
% Probit coefficients table
\begin{table}[htbp]
\begin{center}
\caption{Probit coefficients for modified models}
\label{tab:complexmodprob}
<<table-probit-modified, include = T, results = 'asis', warning = F, message = F>>=
# Source the models
source('scripts/modified_regs.R')
# Coefficient name mappings
coefficient_names_m<-c('(Intercept)' = 'Constant',
'year2016' = '2016 Dummy',
'gndrWoman' = 'Woman',
'age' = 'Age',
'ed' = 'Years of education',
'urUrban' = 'Lives in urban setting',
'eff1' = 'External political efficacy',
'eff2' = 'Internal political efficacy',
'prot3Yes' = 'Participation in a protest',
'pint_dic' = 'Interest in politics',
'corrper' = 'Perceptions of corruption',
'corr_exp' = 'Exposure to corruption',
'unem2_4a' = 'Unemployment',
'pres_aprov' = 'Approval of Pres. Performance',
'polscore' = 'Political Wing',
'year2016:unem2_4a' = 'Unemployment Interaction',
'year2016:pres_aprov' = 'Pres. Approval Interaction',
'year2016:polscore' = 'Pol. Wing Interaction')
# GOF mapping
modified_models_stats<-
list(list("raw" = "nobs", "clean" = "$N$", "fmt" = 0),
list("raw" = "aic", "clean" = "AIC", "fmt" = 2),
list("raw" = "bic", "clean" = "BIC", "fmt" = 2))
# Do the table
modelsummary(modified_models_prob,
output = 'latex_tabular',
gof_map = modified_models_stats,
coef_map = coefficient_names_m)
@
\end{center}
\textbf{Note:} Probit coefficients of the modified models as described by Equation \ref{eqn:genmod}. Standard errors consider design effects of the AB complex survey design.\\
*$p$ < 0.1, **$p$< 0.05, ***$p$ < 0.01.
\end{table}
% APE Probit Table
\begin{table}[htbp]
\caption{Average partial effects for probit models in Table \ref{tab:probitsimp}}
\begin{center}
\label{tab:probitcompape}
<<baseline-apes-table-probit, include = T, results = 'asis', cache = F>>=
# Coefficient Mapping
coefficient_names_apes_c<-c('gndrWoman' = 'Woman',
'age' = 'Age',
'ed' = 'Years of education',
'urUrban' = 'Lives in urban setting',
'eff1' = 'External political efficacy',
'eff2' = 'Internal political efficacy',
'prot3Yes' = 'Participation in a protest',
'pint_dic' = 'Interest in politics',
'corrper' = 'Perceptions of corruption',
'corr_exp' = 'Exposure to corruption',
'unem2_4a' = 'Unemployment',
'pres_aprov' = 'Approval of Pres. performance',
'polscore' = 'Political wing')
# Goodness of fit mappings
gof_modapes<-
list(list("raw" = "nobs", "clean" = "$N$", "fmt" = 0))
modelsummary(modified_probit_mfx,
output = 'latex_tabular',
stars = c('*'= 0.1, '**' = 0.05, '***'= 0.01),
coef_map = coefficient_names_apes_c,
gof_map = gof_modapes)
@
\vspace{0.25cm}
\end{center}
\textbf{Note:} Average partial effects for the models estimated in Table \ref{complexmodprob}. Data from the open-access AB databases. Standard errors consider design effects of the AB complex survey design.\\
*$p$ < 0.1, **$p$< 0.05, ***$p$ < 0.01.
\end{table}
% LPM coefficients table
\begin{table}[htbp]
\begin{center}
\caption{LPM coefficients for modified models}
\label{tab:complexmodlpm}
<<table-lpm-modified, include = T, results = 'asis', warning = F, message = F>>=
# Coefficient name mappings
coefficient_names_m<-c('(Intercept)' = 'Constant',
'year2016' = '2016 Dummy',
'gndrWoman' = 'Woman',
'age' = 'Age',
'ed' = 'Years of education',
'urUrban' = 'Lives in urban setting',
'eff1' = 'External political efficacy',
'eff2' = 'Internal political efficacy',
'prot3Yes' = 'Participation in a protest',
'pint_dic' = 'Interest in politics',
'corrper' = 'Perceptions of corruption',
'corr_exp' = 'Exposure to corruption',
'unem2_4a' = 'Unemployment',
'pres_aprov' = 'Approval of Pres. Performance',
'polscore' = 'Political Wing',
'year2016:unem2_4a' = 'Unemployment Interaction',
'year2016:pres_aprov' = 'Pres. Approval Interaction',
'year2016:polscore' = 'Pol. Wing Interaction')
# GOF mapping
modified_models_stats<-
list(list("raw" = "nobs", "clean" = "$N$", "fmt" = 0),
list("raw" = "aic", "clean" = "AIC", "fmt" = 2),
list("raw" = "bic", "clean" = "BIC", "fmt" = 2))
# Do the table
modelsummary(modified_models_lpm,
output = 'latex_tabular',
gof_map = modified_models_stats,
coef_map = coefficient_names_m)
@
\end{center}
\textbf{Note:} LPM coefficients of the modified models as described by Equation \ref{eqn:genmod}. Standard errors consider design effects of the AB complex survey design.\\
*$p$ < 0.1, **$p$< 0.05, ***$p$ < 0.01.
\end{table}