\documentclass[12pt]{article} % Basic packages \usepackage{geometry} \usepackage{amsfonts} \usepackage{amsmath} \usepackage{amssymb} \usepackage{graphicx} \usepackage[small,bf,center]{caption} \usepackage{subfig} \usepackage{setspace} \usepackage{float} \usepackage[autostyle]{csquotes} % Packages for automated tables \usepackage{booktabs} \usepackage{tabularx} \usepackage{standalone} \usepackage{pdflscape} \onehalfspacing \textwidth=6.0in \textheight=8.5in \begin{document} \title{My Paper} \author{Julian Reif\thanks{University of Illinois and NBER.}} \maketitle \begin{abstract} \noindent This paper provides an example of a document with tables and figures that were automated using Stata. \end{abstract} \clearpage \section{Summary} This example paper includes tables and figures that were created using Stata and outputted into the \textbf{/analysis/results} project folder. Copy the contents of that folder to \textbf{/paper} to update the tables and figures in this document. Table \ref{tab:my_summary_stats} reports summary statistics for Stata's \textbf{auto.dta} dataset.% % \footnote{Type \textbf{sysuse auto, clear} at the Stata prompt to load this dataset.} % The average price of automobiles in this dataset is \6,165. The price distribution, iillustrated in Figure \ref{fig:price_histogram}, is skewed right. I estimate the association between automobile prices and fuel efficiency using the following linear model: \begin{align} PRICE_i&=\alpha + \beta X_i + \varepsilon \label{eqn:model} \end{align} The outcome variable,PRICE_i$, is the price of automobile$i$. The parameter of interest is$\beta$, a vector of coefficients. In my first specification (spec 1''), the vector$X_i$includes miles per gallon. The second specification (spec 2'') also includes the car's weight. I estimate this model using ordinary least squares and report standard errors that are robust to heteroskedasticity. The analysis is performed first using Stata, and then repeated using R. Table \ref{tab:my_regressions} reports my Stata estimates, separately for domestic and foreign cars. Column (1) reports that an increase in fuel efficiency of 1 mile per gallon is associated with a \$329 reduction in the price of domestic automobiles. Column (2) shows that this association becomes positive and insignificant when I also include weight as a regressor. Columns (3) and (4) show that these associations are similar for foreign automobiles. Table \ref{tab:my_regressions_with_r} compares these Stata estimates to estimates from R. Panel A reproduces the Stata estimates that were presented in Table \ref{tab:my_regressions}. Panel B of Table \ref{tab:my_regressions_with_r} reports estimates when I repeat this analysis in R using the \textbf{lm\_robust} command from the \textit{estimatr} package. The point estimates and the standard errors are identical across both software packages. \clearpage \section{Figures and Tables} %%% % Figure: Price histogram %%% \begin{figure}[ht] \caption{Automobile prices}\label{fig:price_histogram} \begin{center} {\includegraphics[width=1\textwidth]{./figures/price_histogram.pdf}} \parbox{\linewidth}{\footnotesize {Notes: Data were obtained from Stata's built-in auto dataset.}} \end{center} \end{figure} \clearpage %%% % Tables %%% \input{tables/my_summary_stats.tex} \input{tables/my_regressions.tex} \input{tables/my_regressions_with_r.tex} \end{document}