Ich bin gerade dabei, meine Dissertation zu schreiben und habe ziemlich lange Regressionstabellen, was bedeutet, dass ich nur begrenzt Platz für Text auf einer Seite habe. Gibt es eine Möglichkeit, die Tabelle weiter nach oben auf der Seite zu verschieben und sie auch zu verkleinern, um mehr erklärenden Text unterzubringen?
\documentclass[11pt]{article}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{amsmath,amsfonts,amssymb}
\usepackage{booktabs}
\usepackage{dcolumn}
\usepackage{mathtools}
\usepackage{graphicx}
\usepackage[utf8]{inputenc}
\usepackage{rotating}
\usepackage{adjustbox}
\usepackage{tabularx}
\usepackage[margin=35mm]{geometry}
\usepackage{titlesec}
\usepackage{siunitx} % added
\usepackage{threeparttable}
\newcommand\mc[1]{\multicolumn{1}{c}{#1}}
\titleformat*{\subsection}{\normalfont\large\itshape}
\makeatletter
\renewcommand\footnoterule{
\kern-3\p@
\hrule\@width \textwidth
\kern2.6\p@}
\makeatother
\usepackage{rotating}
\begin{document}
\section{Results}\label{Results}
\subsection{Objective 1}
\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Investment on Bribes: Pooled Sample table\label{tab1}}
\begin{tabular*}{0.7\columnwidth}{@{\hskip\tabcolsep\extracolsep\fill}l*{1}
{D{.}{.}{-1}}}
\toprule
&\multicolumn{1}{c}{(1)}\\
&\multicolumn{1}{c}{ln\_invest}\\
\midrule
Bribes& 0.008 \\
& (0.0136) \\
\addlinespace
Medium(20-99) & 0.529 \\
& (0.267) \\
\addlinespace
Large(100 and over) & 2.171\sym{***}\\
& (0.460) \\
\addlinespace
Government & 0.427 \\
& (0.435) \\
\addlinespace
Foreign & -0.337 \\
& (0.257) \\
\addlinespace
Fincons & 0.107 \\
& (0.154) \\
\addlinespace
Court & -0.168 \\
& (0.148) \\
\addlinespace
lnAge & 0.069 \\
& (0.242) \\
\addlinespace
Constant & 9.976\sym{***}\\
& (0.574) \\
\addlinespace
Year Dummies & \multicolumn{1}{c}{YES}\\
\addlinespace
Industry Dummies & \multicolumn{1}{c}{YES}\\
\addlinespace
Missing Year Dummy & \multicolumn{1}{c}{YES}\\
\addlinespace
\midrule
Observations & \multicolumn{1}{c}{6417} \\
\bottomrule
\multicolumn{2}{l}{\footnotesize \sym{*} \(p<0.05\), \sym{**} \(p<0.01\),
\sym{***} \(p<0.001\)}\\
\multicolumn{2}{l}{\footnotesize Note: Robust standard errors in
parentheses, clustered by Country}\\
\end{tabular*}
\end{table}
The first model estimated, as shown in T, is the most basic form of the mechanism investigated. When controlling for industry and year effects, I find a positive relationship between corruption and bribery, although this result is statistically not significant. Results suggest that for a $1\%$ increase in bribe, this increases ln\_invest by 0.8 percentage points. This positive relationship contradicts our initial hypothesis of bribery
\end{document}
Antwort1
\documentclass[11pt]{article}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{amsmath,amsfonts,amssymb}
\usepackage{booktabs}
\usepackage{dcolumn}
\usepackage{mathtools}
\usepackage{graphicx}
\usepackage[utf8]{inputenc}
\usepackage{rotating}
\usepackage{adjustbox}
\usepackage{tabularx}
\usepackage[margin=35mm]{geometry}
\usepackage{titlesec}
\usepackage{siunitx} % added
\usepackage{threeparttable}
\newcommand\mc[1]{\multicolumn{1}{c}{#1}}
\titleformat*{\subsection}{\normalfont\large\itshape}
\makeatletter
\renewcommand\footnoterule{
\kern-3\p@
\hrule\@width \textwidth
\kern2.6\p@}
\makeatother
\usepackage{rotating}
\begin{document}
\section{Results}\label{Results}
\subsection{Objective 1}
\begin{table}[htbp]\centering
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{Investment on Bribes: Pooled Sample table\label{tab1}}
\begin{tabular*}{0.7\columnwidth}{@{\hskip\tabcolsep\extracolsep\fill}l*{2}
{D{.}{.}{-1}}}
\toprule
\multicolumn{3}{c}{(1)}\\
&\multicolumn{2}{c}{ln\_invest}\\
\midrule
Bribes& 0.008 & (0.0136) \\
Medium(20-99) & 0.529 & (0.267) \\
Large(100 and over) & 2.171\sym{***} & (0.460) \\
Government & 0.427 & (0.435) \\
Foreign & -0.337 & (0.257) \\
Fincons & 0.107 & (0.154) \\
Court & -0.168 & (0.148) \\
lnAge & 0.069 & (0.242) \\
Constant & 9.976\sym{***} & (0.574) \\
\addlinespace
Year Dummies & \multicolumn{2}{c}{YES}\\
Industry Dummies & \multicolumn{2}{c}{YES}\\
Missing Year Dummy & \multicolumn{2}{c}{YES}\\
\midrule
Observations & \multicolumn{1}{c}{6417} \\
\bottomrule
\multicolumn{3}{l}{\footnotesize \sym{*} \(p<0.05\), \sym{**} \(p<0.01\),
\sym{***} \(p<0.001\)}\\
\multicolumn{3}{l}{\footnotesize Note: Robust standard errors in
parentheses, clustered by Country}\\
\end{tabular*}
\end{table}
The first model estimated, as shown in T, is the most basic form of the mechanism investigated. When controlling for industry and year effects, I find a positive relationship between corruption and bribery, although this result is statistically not significant. Results suggest that for a $1\%$ increase in bribe, this increases ln\_invest by 0.8 percentage points. This positive relationship contradicts our initial hypothesis of bribery
\end{document}
Antwort2
Differenz zuDavid CarlisleDie Antwort lautet, dass ich Pakete ausnutze threeparttableund siunitxkeine der OP-Definitionen verwende (ihr Zweck ist mir nicht klar), die Definition, für \mcdie jetzt in der Tabelle verwendet wird, leicht ändere und aus der Präambel nur das in der Tabelle verwendete Paket beibehalte:
\documentclass[11pt]{article}
\usepackage[margin=35mm]{geometry}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
%\usepackage{mathtools,amsfonts,amssymb}
\usepackage{booktabs, threeparttable}
\newcommand\mc[1]{\multicolumn{2}{c}{#1}}% <-- changed
\usepackage{siunitx}
\begin{document}
\begin{center}
\begin{threeparttable}[htb]
\caption{Investment on Bribes: Pooled Sample table}
\label{tab1}
\sisetup{ table-space-text-pre ={(},
table-space-text-post={\textsuperscript{***}},
table-align-text-pre=false,
table-align-text-post=false,
explicit-sign,
input-open-uncertainty={[},
input-close-uncertainty={]}
}
\begin{tabular*}{0.6\linewidth}{l S[table-format=-1.4] S[table-format=1.4]}
\toprule
& \mc{(1)} \\
& \mc{ln\_invest} \\
\midrule
Bribes & 0.008
& (0.0136) \\
Medium(20-99) & 0.529
& (0.267) \\
Large(100 and over) & 2.171\tnote{***}
& (0.460) \\
Government & 0.427
& (0.435) \\
Foreign & -0.337
& (0.257) \\
Fincons & 0.107 \\
& (0.154) \\
Court & -0.168
& (0.148) \\
lnAge & 0.069
& (0.242) \\
Constant & 9.976\tnote{***}
& (0.574) \\
Year Dummies & \mc{YES} \\
Industry Dummies & \mc{YES} \\
Missing Year Dummy & \mc{YES} \\
\midrule
Observations & \mc{6417} \\
\bottomrule
\end{tabular*}\scriptsize\raggedright\smallskip
\begin{tablenotes}[para, flushleft]
\item[*] \(p<0.1\)
\item[**] \(p<0.01\)
\item[***] \(p<0.001\)
\end{tablenotes}
Note: Robust standard errors in parentheses, clustered by Country
\end{threeparttable}
\end{center}
The first model estimated, as shown in T, is the most basic form of the mechanism investigated. When controlling for industry and year effects, I find a positive relationship between corruption and bribery, although this result is statistically not significant. Results suggest that for a $1\%$ increase in bribe, this increases ln\_invest by 0.8 percentage points. This positive relationship contradicts our initial hypothesis of bribery
\end{document}