表格的长度和宽度都很大，因此表格的顶部和底部从 pdf 文件生成的表格中被切掉。以下是 latex 代码。
\documentclass{article}
\usepackage[colorlinks]{hyperref}
\usepackage[noabbrev]{cleveref}
\usepackage{graphics}
\usepackage{amsmath}
\usepackage{ragged2e}
\usepackage{rotating}
\usepackage{latexsym}
\usepackage{epsfig}
\usepackage{graphicx}
\usepackage{enumitem}
\usepackage[tablename=TABLE,labelsep=newline,aboveskip=0pt,bf]{caption}
\usepackage{clipboard}
\usepackage{booktabs}
\usepackage[svgnames]{xcolor}
\usepackage[round]{natbib}
\usepackage[splitrule]{footmisc}
\usepackage{ltablex}
\usepackage{lipsum}
\usepackage{threeparttablex}
\usepackage{afterpage}
\usepackage{xr}
\usepackage{titlesec}
\usepackage{sectsty,textcase}
\usepackage{longtable}
\usepackage{sepfootnotes}
\usepackage{changepage}
\usepackage{siunitx}
%For making the titles in capital letters
%\allsectionsfont{\MakeTextUppercase}
\sisetup{
detect-mode,
% tight-spacing = true,
% group-digits = false,
input-signs = ,
input-symbols = ,
input-open-uncertainty = ,
input-close-uncertainty = ,
table-align-text-pre = false,
% table-align-text-post = false,
% round-mode = figures,
% round-precision = 3,
% table-space-text-pre = (,
% table-space-text-post = ),
}
\protected\def\stars#1{$^{#1}$}
\newcolumntype{k}{>{\hsize=.1\hsize}S}
%Set Counter
\setcounter{footnote}{2}
%Defining text size to control for spacing in tabularx environment
\newcolumntype{b}{>{\centering}>X}
%\newcolumntype{s}{>{\centering}>{\hsize=.25\hsize}>X}
\newcolumntype{j}{>{\centering}>{\hsize=.75\textwidth}>l}
%Redefining multicolumn environment to take care of @{}
\newcommand{\gmc}[2]{\multicolumn{#1}{@{}#2@{}}}
\begin{document}
\afterpage{
\begin{sidewaystable}
\begin{ThreePartTable}
\begin{TableNotes}[flushleft]
\small
\item \label{r2:a} \noindent This table presents the analyses of the nonlinear relationship between absolute discretionary accruals and observable characteristics. All models are estimated using multivariate linear regression, with dependent variable being absolute discretionary accrual and independent regressors being observable characteristics and their second order power transformations. *,**,*** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively using two-tailed tests. All t-statistics (in parentheses) and p-values are calculated using heteroscedasticity-adjusted clustered (HAC) standard errors by company. Only the model in column (I) includes year and industry-specific intercepts, but for brevity those are not reported.
\end{TableNotes}
\begin{tabularx}{\textwidth}{@{}Xcccccccccccc@{}}
\caption{\textit{Nonlinear Relationship}}\label{tab:sample}\\\toprule\toprule
& \gmc{12}{l} \mbox{Dependent Variable = Absolute Discretionary Accrual}\\\cmidrule(l r){2-13}\\
Variables & (I) & (II) & (III) & (IV) & (V) & (VI) & (VII)& (VIII)& (IX)& (X)& (XI) & (XII) \\\midrule\endfirsthead
\caption{\textit{Nonlinear Relationship - Continued}}\\\toprule\toprule
& \gmc{12}{l} \mbox{Dependent Variable = Absolute Discretionary Accrual}\\\cmidrule(l r){2-13}\\
Variables & (I) & (II) & (III) & (IV) & (V) & (VI) & (VII)& (VIII)& (IX)& (X)& (XI) & (XII) \\\midrule\endhead
\bottomrule\gmc{2}{r}{\small\textit{(Continued)}}\endfoot
\bottomrule\insertTableNotes\endlastfoot
(Intercept) & $0.034^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ \\
& $(8.256)$ & $(326.308)$ & $(302.004)$ & $(295.880)$ & $(285.826)$ & $(308.718)$ & $(307.844)$ & $(284.705)$ & $(285.351)$ & $(293.573)$ & $(291.619)$ & $(285.588)$ \\
poly(LOGASSETS, 2)1 & $-4.942^{***}$ & $-6.068^{***}$ & $-5.532^{***}$ & & & & & & & & & \\
& $(-20.000)$ & $(-25.236)$ & $(-94.950)$ & & & & & & & & & \\
poly(LOGASSETS, 2)2 & $-1.280^{***}$ & $-1.709^{***}$ & $0.948^{***}$ & & & & & & & & & \\
& $(-11.620)$ & $(-15.860)$ & $(15.861)$ & & & & & & & & & \\
poly(LOGMKT, 2)1 & $0.855^{***}$ & $1.401^{***}$ & & $-4.618^{***}$ & & & & & & & & \\
& $(3.435)$ & $(5.758)$ & & $(-80.615)$ & & & & & & & & \\
poly(LOGMKT, 2)2 & $0.747^{***}$ & $0.985^{***}$ & & $0.161^{***}$ & & & & & & & & \\
& $(7.709)$ & $(10.244)$ & & $(2.740)$ & & & & & & & & \\
poly(LEV, 2)1 & $0.486^{***}$ & $0.295^{***}$ & & & $0.123$ & & & & & & & \\
& $(4.894)$ & $(3.009)$ & & & $(1.588)$ & & & & & & & \\
poly(LEV, 2)2 & $-0.381^{***}$ & $-0.307^{***}$ & & & $1.808^{***}$ & & & & & & & \\
& $(-3.967)$ & $(-3.202)$ & & & $(15.131)$ & & & & & & & \\
poly(ROA, 2)1 & $-2.520^{***}$ & $-2.685^{***}$ & & & & $-6.478^{***}$ & & & & & & \\
& $(-10.658)$ & $(-11.346)$ & & & & $(-66.153)$ & & & & & & \\
poly(ROA, 2)2 & $-1.987^{***}$ & $-1.961^{***}$ & & & & $-0.103$ & & & & & & \\
& $(-11.164)$ & $(-11.039)$ & & & & $(-0.912)$ & & & & & & \\
poly(CFO, 2)1 & $-2.219^{***}$ & $-2.349^{***}$ & & & & & $-5.851^{***}$ & & & & & \\
& $(-10.461)$ & $(-11.399)$ & & & & & $(-60.246)$ & & & & & \\
poly(CFO, 2)2 & $3.444^{***}$ & $3.281^{***}$ & & & & & $2.536^{***}$ & & & & & \\
& $(22.091)$ & $(21.086)$ & & & & & $(21.182)$ & & & & & \\
poly(BTM, 2)1 & $-0.315^{***}$ & $-0.288^{***}$ & & & & & & $-0.882^{***}$ & & & & \\
& $(-3.768)$ & $(-3.406)$ & & & & & & $(-12.263)$ & & & & \\
poly(BTM, 2)2 & $0.046$ & $0.077$ & & & & & & $0.590^{***}$ & & & & \\
& $(0.582)$ & $(0.963)$ & & & & & & $(7.778)$ & & & & \\
poly(GROWTH, 2)1 & $0.431^{***}$ & $0.482^{***}$ & & & & & & & $0.826^{***}$ & & & \\
& $(6.145)$ & $(6.971)$ & & & & & & & $(10.033)$ & & & \\
poly(GROWTH, 2)2 & $-0.233^{***}$ & $-0.288^{***}$ & & & & & & & $1.298^{***}$ & & & \\
& $(-3.318)$ & $(-4.113)$ & & & & & & & $(14.251)$ & & & \\
poly(ABSACCRL, 2)1 & $1.299^{***}$ & $1.375^{***}$ & & & & & & & & $4.043^{***}$ & & \\
& $(15.240)$ & $(16.018)$ & & & & & & & & $(48.167)$ & & \\
poly(ABSACCRL, 2)2 & $-0.470^{***}$ & $-0.489^{***}$ & & & & & & & & $-1.020^{***}$ & & \\
& $(-5.455)$ & $(-5.619)$ & & & & & & & & $(-11.227)$ & & \\
poly(ALTMAN, 2)1 & $0.362^{***}$ & $0.434^{***}$ & & & & & & & & & $-3.508^{***}$ & \\
& $(3.427)$ & $(4.219)$ & & & & & & & & & $(-37.658)$ & \\
poly(ALTMAN, 2)2 & $0.177^{*}$ & $0.211^{**}$ & & & & & & & & & $1.280^{***}$ & \\
& $(1.951)$ & $(2.315)$ & & & & & & & & & $(11.271)$ & \\
poly(STDEARN, 2)1 & $1.557^{***}$ & $1.821^{***}$ & & & & & & & & & & $-1.212^{***}$ \\
& $(21.667)$ & $(25.899)$ & & & & & & & & & & $(-21.981)$ \\
poly(STDEARN, 2)2 & $-0.840^{***}$ & $-0.992^{***}$ & & & & & & & & & & $1.155^{***}$ \\
& $(-13.228)$ & $(-15.603)$ & & & & & & & & & & $(19.654)$ \\
Industry and Year F.E. Included & Yes & No & No & No & No & No & No & No & No & No & No & No \\
Adjusted $R^2$ & 0.2622 & 0.2425 & \\
\end{tabularx}
\end{ThreePartTable}
\end{sidewaystable}
}
\end{document}
答案1
使用默认的tabularx包ltablex
\documentclass[a4paper]{article}
\usepackage{geometry}
\usepackage[tablename=TABLE,labelsep=newline,aboveskip=0pt,bf]{caption}
\usepackage{booktabs}
\usepackage[svgnames]{xcolor}
\usepackage{ltablex}
\usepackage{amsmath}
\protected\def\stars#1{$^{#1}$}
\newcolumntype{k}{>{\hsize=.1\hsize}S}
\newcolumntype{b}{>{\centering}>X}
\newcolumntype{j}{>{\centering}>{\hsize=.75\textwidth}>l}
\newcommand{\gmc}[2]{\multicolumn{#1}{@{}#2@{}}}
\geometry{landscape, textheight = 15.75cm, textwidth = 23.4cm, marginratio={1:1}, nomarginpar}
\pagestyle{empty}
\begin{document}
\begingroup
\setlength\tabcolsep{3pt}\renewcommand\arraystretch{1.333}\small
\begin{tabularx}{\textwidth}{@{}X*{12}{>{$}c<{$}}@{}}
\caption{\textit{Nonlinear Relationship}}\label{tab:sample}\\\toprule\toprule
& \gmc{12}{l}{Dependent Variable = Absolute Discretionary Accrual}\\\cmidrule(l r){2-13}
Variables & (I) & (II) & (III) & (IV) & (V) & (VI) & (VII)& (VIII)& (IX)& (X)& (XI) & (XII) \\\midrule
\endfirsthead
\multicolumn{13}{c}{\itshape Nonlinear Relationship -- Continued}\\\hline
& \gmc{12}{l}{Dependent Variable = Absolute Discretionary Accrual}\\\cmidrule(l r){2-13}
Variables & (I) & (II) & (III) & (IV) & (V) & (VI) & (VII)& (VIII)& (IX)& (X)& (XI) & (XII) \\\midrule
\endhead
\bottomrule\gmc{13}{c}{\small\textit{(Continued)}}
\endfoot
\bottomrule
\endlastfoot
(Intercept) & 0.034^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} \\
& (8.256) & (326.308) & (302.004) & (295.880) & (285.826) & (308.718) & (307.844) & (284.705) & (285.351) & (293.573) & (291.619) & (285.588) \\
poly(LOGASSETS, 2)1 & -4.942^{***} & -6.068^{***} & -5.532^{***} & & & & & & & & & \\
& (-20.000) & (-25.236) & (-94.950) & & & & & & & & & \\
poly(LOGASSETS, 2)2 & -1.280^{***} & -1.709^{***} & 0.948^{***} & & & & & & & & & \\
& (-11.620) & (-15.860) & (15.861) & & & & & & & & & \\
poly(LOGMKT, 2)1 & 0.855^{***} & 1.401^{***} & & -4.618^{***} & & & & & & & & \\
& (3.435) & (5.758) & & (-80.615) & & & & & & & & \\
poly(LOGMKT, 2)2 & 0.747^{***} & 0.985^{***} & & 0.161^{***} & & & & & & & & \\
& (7.709) & (10.244) & & (2.740) & & & & & & & & \\
poly(LEV, 2)1 & 0.486^{***} & 0.295^{***} & & & 0.123 & & & & & & & \\
& (4.894) & (3.009) & & & (1.588) & & & & & & & \\
poly(LEV, 2)2 & -0.381^{***} & -0.307^{***} & & & 1.808^{***} & & & & & & & \\
& (-3.967) & (-3.202) & & & (15.131) & & & & & & & \\
poly(ROA, 2)1 & -2.520^{***} & -2.685^{***} & & & & -6.478^{***} & & & & & & \\
& (-10.658) & (-11.346) & & & & (-66.153) & & & & & & \\
poly(ROA, 2)2 & -1.987^{***} & -1.961^{***} & & & & -0.103 & & & & & & \\
& (-11.164) & (-11.039) & & & & (-0.912) & & & & & & \\
poly(CFO, 2)1 & -2.219^{***} & -2.349^{***} & & & & & -5.851^{***} & & & & & \\
& (-10.461) & (-11.399) & & & & & (-60.246) & & & & & \\
poly(CFO, 2)2 & 3.444^{***} & 3.281^{***} & & & & & 2.536^{***} & & & & & \\
& (22.091) & (21.086) & & & & & (21.182) & & & & & \\
poly(BTM, 2)1 & -0.315^{***} & -0.288^{***} & & & & & & -0.882^{***} & & & & \\
& (-3.768) & (-3.406) & & & & & & (-12.263) & & & & \\
poly(BTM, 2)2 & 0.046 & 0.077 & & & & & & 0.590^{***} & & & & \\
& (0.582) & (0.963) & & & & & & (7.778) & & & & \\
poly(GROWTH, 2)1 & 0.431^{***} & 0.482^{***} & & & & & & & 0.826^{***} & & & \\
& (6.145) & (6.971) & & & & & & & (10.033) & & & \\
poly(GROWTH, 2)2 & -0.233^{***} & -0.288^{***} & & & & & & & 1.298^{***} & & & \\
& (-3.318) & (-4.113) & & & & & & & (14.251) & & & \\
poly(ABSACCRL, 2)1 & 1.299^{***} & 1.375^{***} & & & & & & & & 4.043^{***} & & \\
& (15.240) & (16.018) & & & & & & & & (48.167) & & \\
poly(ABSACCRL, 2)2 & -0.470^{***} & -0.489^{***} & & & & & & & & -1.020^{***} & & \\
& (-5.455) & (-5.619) & & & & & & & & (-11.227) & & \\
poly(ALTMAN, 2)1 & 0.362^{***} & 0.434^{***} & & & & & & & & & -3.508^{***} & \\
& (3.427) & (4.219) & & & & & & & & & (-37.658) & \\
poly(ALTMAN, 2)2 & 0.177^{*} & 0.211^{**} & & & & & & & & & 1.280^{***} & \\
& (1.951) & (2.315) & & & & & & & & & (11.271) & \\
poly(STDEARN, 2)1 & 1.557^{***} & 1.821^{***} & & & & & & & & & & -1.212^{***} \\
& (21.667) & (25.899) & & & & & & & & & & (-21.981) \\
poly(STDEARN, 2)2 & -0.840^{***} & -0.992^{***} & & & & & & & & & & 1.155^{***} \\
& (-13.228) & (-15.603) & & & & & & & & & & (19.654) \\
Industry and Year F.E. Included & \text{Yes} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} \\
Adjusted $ R² $ & 0.2622 & 0.2425 & \\
\end{tabularx}
\smallskip\noindent
\small\label{r2:a}%
This table presents the analyses of the nonlinear relationship between absolute discretionary accruals and observable characteristics. All models are estimated using multivariate linear regression, with dependent variable being absolute discretionary accrual and independent regressors being observable characteristics and their second order power transformations. *,**,*** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively using two-tailed tests. All t-statistics (in parentheses) and p-values are calculated using heteroscedasticity-adjusted clustered (HAC) standard errors by company. Only the model in column (I) includes year and industry-specific intercepts, but for brevity those are not reported.
\endgroup
\end{document}
答案2
我会将长表编译为横向格式的独立文件，然后使用包将其包含在主文件中pdfpages。以下是横向格式的代码
\documentclass[a4paper]{article}%
\usepackage{geometry}%,showframe, nomarginpar [textwidth = 15.75cm, textheight = 25cm, marginratio={4:6,5:7}]
\usepackage[colorlinks]{hyperref}
\usepackage[noabbrev]{cleveref}
%\usepackage{graphics}
\usepackage{amsmath}
\usepackage{ragged2e}
\usepackage{rotating}
\usepackage{latexsym}
%\usepackage{epsfig}
\usepackage{graphicx}
\usepackage{enumitem}
\usepackage[tablename=TABLE,labelsep=newline,aboveskip=0pt,bf]{caption}
%\usepackage{clipboard}
\usepackage{booktabs}
\usepackage[svgnames]{xcolor}
%\usepackage[round]{natbib}
\usepackage[splitrule]{footmisc}
\usepackage{ltablex}
\usepackage{lipsum}
\usepackage{threeparttablex}
\usepackage{afterpage}
\usepackage{xr}
\usepackage{titlesec}
%\usepackage{sectsty,textcase}
%\usepackage{longtable}
%\usepackage{sepfootnotes}
\usepackage{changepage}
\usepackage{siunitx}
%For making the titles in capital letters
%\allsectionsfont{\MakeTextUppercase}
\sisetup{
detect-mode,
tight-spacing = true,
group-digits = false,
input-signs = ,
input-symbols = ,
input-open-uncertainty = ,
input-close-uncertainty = ,
table-align-text-pre = false,
table-align-text-post = false,
round-mode = figures,
round-precision = 3,
table-space-text-pre = (,
table-space-text-post = ),
}
\protected\def\stars#1{$^{#1}$}
\newcolumntype{k}{>{\hsize=.1\hsize}S}
\setcounter{footnote}{2}
%Defining text size to control for spacing in tabularx environment
\newcolumntype{b}{>{\centering}>X}
%\newcolumntype{s}{>{\centering}>{\hsize=.25\hsize}>X}
\newcolumntype{j}{>{\centering}>{\hsize=.75\textwidth}>l}
%Redefining multicolumn environment to take care of @{}
\newcommand{\gmc}[2]{\multicolumn{#1}{@{}#2@{}}}
\geometry{landscape, textheight = 15.75cm, textwidth = 23.4cm, marginratio={1:1},showframe, nomarginpar}
\pagestyle{empty}
\begin{document}
\begin{ThreePartTable}
\setlength\tabcolsep{4pt}\renewcommand\arraystretch{1.333}\small
\begin{TableNotes}[flushleft]
\small
\item \label{r2:a} \noindent This table presents the analyses of the nonlinear relationship between absolute discretionary accruals and observable characteristics. All models are estimated using multivariate linear regression, with dependent variable being absolute discretionary accrual and independent regressors being observable characteristics and their second order power transformations. *,**,*** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively using two-tailed tests. All t-statistics (in parentheses) and p-values are calculated using heteroscedasticity-adjusted clustered (HAC) standard errors by company. Only the model in column (I) includes year and industry-specific intercepts, but for brevity those are not reported.
\end{TableNotes}
\begin{tabularx}{\textwidth}{@{}X*{12}{>{$}c<{$}}@{}}
\caption{\textit{Nonlinear Relationship}}\label{tab:sample}\\\toprule\toprule
& \gmc{12}{l} \mbox{Dependent Variable = Absolute Discretionary Accrual}\\\cmidrule(l r){2-13}\\
Variables & (I) & (II) & (III) & (IV) & (V) & (VI) & (VII)& (VIII)& (IX)& (X)& (XI) & (XII) \\
\midrule\endfirsthead
\caption{\textit{Nonlinear Relationship -- Continued}}\\
\toprule\toprule
& \gmc{12}{l} \mbox{Dependent Variable = Absolute Discretionary Accrual}\\\cmidrule(l r){2-13}\\
Variables & \text{(I)} & \text{(II)} & \text{(III)} & \text{(IV)} & \text{(V)} & \text{(VI)} & \text{(VII)}& \text{(VIII)}& \text{(IX)} & \text{(X)} & \text{(XI)} & \text{(XII)} \\
\midrule\endhead
%
\bottomrule\gmc{2}{r}{\small\textit{(Continued)}}
\endfoot
\bottomrule
\noalign{\vskip 2ex}
\insertTableNotes
\endlastfoot
%
(Intercept) & 0.034^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} & 0.059^{***} \\
& (8.256) & (326.308) & (302.004) & (295.880) & (285.826) & (308.718) & (307.844) & (284.705) & (285.351) & (293.573) & (291.619) & (285.588) \\
poly(LOGASSETS, 2)1 & -4.942^{***} & -6.068^{***} & -5.532^{***} & & & & & & & & & \\
& (-20.000) & (-25.236) & (-94.950) & & & & & & & & & \\
poly(LOGASSETS, 2)2 & -1.280^{***} & -1.709^{***} & 0.948^{***} & & & & & & & & & \\
& (-11.620) & (-15.860) & (15.861) & & & & & & & & & \\
poly(LOGMKT, 2)1 & 0.855^{***} & 1.401^{***} & & -4.618^{***} & & & & & & & & \\
& (3.435) & (5.758) & & (-80.615) & & & & & & & & \\
poly(LOGMKT, 2)2 & 0.747^{***} & 0.985^{***} & & 0.161^{***} & & & & & & & & \\
& (7.709) & (10.244) & & (2.740) & & & & & & & & \\
poly(LEV, 2)1 & 0.486^{***} & 0.295^{***} & & & 0.123 & & & & & & & \\
& (4.894) & (3.009) & & & (1.588) & & & & & & & \\
poly(LEV, 2)2 & -0.381^{***} & -0.307^{***} & & & 1.808^{***} & & & & & & & \\
& (-3.967) & (-3.202) & & & (15.131) & & & & & & & \\
poly(ROA, 2)1 & -2.520^{***} & -2.685^{***} & & & & -6.478^{***} & & & & & & \\
& (-10.658) & (-11.346) & & & & (-66.153) & & & & & & \\
poly(ROA, 2)2 & -1.987^{***} & -1.961^{***} & & & & -0.103 & & & & & & \\
& (-11.164) & (-11.039) & & & & (-0.912) & & & & & & \\
poly(CFO, 2)1 & -2.219^{***} & -2.349^{***} & & & & & -5.851^{***} & & & & & \\
& (-10.461) & (-11.399) & & & & & (-60.246) & & & & & \\
poly(CFO, 2)2 & 3.444^{***} & 3.281^{***} & & & & & 2.536^{***} & & & & & \\
& (22.091) & (21.086) & & & & & (21.182) & & & & & \\
poly(BTM, 2)1 & -0.315^{***} & -0.288^{***} & & & & & & -0.882^{***} & & & & \\
& (-3.768) & (-3.406) & & & & & & (-12.263) & & & & \\
poly(BTM, 2)2 & 0.046 & 0.077 & & & & & & 0.590^{***} & & & & \\
& (0.582) & (0.963) & & & & & & (7.778) & & & & \\
poly(GROWTH, 2)1 & 0.431^{***} & 0.482^{***} & & & & & & & 0.826^{***} & & & \\
& (6.145) & (6.971) & & & & & & & (10.033) & & & \\
poly(GROWTH, 2)2 & -0.233^{***} & -0.288^{***} & & & & & & & 1.298^{***} & & & \\
& (-3.318) & (-4.113) & & & & & & & (14.251) & & & \\
poly(ABSACCRL, 2)1 & 1.299^{***} & 1.375^{***} & & & & & & & & 4.043^{***} & & \\
& (15.240) & (16.018) & & & & & & & & (48.167) & & \\
poly(ABSACCRL, 2)2 & -0.470^{***} & -0.489^{***} & & & & & & & & -1.020^{***} & & \\
& (-5.455) & (-5.619) & & & & & & & & (-11.227) & & \\
poly(ALTMAN, 2)1 & 0.362^{***} & 0.434^{***} & & & & & & & & & -3.508^{***} & \\
& (3.427) & (4.219) & & & & & & & & & (-37.658) & \\
poly(ALTMAN, 2)2 & 0.177^{*} & 0.211^{**} & & & & & & & & & 1.280^{***} & \\
& (1.951) & (2.315) & & & & & & & & & (11.271) & \\
poly(STDEARN, 2)1 & 1.557^{***} & 1.821^{***} & & & & & & & & & & -1.212^{***} \\
& (21.667) & (25.899) & & & & & & & & & & (-21.981) \\
poly(STDEARN, 2)2 & -0.840^{***} & -0.992^{***} & & & & & & & & & & 1.155^{***} \\
& (-13.228) & (-15.603) & & & & & & & & & & (19.654) \\
Industry and Year F.E. Included & \text{Yes} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} & \text{No} \\
Adjusted $ R² $ & 0.2622 & 0.2425 & \\
\end{tabularx}
\end{ThreePartTable}
\end{document}
结果如下：
另一种方法是使用pdflscape包临时设置横向格式，但第一列不能使用说明X符，因为它似乎与不兼容tabularx。所以我使用了p{…}说明符。除了不使用该选项外，与之前代码的唯一区别landscape在于将表包装在landscape环境中。
结果：
答案3
tabularx通过调整列宽来调整表格宽度以进行换行段落像这里一样，在表格数据上使用它很少有意义。
tabular我通过删除未使用的包、代替tabularx并截断第 1 列中最后一个条目（这使其太宽）使您的“最小”示例更加简洁。
\documentclass{article}
\usepackage{threeparttablex}
\usepackage{rotating}
\usepackage{booktabs,array}
\protected\def\stars#1{$^{#1}$}
%Set Counter
\setcounter{footnote}{2}
%Redefining multicolumn environment to take care of @{}
\newcommand{\gmc}[2]{\multicolumn{#1}{@{}#2@{}}}
\begin{document}
\begin{sidewaystable}
\begin{ThreePartTable}
\begin{TableNotes}[flushleft]
\small
\item \label{r2:a} \noindent This table presents the analyses of the nonlinear relationship between absolute discretionary accruals and observable characteristics. All models are estimated using multivariate linear regression, with dependent variable being absolute discretionary accrual and independent regressors being observable characteristics and their second order power transformations. *,**,*** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively using two-tailed tests. All t-statistics (in parentheses) and p-values are calculated using heteroscedasticity-adjusted clustered (HAC) standard errors by company. Only the model in column (I) includes year and industry-specific intercepts, but for brevity those are not reported.
\end{TableNotes}
\caption{\textit{Nonlinear Relationship}}\label{tab:sample}
\setlength\tabcolsep{1pt}
\hspace*{-.7cm}\begin{tabular}{@{}>{\footnotesize}lcccccccccccc@{}}
\toprule
& \gmc{12}{l}{Dependent Variable = Absolute Discretionary Accrual}\\\cmidrule(l r){2-13}\\
\small Variables & (I) & (II) & (III) & (IV) & (V) & (VI) & (VII)& (VIII)& (IX)& (X)& (XI) & (XII) \\\midrule
(Intercept) & $0.034^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ & $0.059^{***}$ \\
& $(8.256)$ & $(326.308)$ & $(302.004)$ & $(295.880)$ & $(285.826)$ & $(308.718)$ & $(307.844)$ & $(284.705)$ & $(285.351)$ & $(293.573)$ & $(291.619)$ & $(285.588)$ \\
poly(LOGASSETS, 2)1 & $-4.942^{***}$ & $-6.068^{***}$ & $-5.532^{***}$ & & & & & & & & & \\
& $(-20.000)$ & $(-25.236)$ & $(-94.950)$ & & & & & & & & & \\
poly(LOGASSETS, 2)2 & $-1.280^{***}$ & $-1.709^{***}$ & $0.948^{***}$ & & & & & & & & & \\
& $(-11.620)$ & $(-15.860)$ & $(15.861)$ & & & & & & & & & \\
poly(LOGMKT, 2)1 & $0.855^{***}$ & $1.401^{***}$ & & $-4.618^{***}$ & & & & & & & & \\
& $(3.435)$ & $(5.758)$ & & $(-80.615)$ & & & & & & & & \\
poly(LOGMKT, 2)2 & $0.747^{***}$ & $0.985^{***}$ & & $0.161^{***}$ & & & & & & & & \\
& $(7.709)$ & $(10.244)$ & & $(2.740)$ & & & & & & & & \\
poly(LEV, 2)1 & $0.486^{***}$ & $0.295^{***}$ & & & $0.123$ & & & & & & & \\
& $(4.894)$ & $(3.009)$ & & & $(1.588)$ & & & & & & & \\
poly(LEV, 2)2 & $-0.381^{***}$ & $-0.307^{***}$ & & & $1.808^{***}$ & & & & & & & \\
& $(-3.967)$ & $(-3.202)$ & & & $(15.131)$ & & & & & & & \\
poly(ROA, 2)1 & $-2.520^{***}$ & $-2.685^{***}$ & & & & $-6.478^{***}$ & & & & & & \\
& $(-10.658)$ & $(-11.346)$ & & & & $(-66.153)$ & & & & & & \\
poly(ROA, 2)2 & $-1.987^{***}$ & $-1.961^{***}$ & & & & $-0.103$ & & & & & & \\
& $(-11.164)$ & $(-11.039)$ & & & & $(-0.912)$ & & & & & & \\
poly(CFO, 2)1 & $-2.219^{***}$ & $-2.349^{***}$ & & & & & $-5.851^{***}$ & & & & & \\
& $(-10.461)$ & $(-11.399)$ & & & & & $(-60.246)$ & & & & & \\
poly(CFO, 2)2 & $3.444^{***}$ & $3.281^{***}$ & & & & & $2.536^{***}$ & & & & & \\
& $(22.091)$ & $(21.086)$ & & & & & $(21.182)$ & & & & & \\
poly(BTM, 2)1 & $-0.315^{***}$ & $-0.288^{***}$ & & & & & & $-0.882^{***}$ & & & & \\
& $(-3.768)$ & $(-3.406)$ & & & & & & $(-12.263)$ & & & & \\
poly(BTM, 2)2 & $0.046$ & $0.077$ & & & & & & $0.590^{***}$ & & & & \\
& $(0.582)$ & $(0.963)$ & & & & & & $(7.778)$ & & & & \\
poly(GROWTH, 2)1 & $0.431^{***}$ & $0.482^{***}$ & & & & & & & $0.826^{***}$ & & & \\
& $(6.145)$ & $(6.971)$ & & & & & & & $(10.033)$ & & & \\
poly(GROWTH, 2)2 & $-0.233^{***}$ & $-0.288^{***}$ & & & & & & & $1.298^{***}$ & & & \\
& $(-3.318)$ & $(-4.113)$ & & & & & & & $(14.251)$ & & & \\
poly(ABSACCRL, 2)1 & $1.299^{***}$ & $1.375^{***}$ & & & & & & & & $4.043^{***}$ & & \\
& $(15.240)$ & $(16.018)$ & & & & & & & & $(48.167)$ & & \\
poly(ABSACCRL, 2)2 & $-0.470^{***}$ & $-0.489^{***}$ & & & & & & & & $-1.020^{***}$ & & \\
& $(-5.455)$ & $(-5.619)$ & & & & & & & & $(-11.227)$ & & \\
poly(ALTMAN, 2)1 & $0.362^{***}$ & $0.434^{***}$ & & & & & & & & & $-3.508^{***}$ & \\
& $(3.427)$ & $(4.219)$ & & & & & & & & & $(-37.658)$ & \\
poly(ALTMAN, 2)2 & $0.177^{*}$ & $0.211^{**}$ & & & & & & & & & $1.280^{***}$ & \\
& $(1.951)$ & $(2.315)$ & & & & & & & & & $(11.271)$ & \\
poly(STDEARN, 2)1 & $1.557^{***}$ & $1.821^{***}$ & & & & & & & & & & $-1.212^{***}$ \\
& $(21.667)$ & $(25.899)$ & & & & & & & & & & $(-21.981)$ \\
poly(STDEARN, 2)2 & $-0.840^{***}$ & $-0.992^{***}$ & & & & & & & & & & $1.155^{***}$ \\
& $(-13.228)$ & $(-15.603)$ & & & & & & & & & & $(19.654)$ \\
Industry and Year F.E. & Yes & No & No & No & No & No & No & No & No & No & No & No \\
Adjusted $R^2$ & 0.2622 & 0.2425 & \\
\end{tabular}\hspace*{-3cm}
\end{ThreePartTable}
\end{sidewaystable}
\end{document}