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完整代码

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完整代码 
\documentclass[10pt]{article}
\usepackage{graphicx}
\usepackage{amsmath}
\begin{document}

\title{Case studies: Long run relationship}

\maketitle 

\begin{abstract}
The cointegrating approach is proposed
\end{abstract}

\section{introduction}


Over the last century

\section{Methods and Results}

The 9 prices indexes from each province  

A VAR(p) is written as

\begin{equation}
k_{t } =  A_{0}+ A_1k_{t-1}+ A_{2}k_{t-2}+ A_{2} k_{t-2} +......A_nk_{t-n} + e_{t}
\end{equation}
####################################################################################
where k_{n}  =  (k_{1t}, k_{2t},.....,m_{Kt))  for k=1,....., K time series. The key assumptions are in the VAR(p)
##################################################################################
\begin{eqnarray}
\Delta k_{t} & = & \Delta\Gamma{1}k_{t-1}+\Delta\Gamma{2}k_{t-2}+...+\Delta\Gamma{n}k_{t-n} 
 \end{eqnarray}

#########################################################################################
Where \gamma_{i}=-(I-A_{1} - ...A_{i}),  i= 2,.....,p-1     \Pi= -(I - A_{1}-....-A_{p})  is a N-dimensional time series,  A_{0}  is the intercept term, e_{t} is white noise
########################################################################################
\end{document}

大家好，我正试图用 latex 写这篇文章，但由于这 4 行代码，它根本无法工作。如果删除以下 4 行代码，那么文章会写得很好。但是当我插入它们时，latex 无法识别。但是我必须在我的文章中插入这些解释。请您能帮我添加这 4 个代码，以便我写出正式的文章。我必须插入，因为每个人都必须解释他的数学模型上的每个符号。请帮我将它们插入我的文章中。

where k_{n}  =  (k_{1t}, k_{2t},.....,m_{Kt))  for k=1,....., K time series. The key assumptions are in the VAR(p)


Where \gamma_{i}=-(I-A_{1} - ...A_{i}),  i= 2,.....,p-1     \Pi= -(I - A_{1}-....-A_{p})  is a N-dimensional time series,  A_{0}  is the intercept term, e_{t} is white noise

答案1

试着关注这个.log文件。例如，你会注意到以下消息：

! Missing $ inserted.
<inserted text> 
                $
l.29 where k_
             {n}  =  (k_{1t}, k_{2t},.....,m_{Kt))  for $k=1,....., K$ time ...

这是 LaTeX 告诉你第 29 行缺少一个$符号。正如 David 在评论中建议的那样，你需要用$...$或括住内联数学内容\(...\)。因此，对于给你带来麻烦的两个情况，你应该执行以下操作

where $k_{n}  =  (k_{1t}, k_{2t},.....,m_{Kt})$  for $k=1,....., K$ time series.

和

Where $\gamma_{i}=-(I-A_{1} - ...A_{i})$,  $i= 2,.....,p-1$     
$\Pi= -(I - A_{1}-....-A_{p})$ is a $N$-dimensional time series,  
$A_{0}$ is the intercept term, $e_{t}$ is white noise.

还有其他几个地方的代码不被视为“最佳实践”：

  • 而不是+ ... +，使用+ \cdots +
  • 而不是, ... ,，使用, \ldots ,或, \dots ,
  • eqnarray不要使用，而是使用align

完整代码

\documentclass[10pt]{article}
\usepackage{graphicx}
\usepackage{amsmath}
\begin{document}

\title{Case studies: Long run relationship}

\maketitle 

\begin{abstract}
  The cointegrating approach is proposed
\end{abstract}

\section{introduction}

Over the last century

\section{Methods and Results}

The 9 prices indexes from each province  

A VAR(p) is written as

\begin{equation}
  k_{t } =  A_{0}+ A_1k_{t-1}+ A_{2}k_{t-2}+ A_{2} k_{t-2} +\cdots +A_nk_{t-n} + e_{t}
\end{equation}
% ####################################################################################
where $k_{n}  =  (k_{1t}, k_{2t},\ldots,m_{Kt})$  for $k=1,\ldots, K$ time series. 
The key assumptions are in the VAR(p)
% ##################################################################################
\begin{align}
  \Delta k_{t} & = \Delta\Gamma{1}k_{t-1}+\Delta\Gamma{2}k_{t-2}+\cdots+\Delta\Gamma{n}k_{t-n} 
\end{align}

% ##############################################################################
Where $\gamma_{i}=-(I-A_{1} - \cdots -A_{i})$,  $i= 2,\ldots,p-1$     
$\Pi= -(I - A_{1}-\cdots-A_{p})$  is a $N$-dimensional time series,  
$A_{0}$  is the intercept term, $e_{t}$ is white noise
% ##############################################################################
\end{document}

输出

在此处输入图片描述

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