\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
