我有以下 MWE：
\documentclass[xcolor=pdftex,t,11pt]{beamer}
\newenvironment{stepenumerate}{\begin{enumerate}[<+->]}{\end{enumerate}}
\newenvironment{stepitemize}{\begin{itemize}[<+->]}{\end{itemize} }
\newenvironment{stepenumeratewithalert}{\begin{enumerate}[<+-| alert@+>]}{\end{enumerate}}
\newenvironment{stepitemizewithalert}{\begin{itemize}[<+-| alert@+>]}{\end{itemize} }
\begin{document}
\section{Time series}
\subsection{Few preliminaries}
\begin{frame}
\frametitle{Time series}
\framesubtitle{Few preliminaries}
\begin{stepitemizewithalert}
\item Before entering our topics, let us review some of the properties of time series.\medskip
\begin{stepenumeratewithalert}[Prop. 1:]
\item Time series data have autoregressive (AR), moving average (MA) and seasonal dynamic processes. Since data are ordered in time, this means that past values affect future values. This often results in a violation of the assumption of no serial correlation in the OLS:\medskip
\begin{equation*}
E\left[\varepsilon_{t},\varepsilon_{s}|\mathbf{x}\right]\neq0 \ \forall t\neq s
\end{equation*}
\item Time series often have time dependent moments (as mean, variance and so on). In many time series, the mean or the variance increase over time (this is a non--stationarity issue, we will discuss later).
\end{stepenumeratewithalert}
\end{stepitemizewithalert}
\end{frame}
\begin{frame}
\frametitle{Time series}
\framesubtitle{Few preliminaries}
\begin{stepitemizewithalert}
\item In addition:\medskip
\begin{stepenumeratewithalert}[Prop. 3:]
\setcounter{enumi}{2}
\item The sequential nature of the data allows for forecasting of future events.\medskip
\item Events in time series may generate a structural break (e.g. remember the Chow break--point test, you carried out last time).\medskip
\item Many time series are in an equilibrium relationship (this is the cointegration topic we will discuss later).\medskip
\item Many time series are endogenously related. In this case we can model them within a multi-equation time series approach like the vector autoregressive model (VAR).\medskip
\item The effect of the regressors on the regressand can vary over time. In this case, we can use some varying parameter models to account for this.
\end{stepenumeratewithalert}
\end{stepitemizewithalert}
\end{frame}
\end{document}
我尝试自定义 stepenumeratewithalert 环境。我希望它显示 Prop. 1、Prop. 2 等等，而不是 1、2 等等。
第一张幻灯片有效：我有 Prop. 1 和 Prop. 2。在第二张幻灯片中，所有条目都是 Prop. 3。我尝试使用设置计数器，\setcounter{enumi}{2}但它不起作用。有什么建议吗？提前致谢。
答案1
我想你的思路是对的\setcounter{enumi}{2}，但是在这种情况下你需要[Prop. 1:]。
\documentclass[xcolor=pdftex,t,11pt]{beamer}
\newenvironment{stepenumerate}{\begin{enumerate}[<+->]}{\end{enumerate}}
\newenvironment{stepitemize}{\begin{itemize}[<+->]}{\end{itemize} }
\newenvironment{stepenumeratewithalert}{\begin{enumerate}[<+-| alert@+>]}{\end{enumerate}}
\newenvironment{stepitemizewithalert}{\begin{itemize}[<+-| alert@+>]}{\end{itemize} }
\begin{document}
\section{Time series}
\subsection{Few preliminaries}
\begin{frame}
\frametitle{Time series}
\framesubtitle{Few preliminaries}
\begin{stepitemizewithalert}
\item Before entering our topics, let us review some of the properties of time series.\medskip
\begin{stepenumeratewithalert}[Prop. 1:]
\item Time series data have autoregressive (AR), moving average (MA) and seasonal dynamic processes. Since data are ordered in time, this means that past values affect future values. This often results in a violation of the assumption of no serial correlation in the OLS:\medskip
\begin{equation*}
E\left[\varepsilon_{t},\varepsilon_{s}|\mathbf{x}\right]\neq0 \ \forall t\neq s
\end{equation*}
\item Time series often have time dependent moments (as mean, variance and so on). In many time series, the mean or the variance increase over time (this is a non--stationarity issue, we will discuss later).
\end{stepenumeratewithalert}
\end{stepitemizewithalert}
\end{frame}
\begin{frame}
\frametitle{Time series}
\framesubtitle{Few preliminaries}
\begin{stepitemizewithalert}
\item In addition:\medskip
\begin{stepenumeratewithalert}[Prop. 1:]
\addtocounter{enumi}{2}
\item The sequential nature of the data allows for forecasting of future events.\medskip
\item Events in time series may generate a structural break (e.g. remember the Chow break--point test, you carried out last time).\medskip
\item Many time series are in an equilibrium relationship (this is the cointegration topic we will discuss later).\medskip
\item Many time series are endogenously related. In this case we can model them within a multi-equation time series approach like the vector autoregressive model (VAR).\medskip
\item The effect of the regressors on the regressand can vary over time. In this case, we can use some varying parameter models to account for this.
\end{stepenumeratewithalert}
\end{stepitemizewithalert}
\end{frame}
\end{document}
