

    \filetitle{chowlin}{Chow-Lin distribution of low-frequency observations over higher-frequency periods}{tseries/chowlin}

	\paragraph{Syntax}

\begin{verbatim}
[Y2,B,RHO,U1,U2] = chowlin(Y1,X2)
[Y2,B,RHO,U1,U2] = chowlin(Y1,X2,range,...)
\end{verbatim}

\paragraph{Input arguments}

\begin{itemize}
\item
  \texttt{Y1} {[} tseries {]} - Low-frequency input tseries object that
  will be distributed over higher-frequency observations.
\item
  \texttt{X2} {[} tseries {]} - Tseries object with regressors used to
  distribute the input data.
\item
  \texttt{range} {[} numeric {]} - Low-frequency date range on which the
  distribution will be computed.
\end{itemize}

\paragraph{Output arguments}

\begin{itemize}
\item
  \texttt{Y2} {[} tseries {]} - Output data distributed with higher
  frequency.
\item
  \texttt{B} {[} numeric {]} - Vector of regression coefficients.
\item
  \texttt{RHO} {[} numeric {]} - Actually used autocorrelation
  coefficient in the residuals.
\item
  \texttt{U1} {[} tseries {]} - Low-frequency regression residuals.
\item
  \texttt{U2} {[} tseries {]} - Higher-frequency regression residuals.
\end{itemize}

\paragraph{Options}

\begin{itemize}
\item
  \texttt{'constant='} {[} \emph{\texttt{true}} \textbar{}
  \texttt{false} {]} - Include a constant term in the regression.
\item
  \texttt{'log='} {[} \texttt{true} \textbar{} \emph{\texttt{false}} {]}
  - Logarithmise the data before distribution, de-logarithmise
  afterwards.
\item
  \texttt{'ngrid='} {[} numeric \textbar{} \emph{\texttt{200}} {]} -
  Number of grid search points for finding autocorrelation coefficient
  for higher-frequency residuals.
\item
  \texttt{'rho='} {[} \emph{\texttt{'estimate'}} \textbar{}
  \texttt{'positive'} \textbar{} \texttt{'negative'} \textbar{} numeric
  {]} - How to determine the autocorrelation coefficient for
  higher-frequency residuals.
\item
  \texttt{'timeTrend='} {[} \texttt{true} \textbar{}
  \emph{\texttt{false}} {]} - Include a time trend in the regression.
\end{itemize}

\paragraph{Description}

Chow,G.C., and A.Lin (1971). Best Linear Unbiased Interpolation,
Distribution and Extrapolation of Time Series by Related Times Series.
Review of Economics and Statistics, 53, pp.~372-75.

See also Appendix 2 in Robertson, J.C., and E.W.Tallman (1999). Vector
Autoregressions: Forecasting and Reality. FRB Atlanta Economic Review,
1st Quarter 1999, pp.4-17.

\paragraph{Example}


