Revision 1073 trunk/docs/manuals/admb/admb.tex
admb.tex (revision 1073)  

26  26 
\newcommand\Pone{P_{tt1}} 
27  27 
\newcommand\diag{\textrm{diag}} 
28  28 
\newcommand\ep{\textrm{elem\_prod}} 
29 
\newcommand\admbversion{11.1} 

29  30  
30  31 
\makeindex 
31  32  
...  ...  
35  36 
\smalltitlepart{An Introduction to} 
36  37 
\largetitlepart{AD MODEL BUILDER} 
37  38 
\smalltitlepart{for Use in Nonlinear Modeling and Statistics} 
38 
\vspace{3ex}\textsf{\textit{Version 11.1~~(20130501)}}\vspace{3ex} 

39 
\vspace{3ex}\textsf{\textit{Version \admbversion~~(20130501)\\[3pt] 

40 
Revised manual~~(20130624)}}\vspace{3ex} 

39  41 
} 
40  42 
\author{\textsf{\textit{David Fournier}}} 
41  43 
\manualname{AD Model Builder} 
...  ...  
44  46  
45  47 
~\vfill 
46  48 
\noindent ADMB Foundation, Honolulu.\\\\ 
47 
\noindent This is the manual for AD Model Builder (ADMB) version 10.0.\\\\ 

49 
\noindent This is the manual for AD Model Builder (ADMB) version 

50 
\admbversion.\\\\ 

48  51 
\noindent Copyright \copyright\ 1993, 1994, 1996, 2000, 2001, 2004, 2007, 2008, 
49 
2011 David Fournier\\\\ 

52 
2011, 2013 David Fournier\\\\


50  53 
\noindent The latest edition of the manual is available at:\\ 
51 
\url{http://admbproject.org/documentation/manuals/admbusermanuals}


54 
\url{http://admbproject.org/documentation/manuals} 

52  55  
53  56 
\tableofcontents 
54  57  
...  ...  
163  166 
render the estimation of parameters in such nonlinear models more tractable. The 
164  167 
\ADMS package is intended to organize these techniques in such a way that they 
165  168 
are easy to employ (where possible, employing them in a way that the user does 
166 
not need to be aware of them), so that investigating nonlinear statistical models 

167 
becomesso far as possibleas simple as for linear statistical models. 

169 
not need to be aware of them), so that investigating nonlinear statistical 

170 
models becomesso far as possibleas simple as for linear statistical 

171 
models. 

168  172  
169  173 
\section{Installing the software} 
170  174  
...  ...  
3052  3056 
$\infty$ for this example). The integer argument \texttt{nsteps} determines how 
3053  3057 
accurate the integration will be. Higher values of \texttt{nsteps} will be more 
3054  3058 
accurate, but greatly increase the amount of time necessary to fit the model. 
3055 
The basic strategy is to use a moderate value for \texttt{nsteps}, such as~6, and


3056 
then to increase this value to see if the parameter estimates change much. 

3059 
The basic strategy is to use a moderate value for \texttt{nsteps}, such as~6, 

3060 
and then to increase this value to see if the parameter estimates change much.


3057  3061 
\begin{lstlisting} 
3058  3062 
FUNCTION dvariable h(const dvariable& z) 
3059  3063 
\end{lstlisting} 
...  ...  
3423  3427 
default behavior of \ADM\ is to read in initial parameter values for the 
3424  3428 
parameters from a \texttt{PAR} file, if it finds one. Otherwise, they are given 
3425  3429 
default values consistent with their type. The quantity~\texttt{f} is a vector 
3426 
of four coefficients for the autoregressive process. \texttt{Pcoff} is a $2\times


3427 
2$ matrix used to parameterize the transition matrix \texttt{P} for the Markov


3428 
process. Its values are restricted to lie between~$0.01$ and~$0.99$. 

3430 
of four coefficients for the autoregressive process. \texttt{Pcoff} is a 

3431 
$2\times 2$ matrix used to parameterize the transition matrix \texttt{P} for the


3432 
Markov process. Its values are restricted to lie between~$0.01$ and~$0.99$.


3429  3433 
\texttt{smult} is a number used to parameterize \texttt{sigma} and \texttt{var} 
3430  3434 
(which is the variance) as a multiple of the meansquared residuals. This 
3431  3435 
reparameterization undimensionalizes the calculation and is a good technique to 
...  ...  
3616  3620 
interest. The matrix~\texttt{z} is calculated using a transformed matrix, 
3617  3621 
because \ADM\ deals with vector rows better than columns. The probability 
3618  3622 
distribution for the states in period~1, \texttt{qb1}, is set equal to the 
3619 
unconditional distribution for a Markov process in terms of its transition matrix


3620 
\texttt{P}, as discussed in~\cite{hamilton1994}. The transition matrix is used


3621 
to compute the probability distribution of the states in periods $(2,1)$, 

3623 
unconditional distribution for a Markov process in terms of its transition 

3624 
matrix \texttt{P}, as discussed in~\cite{hamilton1994}. The transition matrix is


3625 
used to compute the probability distribution of the states in periods $(2,1)$,


3622  3626 
$(3,2,1)$, $(4,3,2,1)$, and finally, $(5,4,3,2,1)$. For the last quintuplet, 
3623  3627 
this is the probability distribution before observing~\texttt{y(5)}. The 
3624  3628 
quantities \texttt{eps} in the code correspond to the possible realized values 
Also available in: Unified diff