{smcl}
{* *! version 1.1.1}{...}
{title:Title}
{phang}
{bf:cdecompose} {hline 2} Executes estimation of canonical permanent-transitory state space models.
{marker syntax}{...}
{title:Syntax}
{p 4 17 2}
{cmd:cdecompose}
{it:y1}
{it:y2}
...
{it:yT}
{ifin}
[{cmd:,} {bf:p}({it:real}) {bf:q}({it:real}) {bf:delta}({it:real}) {bf:nboot}({it:real})]
{marker description}{...}
{title:Description}
{phang}
{cmd:cdecompose} executes estimation of canonical permanent-transitory state space models based on
{browse "https://onlinelibrary.wiley.com/doi/full/10.3982/QE1117":Hu, Moffitt, and Sasaki (2019)}.
Consider the state space model {it:Y}({it:t}) = {it:U}({it:t}) + {it:V}({it:t}) where {it:U}({it:t}) is an unobserved permanent component that follows the unit-root process {it:U}({it:t}) = {it:U}({it:t}-1) + {it:W}({it:t}) and {it:V}({it:t}) is an unobserved transitory component that follows the semiparametric ARMA({it:p},{it:q}) process {it:V}({it:t}) = {it:r}(1){it:V}({it:t}-1) + ... + {it:r}({it:p}){it:V}({it:t}-{it:p}) + {it:G}({it:e}({it:t}),...,{it:e}({it:t}-{it:q})).
The command takes {it:p} + 2{it:q} + 2 periods of {it:y}({it:t}) as input and estimates the mean, standard deviation, skewness, and kurtosis of the permanent component {it:U}({it:t}) and transitory component {it:V}({it:t}). In order to estimate these statistics for time period {it:t},
a user should use {it:y}({it:t}-{it:p}-{it:q})...{it:y}({it:t}+{it:q}+1) as input.
{marker options}{...}
{title:Options}
{phang}
{bf:p({it:real})} sets the AR order {it:p} of the ARMA({it:p},{it:q}) model of transitory process. The default value is {bf: p(1)}.
{p_end}
{phang}
{bf:q({it:real})} sets the MA order {it:q} of the ARMA({it:p},{it:q}) model of transitory process. The default value is {bf: q(1)}.
{p_end}
{phang}
{bf:delta({it:real})} sets a precision parameter for computing numerical derivative in approximating derivatives of empirical characteristic functions for moment estimation. The default value is {bf: delta(5)}.
{p_end}
{phang}
{bf:nboot({it:real})} sets the number of bootstrap iterations for approximating standard errors. The default value is {bf: nboot(1000)}.
{p_end}
{marker examples}{...}
{title:Examples}
{phang}
({bf:y37}...{bf:y42} earnings at ages 38...42, respectively){p_end}
{phang}Estimation at age 40 under the ARMA(1,1) transitory process:{p_end}
{phang}{cmd:. cdecompose y38 y39 y40 y41 y42}{p_end}
{phang}Estimation at age 40 under the ARMA(2,1) transitory process:{p_end}
{phang}{cmd:. cdecompose y37 y38 y39 y40 y41 y42, p(2) q(1)}{p_end}
{phang}(Note that it is a common practice in the earnings dynamics literature
that {bf:y37}...{bf:y42} are defined as the residual of earnings on
observed attributes.){p_end}
{marker stored}{...}
{title:Stored results}
{phang}
{bf:cdecompose} stores the following in {bf:e()}:
{p_end}
{phang}
Scalars
{p_end}
{phang2}
{bf:e(N)} {space 10}observations
{p_end}
{phang2}
{bf:e(p)} {space 10}AR order
{p_end}
{phang2}
{bf:e(q)} {space 10}MA order
{p_end}
{phang}
Macros
{p_end}
{phang2}
{bf:e(cmd)} {space 8}{bf:cdecompose}
{p_end}
{phang2}
{bf:e(properties)} {space 1}{bf:b V}
{p_end}
{phang}
Matrices
{p_end}
{phang2}
{bf:e(b)} {space 10}coefficient vector
{p_end}
{phang2}
{bf:e(V)} {space 10}variance-covariance matrix of the estimators
{p_end}
{phang2}
{bf:e(rho)} {space 8}AR coefficients
{p_end}
{phang}
Functions
{p_end}
{phang2}
{bf:e(sample)} {space 5}marks estimation sample
{p_end}
{title:Reference}
{p 4 8}Hu, Y., R. Moffitt, and Y. Sasaki. 2019. Semiparametric Estimation of the Canonical PermanentâTransitory Model of Earnings Dynamics. {it:Quantitative Economics}, 10 (4), pp. 1495-1536.
{browse "https://onlinelibrary.wiley.com/doi/full/10.3982/QE1117":Link to Paper}.
{p_end}
{title:Authors}
{p 4 8}Yingyao Hu, Johns Hopkins University, Baltimore, MA.{p_end}
{p 4 8}Robert Moffitt, Johns Hopkins University, Baltimore, MA.{p_end}
{p 4 8}Yuya Sasaki, Vanderbilt University, Nashville, TN.{p_end}