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*! version 1.2 09-Oct-2022
capture program drop binsregselect
program define binsregselect, eclass
version 13
syntax varlist(min=2 numeric ts fv) [if] [in] [fw aw pw] [, deriv(integer 0) ///
absorb(string asis) reghdfeopt(string asis) ///
bins(numlist integer max=2 >=0) pselect(numlist integer >=0) sselect(numlist integer >=0) ///
binspos(string) nbins(string) ///
binsmethod(string) nbinsrot(string) ///
simsgrid(integer 20) savegrid(string asis) replace ///
dfcheck(numlist integer max=2 >=0) masspoints(string) usegtools(string) ///
vce(passthru) useeffn(string) randcut(numlist max=1 >=0 <=1) ///
norotnorm numdist(string) numclust(string)]
/* last line only for internal use */
set more off
**************************************
******** Regularization constant ****
**************************************
local qrot=2
local rot_lb=1
local den_alp=0.975
**************************************
* Create weight local
if ("`weight'"!="") {
local wt [`weight'`exp']
local wtype=substr("`weight'",1,1)
}
**********************
** Extract options ***
**********************
* default vce
if ("`vce'"=="") local vce "vce(robust)"
* binning
* indictors: selectJ (F means select p)
local selectJ ""
*if ("`nbins'"=="F") local nbins ""
if ("`nbins'"=="T"|"`nbins'"=="") local nbins=0 /* default: select J */
local len_nbins=0
if ("`nbins'"!="") {
numlist "`nbins'", integer range(>=0) sort
local nbins=r(numlist)
local len_nbins: word count `nbins'
}
if ("`nbins'"=="0"|`len_nbins'>1|"`bins'"!="") local selectJ "T"
* analyze bin- and order-related options
local len_p=0
local len_s=0
if ("`pselect'"!="") {
numlist "`pselect'", integer range(>=`deriv') sort
local plist=r(numlist)
}
if ("`sselect'"!="") {
numlist "`sselect'", integer range(>=0) sort
local slist=r(numlist)
}
if ("`bins'"!="") {
tokenize `bins' /* overwrite pselect and sselect */
local plist "`1'"
local slist "`2'"
if ("`plist'"=="") local plist=`deriv'
if ("`slist'"=="") local slist=`plist'
}
local len_p: word count `plist'
local len_s: word count `slist'
if ((`len_p'==1&`len_s'==0)|(`len_p'==0&`len_s'==1)|(`len_p'==1&`len_s'==1)) {
local selectJ "T"
}
if ("`selectJ'"=="T") {
if (`len_p'>1|`len_s'>1) {
di as error "Only one p and one s are allowed."
exit
}
if ("`plist'"=="") local plist=`deriv'
if ("`slist'"=="") local slist=`plist'
}
local len_p: word count `plist'
local len_s: word count `slist'
if ((`len_p'>1|`len_s'>1) & "`selectJ'"!="T") {
local selectJ "F" /* select p and s */
}
if ("`selectJ'"=="") {
di as error "Degree, smoothness, or # of bins are not correctly specified."
exit
}
* find all compatible pairs of p and s
tempname deg_mat
if ("`selectJ'"=="F") {
if (`len_p'>0 & `len_s'==0) {
mat `deg_mat'=J(`len_p', 2, .)
forval i=1/`len_p' {
local el : word `i' of `plist'
mat `deg_mat'[`i',1]=`el'
mat `deg_mat'[`i',2]=`el'
}
}
if (`len_p'==0 & `len_s'>0) {
mat `deg_mat'=J(`len_s', 2, .)
forval i=1/`len_s' {
local el : word `i' of `slist'
mat `deg_mat'[`i',1]=`el'
mat `deg_mat'[`i',2]=`el'
}
}
if (`len_p'>0 & `len_s'>0) {
mat `deg_mat'=J(`=`len_p'*`len_s'',2,.)
local ncom=0
forval i=1/`len_p' {
local el_p : word `i' of `plist'
forval j=1/`len_s' {
local el_s : word `j' of `slist'
if (`el_p'>=`el_s') {
mat `deg_mat'[`=`ncom'+1',1]=`el_p'
mat `deg_mat'[`=`ncom'+1',2]=`el_s'
local ++ncom
}
}
}
if (`ncom'>0) mat `deg_mat'=`deg_mat'[1..`ncom', 1..2]
else {
di as error "degree and smoothness incompatible"
exit
}
}
}
else {
mat `deg_mat'=(`plist', `slist')
}
* take submatrix with p>=deriv
local ncom=0
local index ""
tempname m_deg /* degree matrix to be used */
forval i=1/`=rowsof(`deg_mat')' {
if (`deg_mat'[`i',1]>=`deriv') {
local ++ncom
mat `m_deg'=(nullmat(`m_deg') \ `deg_mat'[`i',1..2])
}
}
if (`ncom'==0) {
di as error "Degree and smoothness incorrectly specified."
exit
}
if ("`binspos'"=="") local binspos "QS"
if ("`binspos'"=="es") local binspos "ES"
if ("`binspos'"=="qs") local binspos "QS"
if ("`binsmethod'"=="") local binsmethod "DPI"
if ("`binsmethod'"=="rot") local binsmethod "ROT"
if ("`binsmethod'"=="dpi") local binsmethod "DPI"
if ("`dfcheck'"=="") local dfcheck 20 30
* mass check?
if ("`masspoints'"=="") {
local massadj "T"
local localcheck "T"
}
else if ("`masspoints'"=="off") {
local massadj "F"
local localcheck "F"
}
else if ("`masspoints'"=="noadjust") {
local massadj "F"
local localcheck "T"
}
else if ("`masspoints'"=="nolocalcheck") {
local massadj "T"
local localcheck "F"
}
else if ("`masspoints'"=="veryfew") {
di as text in gr "Warning: masspoints(veryfew) not allowed for bin selection."
local localcheck "F"
local rot_fewobs "T"
local dpi_fewobs "T"
}
tokenize `dfcheck'
local dfcheck_n1 "`1'"
local dfcheck_n2 "`2'"
* use gtools commands instead?
if ("`usegtools'"=="off") local usegtools ""
if ("`usegtools'"=="on") local usegtools usegtools
if ("`usegtools'"!="") {
capture which gtools
if (_rc) {
di as error "Gtools package not installed."
exit
}
local localcheck "F"
* use gstats tab instead of tabstat/collapse
* use gquantiles instead of _pctile
* use gunique instead of binsreg_uniq
* use fasterxtile instead of irecode (within binsreg_irecode)
* shut down local checks & do not sort
}
* cluster var?
local vcetemp: subinstr local vce "vce(" "", all
local vcetemp: subinstr local vcetemp ")" "", all
tokenize "`vcetemp'", parse(", ")
if ("`1'"=="cl"|"`1'"=="clu"|"`1'"=="clus"|"`1'"=="clust"|"`1'"=="cluste"|"`1'"=="cluster") {
if ("`3'"==""|"`3'"==",") local clusterON "T"
local clustervar `2' /* only keep the 1st cluster var */
}
* use reghdfe?
if ("`absorb'"!="") {
capture which reghdfe
if (_rc) {
di as error "reghdfe not installed."
exit
}
}
*****************************************************
* Error checks
if (`deriv'<0) {
di as error "deriv() incorrectly specified."
exit
}
if (`simsgrid'<0) {
di as error "simsgrid() incorrectly specified."
exit
}
if (`"`savegrid'"'!=`""'&"`replace'"=="") {
confirm new file `"`savegrid'.dta"'
}
if ("`nbinsrot'"!="") {
confirm integer n `nbinsrot'
}
* Mark sample
preserve
* Parse varlist into y_var, x_var and w_var
tokenize `varlist'
fvrevar `1', tsonly
local y_var "`r(varlist)'"
fvrevar `2', tsonly
local x_var "`r(varlist)'"
fvrevar `2', list
local x_varname "`r(varlist)'"
macro shift 2
local w_var "`*'"
fvrevar `w_var', list
local w_varname "`r(varlist)'"
fvrevar `w_var', tsonly
local w_var "`r(varlist)'" /* so time series operator respected */
marksample touse /* now renew the marker to account for missing values */
qui keep if `touse'
local eN=_N
local samplesize=_N
if ("`usegtools'"==""&("`masspoints'"!="off"|"`binspos'"=="QS")) {
if ("`:sortedby'"!="`x_var'") sort `x_var', stable
}
* Normalize support
tempvar z_var
if ("`wtype'"=="f") qui sum `x_var' `wt', meanonly
else qui sum `x_var', meanonly
local N=r(N) /* sample size, with wt */
local xmin=r(min)
local xmax=r(max)
tempname xvec zvec Xm binedges
* Extract effective sample size
local Ndist=.
if ("`massadj'"=="T") {
if ("`numdist'"!=""&"`numdist'"!=".") {
local Ndist=`numdist'
}
else {
if ("`usegtools'"=="") {
mata: `binedges'=binsreg_uniq(st_data(.,"`x_var'"), ., 1, "Ndist")
mata: mata drop `binedges'
}
else {
qui gunique `x_var'
local Ndist=r(unique)
}
}
local eN=min(`eN', `Ndist')
}
local Nclust=.
if ("`clusterON'"=="T") {
if ("`numclust'"!=""&"`numclust'"!=".") {
local Nclust=`numclust'
}
else {
if ("`usegtools'"=="") {
mata: st_local("Nclust", strofreal(rows(uniqrows(st_data(.,"`clustervar'")))))
}
else {
qui gunique `clustervar'
local Nclust=r(unique)
}
}
local eN=min(`eN', `Nclust')
}
* Take a subsample?
if ("`randcut'"!="") {
mata: `xvec'=st_data(.,"`x_var'")
qui keep if runiform()<=`randcut'
local eN_sub=_N
local Ndist_sub=.
if ("`massadj'"=="T") {
if ("`usegtools'"=="") {
mata: `binedges'=binsreg_uniq(st_data(.,"`x_var'"), ., 1, "Ndist_sub")
mata: mata drop `binedges'
}
else {
qui gunique `x_var'
local Ndist_sub=r(unique)
}
local eN_sub=min(`eN_sub', `Ndist_sub')
}
local Nclust_sub=.
if ("`clusterON'"=="T") {
if ("`usegtools'"=="") {
mata: st_local("Nclust_sub", strofreal(rows(uniqrows(st_data(.,"`clustervar'")))))
}
else {
qui gunique `clustervar'
local Nclust_sub=r(unique)
}
local eN_sub=min(`eN_sub', `Nclust_sub')
}
}
else {
local eN_sub=`eN'
local Ndist_sub=`Ndist'
local Nclust_sub=`Nclust'
}
sum `x_var', meanonly
gen `z_var'=(`x_var'-`=r(min)')/(`=r(max)'-`=r(min)')
mata: `zvec'=st_data(., "`z_var'") /* normalized x, subsample */
* Define matrices here to save results
tempname mat_imse_bsq_rot mat_imse_var_rot mat_imse_bsq_dpi mat_imse_var_dpi ///
mat_J_rot_unreg mat_J_rot_reg mat_J_rot_uniq mat_J_dpi mat_J_dpi_uniq
mat `mat_imse_bsq_rot'=J(`ncom',1,.)
mat `mat_imse_var_rot'=J(`ncom',1,.)
mat `mat_imse_bsq_dpi'=J(`ncom',1,.)
mat `mat_imse_var_dpi'=J(`ncom',1,.)
mat `mat_J_rot_unreg'=J(`ncom',1,.)
mat `mat_J_rot_reg'=J(`ncom',1,.)
mat `mat_J_rot_uniq'=J(`ncom',1,.)
mat `mat_J_dpi'=J(`ncom',1,.)
mat `mat_J_dpi_uniq'=J(`ncom',1,.)
****** START loop here **************
forval num=1/`ncom' {
* extract p and s from the matrix
local p=`m_deg'[`num', 1]
local s=`m_deg'[`num', 2]
* prepare locals for reporting
local imse_bsq_rot=.
local imse_var_rot=.
local imse_bsq_dpi=.
local imse_var_dpi=.
***************************
******* ROT choice ********
***************************
tempname vcons bcons coef
tempvar resid1 resid2 /* only used by reghdfe */
local J_rot_reg=.
local J_rot_unreg=.
if ("`nbinsrot'"!="") local J_rot_reg=`nbinsrot'
* Initial checking of sample size (for ROT)
if (`J_rot_reg'==.&"`rot_fewobs'"=="") {
if (`eN_sub'<=`dfcheck_n1'+`p'+1+`qrot') {
local rot_fewobs "T"
di as text in gr "Warning: Too small effective sample size for bin selection."
}
}
if ("`rot_fewobs'"!="T"&`J_rot_reg'==.) {
* Power series
local series_rot ""
forvalues i=1/`=`p'+`qrot'' {
tempvar z_var_`i'
qui gen `z_var_`i''=`z_var'^`i'
local series_rot `series_rot' `z_var_`i''
}
* Variance Component
if ("`absorb'"=="") capture reg `y_var' `series_rot' `w_var' `wt'
else capture reghdfe `y_var' `series_rot' `w_var' `wt', absorb(`absorb') resid(`resid1')
if (_rc==0) {
mat `coef'=e(b)
mat `coef'=`coef'[1,`=`p'+1'..`=`p'+`qrot'']
}
else {
error _rc
exit _rc
}
tempvar pred_y y_var_2 pred_y2 s2
if ("`absorb'"=="") predict `pred_y', xb
else predict `pred_y', xbd
qui gen `y_var_2'=`y_var'^2 // move it outside
if ("`absorb'"=="") {
capture reg `y_var_2' `series_rot' `w_var' `wt'
if (_rc) {
error _rc
exit _rc
}
predict `pred_y2', xb
}
else {
capture reghdfe `y_var_2' `series_rot' `w_var' `wt', absorb(`absorb') resid(`resid2')
if (_rc) {
error _rc
exit _rc
}
predict `pred_y2', xbd
}
qui gen `s2'=`pred_y2'-`pred_y'^2 /* sigma^2(x) var */
* Normal density
if ("`rotnorm'"=="") {
if ("`wtype'"!="p") qui sum `z_var' `wt'
else qui sum `z_var' [aw`exp']
local zbar=r(mean)
local zsd=r(sd)
tempvar fz
* trim density from below
local cutval=normalden(invnormal(`den_alp')*`zsd', 0, `zsd')
qui gen `fz'=max(normalden(`z_var', `zbar', `zsd'), `cutval')
if ("`binspos'"=="ES") qui replace `s2'=`s2'/`fz'
else qui replace `s2'=`s2'*(`fz'^(2*`deriv'))
}
if ("`wt'"!="") qui sum `s2' [aw`exp'], meanonly
else qui sum `s2', meanonly
local sig2=r(mean)
mata: imse_v_cons(`p', `deriv', "`vcons'")
local imse_v=`sig2'*`vcons' /* variance constant */
* Bias component
* gen data for derivative
tempvar pred_deriv
mata: `Xm'=J(rows(`zvec'),0,.)
forval i=`=`p'+1'/`=`p'+`qrot'' {
mata:`Xm'=(`Xm',`zvec':^(`i'-`p'-1)*factorial(`i')/factorial(`i'-`p'-1))
}
mata: `Xm'=`Xm'*st_matrix("`coef'")'; ///
st_store(.,st_addvar("float", "`pred_deriv'"), `Xm':^2)
mata: mata drop `Xm'
if ("`rotnorm'"=="") {
if ("`binspos'"=="QS") {
qui replace `pred_deriv'=`pred_deriv'/(`fz'^(2*`p'+2-2*`deriv'))
}
}
if ("`wt'"!="") qui sum `pred_deriv' [aw`exp'], meanonly
else qui sum `pred_deriv', meanonly
local mean_deriv=r(mean)
mata: imse_b_cons(`p', `deriv', "`bcons'")
local imse_b=`mean_deriv'*`bcons' /* bias constant */
* ROT J
local J_rot_unreg=ceil((`imse_b'*2*(`p'+1-`deriv')/ ///
(`imse_v'*(1+2*`deriv')))^(1/(2*`p'+2+1))* ///
`eN_sub'^(1/(2*`p'+2+1)))
local J_rot_reg=max(`J_rot_unreg', ///
ceil((2*(`p'+1-`deriv')/(1+2*`deriv')*`rot_lb'*`eN_sub')^(1/(2*`p'+2+1))))
local imse_bsq_rot=`imse_b'
local imse_var_rot=`imse_v'
mat `mat_imse_bsq_rot'[`num',1]=`imse_bsq_rot'
mat `mat_imse_var_rot'[`num',1]=`imse_var_rot'
}
** Repeated knots? ***************
local J_rot_uniq=`J_rot_reg'
if (("`binsmethod'"=="DPI"|"`localcheck'"=="T")&"`masspoints'"!="veryfew") {
tempvar zcat
qui gen `zcat'=. in 1
* Prepare bins
tempname kmat
if "`binspos'"=="ES" {
local stepsize=1/`J_rot_reg'
forvalues i=1/`=`J_rot_reg'+1' {
mat `kmat'=(nullmat(`kmat') \ `=0+`stepsize'*(`i'-1)')
}
}
else {
if (`J_rot_reg'==1) {
mat `kmat'=(0 \ 1)
}
else {
binsreg_pctile `z_var' `wt', nq(`J_rot_reg') `usegtools'
mat `kmat'=(0 \ r(Q) \ 1)
}
}
mata: st_matrix("`kmat'", (0 \ uniqrows(st_matrix("`kmat'")[|2 \ `=`J_rot_reg'+1'|])))
local J_rot_uniq=rowsof(`kmat')-1
if ("`binsmethod'"=="DPI"&"`dpi_fewobs'"=="") {
binsreg_irecode `z_var', knotmat(`kmat') bin(`zcat') ///
`usegtools' nbins(`J_rot_uniq') pos(`binspos') knotliston(T)
}
}
*********************************
********** DPI Choice ***********
*********************************
local J_dpi=.
* Check if DPI can be implemented
if ("`J_rot_uniq'"!="."&"`binsmethod'"=="DPI"&"`masspoints'"!="veryfew") {
* Compare with degree of freedom
if ((`p'-`s'+1)*(`J_rot_uniq'-1)+`p'+2+`dfcheck_n2'>=`eN_sub') {
di as text in gr "Warning: Too small effective sample size for DPI selection."
local dpi_fewobs "T"
}
* Check local effective size
if ("`localcheck'"=="T"&"`dpi_fewobs'"!="T") {
mata: st_local("Ncat", strofreal(rows(uniqrows(st_data(.,"`zcat'")))))
if (`J_rot_uniq'==`Ncat') {
mata: `binedges'=binsreg_uniq(`zvec', st_data(.,"`zcat'"), `J_rot_uniq', "uniqmin")
mata: mata drop `binedges'
}
else {
local uniqmin=0
di as text in gr "Warning: There are empty bins."
}
if (`uniqmin'<`p'+1) {
local dpi_fewobs "T"
di as text in gr "Warning: Some bins have too few distinct x-values for DPI selection."
}
}
}
else local dpi_fewobs "T"
if ("`binsmethod'"=="DPI"&"`dpi_fewobs'"!="T") {
* Update vce condition
if ("`massadj'"=="T") {
if ("`absorb'"=="") {
if ("`clusterON'"=="") {
local vce "vce(cluster `z_var')"
}
else {
if (`Nclust_sub'>`Ndist_sub') {
local vce "vce(cluster `z_var')"
di as text in gr "Warning: # of mass points < # of clusters. vce option overridden."
}
}
}
else {
if ("`clustervar'"=="") local vce "vce(cluster `z_var')"
}
}
**************************************
* Start computation
tempvar derivfit derivse biasterm biasterm_v projbias
qui gen `derivfit'=. in 1
qui gen `derivse'=. in 1
qui gen `biasterm'=. in 1 /* save bias */
if (`deriv'>0) qui gen `biasterm_v'=. in 1 /* error of approx deriv */
qui gen `projbias'=. in 1 /* save proj of bias */
**************************************
* predict leading bias
mata: bias("`z_var'", "`zcat'", "`kmat'", `p', 0, "`biasterm'")
if (`deriv'>0) {
mata: bias("`z_var'", "`zcat'", "`kmat'", `p', `deriv', "`biasterm_v'")
}
* Increase order from p to p+1
* Expand basis
local nseries=(`p'-`s'+1)*(`J_rot_uniq'-1)+`p'+2
local series ""
forvalues i=1/`nseries' {
tempvar sp`i'
local series `series' `sp`i''
qui gen `sp`i''=. in 1
}
mata: binsreg_st_spdes(`zvec', "`series'", "`kmat'", st_data(.,"`zcat'"), `=`p'+1', 0, `=`s'+1')
if ("`absorb'"=="") capture reg `y_var' `series' `w_var' `wt', nocon
else capture reghdfe `y_var' `series' `w_var' `wt', absorb(`absorb') `reghdfeopt'
* store results
tempname temp_b temp_V
if (_rc==0) {
matrix `temp_b'=e(b)
matrix `temp_V'=e(V)
}
else {
error _rc
exit _rc
}
* Predict (p+1)th derivative
mata: `Xm'=binsreg_spdes(`zvec', "`kmat'", st_data(.,"`zcat'"), `=`p'+1', `=`p'+1', `=`s'+1'); ///
st_store(.,"`derivfit'", (binsreg_pred(`Xm', (st_matrix("`temp_b'")[|1 \ `nseries'|])', ///
st_matrix("`temp_V'")[|1,1 \ `nseries',`nseries'|], "xb"))[,1])
mata: mata drop `Xm'
qui replace `biasterm'=`derivfit'*`biasterm'
if (`deriv'>0) qui replace `biasterm_v'=`derivfit'*`biasterm_v'
drop `series'
* Then get back degree-p spline, run OLS
local nseries=(`p'-`s'+1)*(`J_rot_uniq'-1)+`p'+1
local series ""
forvalues i=1/`nseries' {
tempvar sp`i'
local series `series' `sp`i''
qui gen `sp`i''=. in 1
}
mata: binsreg_st_spdes(`zvec', "`series'", "`kmat'", st_data(.,"`zcat'"), `p', 0, `s')
capture reg `biasterm' `series' `wt', nocon /* project bias on X of degree p */
tempname bias_b bias_V
if (_rc==0) {
matrix `bias_b'=e(b)
matrix `bias_V'=e(V)
}
else {
error _rc
exit _rc
}
mata: `Xm'=binsreg_spdes(`zvec', "`kmat'", st_data(.,"`zcat'"), `p', `deriv', `s'); ///
st_store(.,"`projbias'", binsreg_pred(`Xm', st_matrix("`bias_b'")', st_matrix("`bias_V'"), "xb")[,1])
if (`deriv'==0) {
qui replace `biasterm'=(`biasterm'-`projbias')^2
}
else {
qui replace `biasterm'=(`biasterm_v'-`projbias')^2 /* still save in biasterm if deriv>0 */
}
if ("`wt'"!="") qui sum `biasterm' [aw`exp'], meanonly
else qui sum `biasterm', meanonly
local m_bias=r(mean)
local imse_b=`m_bias'*`J_rot_uniq'^(2*(`p'+1-`deriv'))
* for variance purpose
if ("`absorb'"=="") capture reg `y_var' `series' `w_var' `wt', nocon `vce'
else capture reghdfe `y_var' `series' `w_var' `wt', absorb(`absorb') `vce' `reghdfeopt'
* store results
if (_rc==0) {
matrix `temp_b'=e(b)
matrix `temp_V'=e(V)
tempname vcov
mata: `vcov'=st_matrix("`temp_V'")
if ("`absorb'"=="") mata: `vcov'=`vcov'[|1,1 \ `nseries',`nseries'|]
else {
mata: `vcov'=(`vcov'[|1,1 \ `nseries', `nseries'|], `vcov'[|1,cols(`vcov') \ `nseries', cols(`vcov')|] \ ///
`vcov'[|cols(`vcov'), 1 \ cols(`vcov'), `nseries'|], `vcov'[cols(`vcov'), cols(`vcov')]); ///
`Xm'=(`Xm', J(rows(`Xm'),1,1))
}
}
else {
error _rc
exit _rc
}
mata: st_store(., "`derivse'", (binsreg_pred(`Xm', ., `vcov', "se")[,2]):^2)
mata: mata drop `vcov'
if ("`wt'"!="") qui sum `derivse' [aw`exp'], meanonly
else qui sum `derivse', meanonly
local m_se=r(mean)
local imse_v=`m_se'/(`J_rot_uniq'^(1+2*`deriv'))
mata: mata drop `Xm'
* DPI J
local J_dpi=ceil((`imse_b'*2*(`p'+1-`deriv')/ ///
(`imse_v'*(1+2*`deriv')))^(1/(2*`p'+2+1)))
local imse_bsq_dpi=`imse_b'
local imse_var_dpi=`imse_v'*`eN_sub'
mat `mat_imse_bsq_dpi'[`num',1]=`imse_bsq_dpi'
mat `mat_imse_var_dpi'[`num',1]=`imse_var_dpi'
}
local J_dpi_uniq=`J_dpi'
************************************************
* update J if useeffn or subsample specified
if ("`useeffn'"!=""|"`randcut'"!="") {
if ("`useeffn'"!="") local scaling=(`useeffn'/`eN')^(1/(2*`p'+2+1))
if ("`randcut'"!="") local scaling=(`eN'/`eN_sub')^(1/(2*`p'+2+1))
if (`J_rot_unreg'!=.) local J_rot_unreg=ceil(`J_rot_unreg'*`scaling')
if (`J_rot_reg'!=.) local J_rot_reg=ceil(`J_rot_reg'*`scaling')
if (`J_rot_uniq'!=.) local J_rot_uniq=ceil(`J_rot_uniq'*`scaling')
if (`J_dpi'!=.) local J_dpi=ceil(`J_dpi'*`scaling')
if (`J_dpi_uniq'!=.) local J_dpi_uniq=ceil(`J_dpi_uniq'*`scaling')
}
mat `mat_J_rot_unreg'[`num',1]=`J_rot_unreg'
mat `mat_J_rot_reg'[`num',1]=`J_rot_reg'
mat `mat_J_rot_uniq'[`num',1]=`J_rot_uniq'
mat `mat_J_dpi'[`num',1]=`J_dpi'
mat `mat_J_dpi_uniq'[`num',1]=`J_dpi_uniq'
}
****** END loop *******
tempname ord_rot_unreg ord_rot_reg ord_rot_uniq ord_dpi ord_dpi_uniq ///
ind_rot_unreg ind_rot_reg ind_rot_uniq ind_dpi ind_dpi_uniq
local imse_var_dpi_upd=.
local imse_bsq_dpi_upd=.
if ("`selectJ'"=="F") {
* output a row vector of p and s
foreach name in "rot_unreg" "rot_reg" "rot_uniq" "dpi" "dpi_uniq" {
mata: findmindex("`mat_J_`name''", "`ind_`name''", `nbins', `ncom')
mat `ord_`name''=`m_deg'[`ind_`name'',1..2]
local J_`name'=`nbins'
}
if (`nbins'!=`=`mat_J_dpi'[`ind_dpi',1]') {
qui bins_imse `y_var' `z_var' `w_var' `wt', deriv(`deriv') ///
p(`=`ord_dpi'[1,1]') s(`=`ord_dpi'[1,2]') nbins(`J_dpi') eN_sub(`eN_sub') ///
binspos(`binspos') `vce' `usegtools' ///
zvec(`zvec') absorb(`absorb') reghdfeopt(`reghdfeopt')
local imse_var_dpi_upd=e(imse_var)
local imse_bsq_dpi_upd=e(imse_bsq)
}
}
else {
if (`len_nbins'>1) {
tempname m_nbins
forval i=1/`len_nbins' {
local el: word `i' of `nbins'
mat `m_nbins'=(nullmat(`m_nbins') \ `el')
}
* output a scalar
foreach name in "rot_unreg" "rot_reg" "rot_uniq" "dpi" "dpi_uniq" {
mata: findmindex("`m_nbins'", "`ind_`name''", `=`mat_J_`name''[1,1]', `len_nbins')
local J_`name'=`m_nbins'[`ind_`name'',1]
}
}
foreach name in "rot_unreg" "rot_reg" "rot_uniq" "dpi" "dpi_uniq" {
mat `ord_`name''=`m_deg'[1,1..2]
}
}
mata: mata drop `zvec'
* Reconstruct knot list
tempname xkmat
if ("`binsmethod'"=="ROT"&"`rot_fewobs'"!="T") {
local Jselected=`J_rot_uniq'
local pselected=`ord_rot_uniq'[1,1]
local sselected=`ord_rot_uniq'[1,2]
}
else if ("`binsmethod'"=="DPI"&"`dpi_fewobs'"!="T") {
local Jselected=`J_dpi'
local pselected=`ord_dpi'[1,1]
local sselected=`ord_dpi'[1,2]
}
else {
local Jselectfail "T"
}
if ("`Jselectfail'"!="T"&"`useeffn'"=="") {
if ("`binspos'"=="ES") {
local stepsize=1/`Jselected'
forvalues i=1/`=`Jselected'+1' {
mat `xkmat'=(nullmat(`xkmat') \ `=`xmin'+`stepsize'*(`i'-1)')
}
}
else {
if (`Jselected'>1) {
if ("`randcut'"!="") {
qui set obs `samplesize'
mata: st_store(., "`x_var'", `xvec')
mata: mata drop `xvec'
}
binsreg_pctile `x_var' `wt', nq(`Jselected')
mat `xkmat'=(`xmin'\ r(Q) \ `xmax')
}
else mat `xkmat'=(`xmin' \ `xmax')
}
* Renew if needed
mata: st_matrix("`xkmat'", (`xmin' \ uniqrows(st_matrix("`xkmat'")[|2 \ `=`Jselected'+1'|])))
if (`Jselected'!=rowsof(`xkmat')-1) {
local Jselected=rowsof(`xkmat')-1
}
if ("`binsmethod'"=="DPI") {
local J_dpi_uniq=`Jselected'
}
}
else mat `xkmat'=.
if ("`binsmethod'"=="DPI") local method "IMSE-optimal plug-in choice"
else local method "IMSE-optimal rule-of-thumb choice"
if ("`selectJ'"=="T") local method "`method' (select # of bins)"
else local method "`method' (select degree and smoothness)"
if ("`binspos'"=="ES") {
local placement "Evenly-spaced"
}
else {
local placement "Quantile-spaced"
}
* Save data?
if (`"`savegrid'"'!=`""') {
if ("`Jselectfail'"!="T"&"`useeffn'"=="") {
clear
local obs=`simsgrid'*`Jselected'+`Jselected'-1
qui set obs `obs'
qui gen `x_varname'=. in 1
label var `x_varname' "Eval. point"
qui gen binsreg_isknot=. in 1
label var binsreg_isknot "Is the eval. point an inner knot"
qui gen binsreg_bin=. in 1
label var binsreg_bin "indicator of bin"
mata: st_store(., (1,2,3), binsreg_grids("`xkmat'", `simsgrid'))
foreach var of local w_varname {
qui gen `var'=0
}
qui save `"`savegrid'"', `replace'
}
else {
di as text in gr "Warning: Grid not saved. Selection fails or useeffn() is specified."
}
}
* Display
di ""
di in smcl in gr "Bin selection for binscatter estimates"
di in smcl in gr "Method: `method'"
di in smcl in gr "Position: `placement'"
if (`"`savegrid'"'!=`""') {
di in smcl in gr `"Output file: `savegrid'.dta"'
}
di ""
di in smcl in gr "{hline 28}{c TT}{hline 10}"
di in smcl in gr "{ralign 27:# of observations}" _col(28) " {c |} " _col(30) as result %7.0f `N'
di in smcl in gr "{ralign 27:# of distince values}" _col(28) " {c |} " _col(30) as result %7.0f `Ndist'
di in smcl in gr "{ralign 27:# of clusters}" _col(28) " {c |} " _col(30) as result %7.0f `Nclust'
if ("`useeffn'"=="") {
di in smcl in gr "{ralign 27:eff. sample size}" _col(28) " {c |} " _col(30) as result %7.0f `eN'
}
else {
di in smcl in gr "{ralign 27:eff. sample size}" _col(28) " {c |} " _col(30) as result %7.0f `useeffn'
}
foreach name in "rot_unreg" "rot_reg" "rot_uniq" "dpi" "dpi_uniq" {
local df_`name'=.
if (`J_`name''!=.) local df_`name'=(`ord_`name''[1,1]-`ord_`name''[1,2]+1)*(`J_`name''-1)+`ord_`name''[1,1]+1
}
if ("`selectJ'"=="F") {
local imse_bsq_rot=`mat_imse_bsq_rot'[`ind_rot_unreg',1]
local imse_var_rot=`mat_imse_var_rot'[`ind_rot_unreg',1]
if ("`imse_var_dpi_upd'"!=".") {
local imse_bsq_dpi=`imse_bsq_dpi_upd'
local imse_var_dpi=`imse_var_dpi_upd'
}
else {
local imse_bsq_dpi=`mat_imse_bsq_dpi'[`ind_dpi',1]
local imse_var_dpi=`mat_imse_var_dpi'[`ind_dpi',1]
}
di in smcl in gr "{hline 28}{c +}{hline 10}"
di in smcl in gr "{ralign 27:# of bins}" _col(28) " {c |} " _col(30) as result %7.0f `nbins'
di in smcl in gr "{hline 28}{c BT}{hline 10}"
di ""
di in smcl in gr "{hline 14}{c TT}{hline 8}{c TT}{hline 7}{c TT}{hline 7}{c TT}{hline 15}{c TT}{hline 14}"
di in smcl in gr "{rcenter 13: method}" _col(13) " {c |} " "{center 7: p}" ///
_col(22) "{c |}" "{rcenter 7: s}" ///
_col(31) "{c |}" "{center 7: df}" ///
_col(40) "{c |}" "{center 14: imse, bias^2}" ///
_col(56) "{c |}" "{center 14: imse, var.}"
di in smcl in gr "{hline 14}{c +}{hline 8}{c +}{hline 7}{c +}{hline 7}{c +}{hline 15}{c +}{hline 14}"
di in smcl in gr "{rcenter 13: ROT-POLY}" _col(13) " {c |} " as result %4.0f `ord_rot_unreg'[1,1] ///
_col(23) in gr " {c |} " as result %4.0f `ord_rot_unreg'[1,2] ///
_col(32) in gr "{c |}" as result %5.0f `df_rot_unreg' ///
_col(40) in gr "{c |} " as result %7.3f `imse_bsq_rot' ///
_col(56) in gr "{c |} " as result %7.3f `imse_var_rot'
di in smcl in gr "{rcenter 13: ROT-REGUL}" _col(13) " {c |} " as result %4.0f `ord_rot_reg'[1,1] ///
_col(23) in gr " {c |} " as result %4.0f `ord_rot_reg'[1,2] ///
_col(32) in gr "{c |}" as result %5.0f `df_rot_reg' ///
_col(40) in gr "{c |} " as result %7.3f . ///
_col(56) in gr "{c |} " as result %7.3f .
di in smcl in gr "{rcenter 13: ROT-UKNOT}" _col(13) " {c |} " as result %4.0f `ord_rot_uniq'[1,1] ///
_col(23) in gr " {c |} " as result %4.0f `ord_rot_uniq'[1,2] ///
_col(32) in gr "{c |}" as result %5.0f `df_rot_uniq' ///
_col(40) in gr "{c |} " as result %7.3f . ///
_col(56) in gr "{c |} " as result %7.3f .
di in smcl in gr "{rcenter 13: DPI}" _col(13) " {c |} " as result %4.0f `ord_dpi'[1,1] ///
_col(23) in gr " {c |} " as result %4.0f `ord_dpi'[1,2] ///
_col(32) in gr "{c |}" as result %5.0f `df_dpi' ///
_col(40) in gr "{c |} " as result %7.3f `imse_bsq_dpi' ///
_col(56) in gr "{c |} " as result %7.3f `imse_var_dpi'
di in smcl in gr "{rcenter 13: DPI-UKNOT}" _col(13) " {c |} " as result %4.0f `ord_dpi_uniq'[1,1] ///
_col(23) in gr " {c |} " as result %4.0f `ord_dpi_uniq'[1,2] ///
_col(32) in gr "{c |}" as result %5.0f `df_dpi_uniq' ///
_col(40) in gr "{c |} " as result %7.3f . ///
_col(56) in gr "{c |} " as result %7.3f .
di in smcl in gr "{hline 14}{c BT}{hline 8}{c BT}{hline 7}{c +}{hline 7}{c BT}{hline 15}{c BT}{hline 14}"
di in smcl in gr "p: degree of polynomial. s: # of smoothness constraints. df: degrees of freedom."
}
else {
local imse_bsq_rot=`mat_imse_bsq_rot'[1,1]
local imse_var_rot=`mat_imse_var_rot'[1,1]
local imse_bsq_dpi=`mat_imse_bsq_dpi'[1,1]
local imse_var_dpi=`mat_imse_var_dpi'[1,1]
di in smcl in gr "{hline 28}{c +}{hline 10}"
di in smcl in gr "{ralign 27:Degree of polynomial}" _col(28) " {c |} " _col(30) as result %7.0f `m_deg'[1,1]
di in smcl in gr "{ralign 27:# of smoothness constraint}" _col(28) " {c |} " _col(30) as result %7.0f `m_deg'[1,2]
di in smcl in gr "{hline 28}{c BT}{hline 10}"
di ""
di in smcl in gr "{hline 14}{c TT}{hline 12}{c TT}{hline 10}{c TT}{hline 14}{c TT}{hline 14}"
di in smcl in gr "{rcenter 13: method}" _col(13) " {c |} " "{center 11: # of bins}" _col(26) "{c |}" "{rcenter 10: df}" _col(39) "{c |}" "{center 14: imse, bias^2}" _col(54) "{c |}" "{center 14: imse, var.}"
di in smcl in gr "{hline 14}{c +}{hline 12}{c +}{hline 10}{c +}{hline 14}{c +}{hline 14}"
di in smcl in gr "{rcenter 13: ROT-POLY}" _col(13) " {c |} " as result %7.0f `J_rot_unreg' _col(28) in gr "{c |}" as result %7.0f `df_rot_unreg' ///
_col(39) in gr "{c |} " as result %7.3f `imse_bsq_rot' _col(54) in gr "{c |} " as result %7.3f `imse_var_rot'
di in smcl in gr "{rcenter 13: ROT-REGUL}" _col(13) " {c |} " as result %7.0f `J_rot_reg' _col(28) in gr "{c |}" as result %7.0f `df_rot_reg' ///
_col(39) in gr "{c |} " as result %7.3f . _col(54) in gr "{c |} " as result %7.3f .
di in smcl in gr "{rcenter 13: ROT-UKNOT}" _col(13) " {c |} " as result %7.0f `J_rot_uniq' _col(28) in gr "{c |}" as result %7.0f `df_rot_uniq' ///
_col(39) in gr "{c |} " as result %7.3f . _col(54) in gr "{c |} " as result %7.3f .
di in smcl in gr "{rcenter 13: DPI}" _col(13) " {c |} " as result %7.0f `J_dpi' _col(28) in gr "{c |}" as result %7.0f `df_dpi' ///
_col(39) in gr "{c |} " as result %7.3f `imse_bsq_dpi' _col(54) in gr "{c |} " as result %7.3f `imse_var_dpi'
di in smcl in gr "{rcenter 13: DPI-UKNOT}" _col(13) " {c |} " as result %7.0f `J_dpi_uniq' _col(28) in gr "{c |}" as result %7.0f `df_dpi_uniq' ///
_col(39) in gr "{c |} " as result %7.3f . _col(54) in gr "{c |} " as result %7.3f .
di in smcl in gr "{hline 14}{c BT}{hline 12}{c BT}{hline 10}{c BT}{hline 14}{c BT}{hline 14}"
di in smcl in gr "df: degrees of freedom."
}
* return
* notes: J_rot_uniq is obtained possibly based on the subsample; J_dpi_uniq is ALWAYS obtained based on the full sample
ereturn clear
ereturn scalar N=`N'
ereturn scalar Ndist=`Ndist'
ereturn scalar Nclust=`Nclust'
ereturn scalar deriv=`deriv'
ereturn scalar imse_bsq_rot=`imse_bsq_rot'
ereturn scalar imse_var_rot=`imse_var_rot'
ereturn scalar imse_bsq_dpi=`imse_bsq_dpi'
ereturn scalar imse_var_dpi=`imse_var_dpi'
ereturn scalar nbinsrot_poly=`J_rot_unreg'
ereturn scalar nbinsrot_regul=`J_rot_reg'
ereturn scalar nbinsrot_uknot=`J_rot_uniq'
ereturn scalar nbinsdpi=`J_dpi'
ereturn scalar nbinsdpi_uknot=`J_dpi_uniq'
ereturn scalar prot_poly=`ord_rot_unreg'[1,1]
ereturn scalar prot_regul=`ord_rot_reg'[1,1]
ereturn scalar prot_uknot=`ord_rot_uniq'[1,1]
ereturn scalar pdpi=`ord_dpi'[1,1]
ereturn scalar pdpi_uknot=`ord_dpi_uniq'[1,1]
ereturn scalar srot_poly=`ord_rot_unreg'[1,2]
ereturn scalar srot_regul=`ord_rot_reg'[1,2]
ereturn scalar srot_uknot=`ord_rot_uniq'[1,2]
ereturn scalar sdpi=`ord_dpi'[1,2]
ereturn scalar sdpi_uknot=`ord_dpi_uniq'[1,2]
ereturn matrix knot=`xkmat'
tempname m_p m_s
mat `m_p'=`m_deg'[1..`ncom',1]
mat `m_s'=`m_deg'[1..`ncom',2]
ereturn matrix m_p=`m_p'
ereturn matrix m_s=`m_s'
ereturn matrix m_nbinsrot_poly=`mat_J_rot_unreg'
ereturn matrix m_nbinsrot_regul=`mat_J_rot_reg'
ereturn matrix m_nbinsrot_uknot=`mat_J_rot_uniq'
ereturn matrix m_nbinsdpi=`mat_J_dpi'
ereturn matrix m_nbinsdpi_uknot=`mat_J_dpi_uniq'
ereturn matrix m_imse_bsq_dpi=`mat_imse_bsq_dpi'
ereturn matrix m_imse_var_dpi=`mat_imse_var_dpi'
ereturn matrix m_imse_bsq_rot=`mat_imse_bsq_rot'
ereturn matrix m_imse_var_rot=`mat_imse_var_rot'
end
* Helper command
program define bins_imse, eclass
version 13
syntax varlist(min=2 numeric ts fv) [if] [in] [fw aw pw] [, deriv(integer 0) ///
p(integer 0) s(integer 0) nbins(integer 0) eN_sub(integer 0) ///
binspos(string) vce(passthru) usegtools ///
zvec(name) absorb(string asis) reghdfeopt(string asis)]
preserve
marksample touse
qui keep if `touse'
if ("`weight'"!="") local wt [`weight'`exp']
tokenize `varlist'
local y_var `1'
local z_var `2'
macro shift 2
local w_var "`*'"
tempvar zcat
qui gen `zcat'=. in 1
* Prepare bins
tempname kmat
if "`binspos'"=="ES" {
local stepsize=1/`nbins'
forvalues i=1/`=`nbins'+1' {
mat `kmat'=(nullmat(`kmat') \ `=0+`stepsize'*(`i'-1)')
}
}
else {
if (`nbins'==1) {
mat `kmat'=(0 \ 1)
}
else {
binsreg_pctile `z_var' `wt', nq(`nbins') `usegtools'
mat `kmat'=(0 \ r(Q) \ 1)
}
}
binsreg_irecode `z_var', knotmat(`kmat') bin(`zcat') ///
`usegtools' nbins(`nbins') pos(`binspos') knotliston(T)
* Start computation
tempvar derivfit derivse biasterm biasterm_v projbias
qui gen `derivfit'=. in 1
qui gen `derivse'=. in 1
qui gen `biasterm'=. in 1 /* save bias */
if (`deriv'>0) qui gen `biasterm_v'=. in 1 /* error of approx deriv */
qui gen `projbias'=. in 1 /* save proj of bias */
**************************************
* predict leading bias
mata: bias("`z_var'", "`zcat'", "`kmat'", `p', 0, "`biasterm'")
if (`deriv'>0) {
mata: bias("`z_var'", "`zcat'", "`kmat'", `p', `deriv', "`biasterm_v'")
}
* Increase order from p to p+1
* Expand basis
local nseries=(`p'-`s'+1)*(`nbins'-1)+`p'+2
local series ""
forvalues i=1/`nseries' {
tempvar sp`i'
local series `series' `sp`i''
qui gen `sp`i''=. in 1
}
mata: binsreg_st_spdes(`zvec', "`series'", "`kmat'", st_data(.,"`zcat'"), `=`p'+1', 0, `=`s'+1')
if ("`absorb'"=="") capture reg `y_var' `series' `w_var' `wt', nocon
else capture reghdfe `y_var' `series' `w_var' `wt', absorb(`absorb') `reghdfeopt'
* store results
tempname temp_b temp_V
if (_rc==0) {
matrix `temp_b'=e(b)
matrix `temp_V'=e(V)
}
else {
error _rc
exit _rc
}
tempname Xm
* Predict (p+1)th derivative
mata: `Xm'=binsreg_spdes(`zvec', "`kmat'", st_data(.,"`zcat'"), `=`p'+1', `=`p'+1', `=`s'+1'); ///
st_store(.,"`derivfit'", (binsreg_pred(`Xm', (st_matrix("`temp_b'")[|1 \ `nseries'|])', ///
st_matrix("`temp_V'")[|1,1 \ `nseries',`nseries'|], "xb"))[,1])
mata: mata drop `Xm'
qui replace `biasterm'=`derivfit'*`biasterm'
if (`deriv'>0) qui replace `biasterm_v'=`derivfit'*`biasterm_v'
drop `series'
* Then get back degree-p spline, run OLS
local nseries=(`p'-`s'+1)*(`nbins'-1)+`p'+1
local series ""
forvalues i=1/`nseries' {
tempvar sp`i'
local series `series' `sp`i''
qui gen `sp`i''=. in 1
}
mata: binsreg_st_spdes(`zvec', "`series'", "`kmat'", st_data(.,"`zcat'"), `p', 0, `s')
capture reg `biasterm' `series' `wt', nocon /* project bias on X of degree p */
tempname bias_b bias_V
if (_rc==0) {
matrix `bias_b'=e(b)
matrix `bias_V'=e(V)
}
else {
error _rc
exit _rc
}
mata: `Xm'=binsreg_spdes(`zvec', "`kmat'", st_data(.,"`zcat'"), `p', `deriv', `s'); ///
st_store(.,"`projbias'", binsreg_pred(`Xm', st_matrix("`bias_b'")', st_matrix("`bias_V'"), "xb")[,1])
if (`deriv'==0) {
qui replace `biasterm'=(`biasterm'-`projbias')^2
}
else {
qui replace `biasterm'=(`biasterm_v'-`projbias')^2 /* still save in biasterm if deriv>0 */
}
if ("`wt'"!="") qui sum `biasterm' [aw`exp'], meanonly
else qui sum `biasterm', meanonly
local m_bias=r(mean)
local imse_b=`m_bias'*`nbins'^(2*(`p'+1-`deriv'))
* for variance purpose
if ("`absorb'"=="") capture reg `y_var' `series' `w_var' `wt', nocon `vce'
else capture reghdfe `y_var' `series' `w_var' `wt', absorb(`absorb') `vce' `reghdfeopt'
* store results
if (_rc==0) {
matrix `temp_b'=e(b)
matrix `temp_V'=e(V)
tempname vcov
mata: `vcov'=st_matrix("`temp_V'")
if ("`absorb'"=="") mata: `vcov'=`vcov'[|1,1 \ `nseries',`nseries'|]
else {
mata: `vcov'=(`vcov'[|1,1 \ `nseries', `nseries'|], `vcov'[|1,cols(`vcov') \ `nseries', cols(`vcov')|] \ ///
`vcov'[|cols(`vcov'), 1 \ cols(`vcov'), `nseries'|], `vcov'[cols(`vcov'), cols(`vcov')]); ///
`Xm'=(`Xm', J(rows(`Xm'),1,1))
}
}
else {
error _rc
exit _rc
}
mata: st_store(., "`derivse'", (binsreg_pred(`Xm', ., `vcov', "se")[,2]):^2)
mata: mata drop `vcov'
if ("`wt'"!="") qui sum `derivse' [aw`exp'], meanonly
else qui sum `derivse', meanonly
local m_se=r(mean)
local imse_v=`m_se'/(`nbins'^(1+2*`deriv'))
mata: mata drop `Xm'
* DPI J
local J_dpi=ceil((`imse_b'*2*(`p'+1-`deriv')/ ///
(`imse_v'*(1+2*`deriv')))^(1/(2*`p'+2+1)))
local imse_bsq_dpi=`imse_b'
local imse_var_dpi=`imse_v'*`eN_sub'
ereturn clear
ereturn scalar imse_bsq=`imse_bsq_dpi'
ereturn scalar imse_var=`imse_var_dpi'
end
version 13
mata:
// Constant in variance
void imse_v_cons(real scalar degree, real scalar deriv, string scalar vcons)
{
real scalar v_cons, m
real matrix V, Vderiv
m=degree+1
if (deriv==0) {
v_cons=m
}
else {
V=J(m, m, .)
Vderiv=J(m, m, 0)
for (i=1; i<=m; i++){
for (j=1; j<=i; j++) {
V[i,j]=1/(i+j-1)
if (i>deriv & j>deriv) {
Vderiv[i,j]=1/(i+j-1-2*deriv)* /*
*/ (factorial(i-1)/factorial(i-1-deriv))* /*
*/ (factorial(j-1)/factorial(j-1-deriv))
}
}
}
V=makesymmetric(V)
Vderiv=makesymmetric(Vderiv)
v_cons=trace(invsym(V)*Vderiv)
}
// return results
st_numscalar(vcons,v_cons)
}
// Constant in bias
void imse_b_cons(real scalar degree, real scalar deriv, string scalar bcons, | real scalar s)
{
real scalar b_cons, m, bernum
m=degree+1
if (args()<4) {
b_cons=1/(2*(m-deriv)+1)/factorial(m-deriv)^2/comb(2*(m-deriv), m-deriv)^2
}
else {
if (degree==0) {
bernum=1/6
}
else if (degree==1) {
bernum=1/30
}
else if (degree==2) {
bernum=1/42
}
else if (degree==3) {
bernum=1/30
}
else if (degree==4) {
bernum=5/66
}
else if (degree==5) {
bernum=691/2730
}
else if (degree==6) {
bernum=7/6
}
else {
_error("p>6 not allowed.")
}
b_cons=1/factorial(2*(m-deriv))*bernum
}
// return results
st_numscalar(bcons, b_cons)
}
// Bernoulli polynomial
real vector bernpoly(real vector x, real scalar degree)
{
n=rows(x)
if (degree==0) {
bernx=J(n,1,1)
}
else if (degree==1) {
bernx=x:-0.5
}
else if (degree==2) {
bernx=x:^2-x:+1/6
}
else if (degree==3) {
bernx=x:^3-1.5*x:^2+0.5*x
}
else if (degree==4) {
bernx=x:^4-2*x:^3+x:^2:-1/30
}
else if (degree==5) {
bernx=x:^5-2.5*x:^4+5/3*x:^3-1/6*x
}
else if (degree==6) {
bernx=x:^6-3*x:^5+2.5*x:^4-0.5*x:^2:+1/42
}
else {
_error("p is too large.")
}
return(bernx)
}
// Leading bias for splines
void bias(string scalar Var, string scalar Xcat, string scalar knotname, ///
real scalar degree, real scalar deriv, ///
string scalar biasname, | string scalar select)
{
if (args()<7) {
X=st_data(., (Var))
xcat=st_data(., (Xcat))
st_view(bias=., ., (biasname))
}
else {
X=st_data(., (Var), select)
xcat=st_data(., (Xcat), select)
st_view(bias=.,.,(biasname), select)
}
knot=st_matrix(knotname)
h=knot[|2 \ length(knot)|]-knot[|1 \ (length(knot)-1)|]
h=h[xcat]
if (rows(h)==1) {
h=h'
}
tl=knot[|1 \ (length(knot)-1)|]
tl=tl[xcat]
if (rows(tl)==1) {
tl=tl'
}
bern=bernpoly((X-tl):/h, degree+1-deriv)/factorial(degree+1-deriv):*(h:^(degree+1-deriv))
bias[.,.]=bern
}
// find the minimum
void findmindex(string scalar matname, string scalar outname, ///
real scalar J, real scalar nr)
{
real matrix A
A=sort((abs(st_matrix(matname):-J), (1::nr)), 1)
st_numscalar(outname, A[1,2])
}
end