{smcl}
{* First Version July 23 2020}{...}
{* This Version Nov 11 2022}{...}
{viewerdialog xtselvar "dialog xtselvar"}{...}
{vieweralsosee "[XT] xtselmod" "help xtselmod"}{...}
{vieweralsosee "[XT] xtoos_t" "help xtoos_t"}{...}
{vieweralsosee "[XT] xtoos_i" "help xtoos_i"}{...}
{vieweralsosee "[XT] xtoos_bin_t" "help xtoos_bin_t"}{...}
{vieweralsosee "[XT] xtoos_bin_i" "help xtoos_bin_i"}{...}
{hline}
Help for {hi:xtselvar}
{hline}
{title:Description}
{p}{cmd:xtselvar} helps us to select the best predictor between a number of alternative explanatory variables ({it:candidates}). The procedure estimates the same defined specification {it:n} times,
keeping constant the same dependent variable and an optional list of control variables. However at each repetition the procedure includes only one of the {it:n} candidate variables in the specification,
({it:in addition to the list of fixed control variables}) until each one of the candidate variables listed by the user in the syntax has been included and evaluated.
For each candidate variable, the procedure estimates a set of parameters and statistical criteria.{p_end}
{p}More specifically, it estimates seven statistics per variable (Coefficient, t-statistic, Adj. R2, AIC, BIC, U-Theil in time-series, U-Theil in cross-individual).
The U-Theil statistic is the ratio between the RMSE of the respective candidate variable versus the RMSE of a "naive" prediction/model, See help {help xtoos_t} and help {help xtoos_i}.
It then ranks each variable according to the latter five statistical criteria and generates one ranking per each one of them. It also computes a composite ranking summarizing all five criteria.
It finally sorts all candidate variables according to the selected ranking, which by default is the composite ranking.
{p}{cmd:xtselvar} allows to choose weights for each one of the five criteria used to compute the composite ranking. It also allows to rank the variables according to a specific criterion of preference.
For instance, if the primary objective of the estimation is to obtain the most accurate prediction of the dependent variable, the user could choose to rank the candidate variables according only to their forecasting ability,
i.e. according to the estimated U-Theil in its time-series dimension.{p_end}
{p}The out-of-sample evaluation of each candidate variable performed by {cmd:xtselvar} is based on the commands {cmd:xtoos_t} and {cmd:xtoos_i} which need to be installed in Stata to be able to execute the procedure.{p_end}
{p}{cmd:xtselvar} allows to choose different estimation methods including some dynamic methodologies and could also be used in a dataset with only time-series observations.
In the case the specification includes lags of the dependent variable, the procedure is able to automatically generate dynamic forecasts for the out-of-sample evaluation performance.{p_end}
{p}In the case of the out-of-sample evaluation in a time-series dimension, {cmd:xtselvar} allows to choose a specific horizon {it:h} at which to evaluate the forecasting performance
of the model including the candidate variable. It also allows to estimate the forecasting performance from horizon {it:t+1} until {it:t+h}.{p_end}
{p}{cmd:xtselvar} saves and displays the results of the analysis in different ways. If the user chooses to display the results of each estimation for each variable
it could also create a log file that saves all the results. Otherwise the user can choose to execute the command quietly and then the procedure just displays a final summary
through a table that shows all the five statistics estimated for each specification, the ranking of each specification according to each criterion, and the composite ranking.
{p}The table of results is displayed ordered by the criterion selected by the user. The user can also choose to save the final results in an excel file.{p_end}
{p}The command {cmd:xtselvar} needs the command {cmd:matsort} in order to be able to sort the candidate variables according to the specified criterion.
Therefore, {cmd:matsort} also needs to be installed in Stata.{p_end}
{title:Use of PCA to construct composite control variables}
{p}{cmd:xtselvar} allows to generate a number of principal components (through PCA) for one or more groups of variables (topics) so that these components
can be used as fixed control variables in each regression in order to avoid or reduce the omitted variables problem.
{p}This option could be specially useful in the case that there is a too large number of possible control variables that cannot be included altogether in the regression.
Given that testing all possible combinations of all the variables might be unfeasible, it could be convenient, for instance, to group them
into a smaller set of principal components that act as uncorrelated control variables.
{p}Similarly, it could also be useful to perform an initial selection of variables when all of the possible predictors could be classified within smaller groups of similar/related variables.
In such case the user can create one or more components from each thematic group of variables. Then, one could be able to select the best predictors from within each group,
while using as control variables the components from the rest of groups.
{p}This strategy might help us to avoid the bias from omitting the control variables from all other groups different than the group in which the selection is being made.
The main components of each group are correlated with all the variables from each topic, and therefore, they are useful as a proxies for all of them, reducing the effect of their omission.{p_end}
{title:Proceedings USA Stata Conference 2020}
https://www.stata.com/meeting/us20/slides/us20_Ugarte-Ruiz.pdf
{title:Syntax}
{p 4 8 2}Note: {cmd:xtselvar} requires package {cmd:xtoos} to be installed. See help {help xtoos_t}, help {help xtoos_i}, or click on {stata "ssc install xtoos"} to install.{p_end}
{p 4 8 2}Note: {cmd:xtselvar} requires package {cmd:matsort} to be installed. See help {help matsort} or click on {stata "ssc install matsort"} to install.{p_end}
{cmd:xtselvar} {depvar} [{indepvars}] [{it:if}] [{it:in}], {opt *ind:ate(string)} {opt *cd:ate(string)} {opt *k:smpl(integer)}
[{opt fix:ed(varlist)}] [{opt m:et(string)}] [{opt mc:omp(string)}] [{opt ev:alopt(varname)}]
[{opt fe}] [{opt xbu}] [{opt dum}] [{opt opar}] [{opt lags(numlist)}] [{opt qui}] [{opt log(string)}] [{opt exc(string)}] [{opt she:et(string)}]
[{opt ord(string)}] [{opt down}] [{opt wei:ghts(numlist)}] [{opt hor(integer)}] [{opt uph:or}] [{opt groups(integer)}] [{opt ncomp(integer)}]
[{opt pca1(varlist)}] [{opt pca2(varlist)}] ... [{opt pca10(varlist)}]
[{it:model_options}]
{synoptset}{...}
{marker Options}{...}
{synopthdr:Options}
{synoptline}
{synopt:{opt *ind:ate()}}It is always required. It specifies the last date (time-period) included in the estimation sample in the first step of the recursive procedure,
i.e. it defines the date at which the performace evaluation starts. It allows Yearly, Quarterly and Monthly dates, or integer numbers, only. Quarterly and Monthly dates should be written as 1990q1 or 1990m1.{p_end}
{synopt:{opt *cd:ate()}}It is always required. It specifies the date at which the evaluation performance ends, i.e. the current date or the date at which the sample ends.
It allows Yearly, Quarterly and Monthly dates, or integer numbers, only. Quarterly and Monthly dates should be written as 1990q1 or 1990m1.{p_end}
{synopt:{opt *k:smpl()}}It is always required. It specifies the size {it:k} of each partition or (non-random) subsample, i.e. the number of individuals
in each partition that is excluded from each (in)sample estimation, and for which the out-of-sample prediction performance is evaluated in the cross-individuals dimension.
The size {it:k} automatically determines the number of subsamples.{p_end}
{synopt:{opt fix:ed()}}Specifies a list of variables that will remain fixed in the specification.{p_end}
{synopt:{opt m:et()}}Specifies the estimation method. The default is {cmd:xtreg} when using panel data and {cmd:reg} when using time-series data.{p_end}
{synopt:{opt mc:omp()}}Specifies the estimation method for the AR1 model used as a comparison and for estimating the U-Theil, when an AR1 is preferred to a "naive" prediction.
The AR1 could be estimated by dynamic panel methods such as {cmd:xtabond} or {cmd:xtdpdsys}, or simply by {cmd:xtreg} or {cmd:reg}.{p_end}
{synopt:{opt ev:alopt()}}Specifies a particular individual or group of individuals for which the prediction performance is evaluated.
The particular individual or group must be defined by a dummy variable equal to one for those individuals.{p_end}
{synopt:{opt fe}}Estimates using Fixed-Effects (within) estimator.{p_end}
{synopt:{opt xbu}}Specifies that the prediction including the fixed or random component should be used.{p_end}
{synopt:{opt dum}}Estimates including one dummy variable per individual in the specification and in the prediction.{p_end}
{synopt:{opt opar}}Specifies that the contribution of the dummy variables per individual should not be included in the prediction.
It can only be used together with the option {opt dum}.{p_end}
{synopt:{opt lags()}}Specifies the number of lags of the dependent variable that must be included the specification, the default is zero lags.
This option should be used when using a dynamic panel methods such as {cmd:xtabond} or {cmd:xtdpdsys}. If other estimation method is being used,
the command {cmd:xtoos_t} also allows to write down the desired lagged dependent variables terms simply as other explanatory variables. {p_end}
{synopt:{opt qui}}Specifies that the output of each regression should not be displayed and should be estimated quietly instead.
When this option is activated only the final table with the summary of all the regression's outputs will be displayed.{p_end}
{synopt:{opt log()}}Specifies a name for a log file in which the output of every regression will be saved.{p_end}
{synopt:{opt exc()}}Specifies a name for an Excel file in which the final table with the summary of all the regression's outputs will be saved. When this
option is activated a name for the sheet must also be provided.{p_end}
{synopt:{opt she:et()}}Specifies the name of the working sheet of Excel in which the final table with the summary of all the regression's outputs will be saved.
This option is compulsory when the option {opt exc} is activated.{p_end}
{synopt:{opt ord()}}Specifies the statistical criterion according to which the summary of results must be ordered. The five criterion that are estimated are
(in parenthesis the name that must be introduced in this option): Adjusted R-squared (R2_ad),
Akaike (AIC), Bayesian Information Criteria (BIC), U-Theil in time-series dimension (Uth_TS) and U-Theil in cross-section dimension (Uth_CS).{p_end}
{synopt:{opt down}}Specifies if the results should be ordered descendingly. If this option is not activated the results will be ordered
ascendingly according to the specified statistical criterion.{p_end}
{synopt:{opt wei:ghts()}}Specifies weights for each one of the five statistical criteria used to construct the final ranking. The specified weights
must be numbers between zero and one, and the sum of the five weights must add up to one.{p_end}
{synopt:{opt h:or()}}Defines a exact forecasting horizon at which the out-of-sample exercise in its time-series dimension must be evaluated.{p_end}
{synopt:{opt uph:or()}}Specifies that the evaluation of the model in its time-series dimension must be evaluated exactly between horizon {it:t+1} and
the forecasting horizon specified in the option {opt h:or()}.{p_end}
{synopt:{opt gr:oups()}}Specifies the number of groups of variables for which a composite component will be generated. The procedures allows a maximum number of 10 groups.{p_end}
{synopt:{opt pca#()}}Specifies the variables that compose the group number #. The procedure allows a maximum number of 10 groups.
If the option {opt groups} is set equal to five, then you must also write down five lists specifying the five different groups of variables, e.g.: pca1(var1 var2) pca2(var3 var4)
pca3(var5 var6) pca4(var7 var8) pca5(var9 var10).{p_end}
{synopt:{opt ncomp()}}Specifies the number of principal components that will be generated for each group of variables.
The default is one component per group, and the maximum number of components is equal to the number of variables inside each group.{p_end}
{synopt:{it:model_options}}Specifies any other estimation options specific to the method used and not defined elsewhere.{p_end}
{synoptline}
{marker Examples}{...}
{title:Example 1. Basics}
Use of {cmd:xtselvar} to rank 21 different variables, x1 and z1_1, z1_2,...z1_20.
The dates at which the time-series out-of-sample evaluation starts and end must be specified ({opt ind:ate()} and {opt end:ate()}),
the same as the number of individuals left-out at each partition in the cross-section out-of-sample evaluation {opt k:sample()}
(For further details, see help for command {cmd:xtoos}):
{p 4 8 2}{cmd:. sysuse panelexample, clear}{p_end}
{p 4 8 2}{cmd:. xtset id t}{p_end}
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100)}{p_end}
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100)} qui{p_end}
{title:Example 2. Fixed Control Variables}
If we want to keep constant in the specification the variables x2, x3, x4 and x5, we should used the option {opt fix:ed()}:
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100) fixed(x2 x3 x4 x5)}{p_end}
{title:Example 3. Saving results in a log file}
If we want to show each variable results and saving them in a log file named "results", we should use the option {opt log()}:
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100) log(results)}{p_end}
{title:Example 4. Saving results in an excel file}
If we do not want to show each variable results, and we want to save the summary table in an excel file named "results" and a worksheet named "results1",
we should use the options {opt qui} and {opt exc()} together with the option {opt she:et()}.
Options {opt exc()} and {opt she:et()} must be used together:
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100) qui exc(results) sheet(results1)}{p_end}
{title:Example 5. Changing weights of the total ranking}
If we want to give null weights to the criteria adjusted R2, AIC and BIC, and equal weights to the criteria U-Theils in time-series and cross-section
dimensions, we should use the option {opt wei:ghts()}. The given weights should be between 0 and 1:
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100) wei(0 0 0 0.5 0.5) }{p_end}
{title:Example 6. Changing the ranking order}
If we want to order the final summary according to the R-squared in a descending order, we should use the options {opt ord()} and {opt down}:.
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100) ord(R2_ad) down}{p_end}
{title:Example 7. Defining a specific prediction horizon for the performance evaluation}
If we want to specify an exact horizon at which the time-series out-of-sample performance should be evaluated, we should use the option {opt hor()}:
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100) hor(3)}{p_end}
{title:Example 8. Defining an interval for the horizon}
If instead of an exact horizon, we want to evaluate the time-series out-of-sample performance between horizons 1 and 3, we should used options
{opt h:or()} and {opt uph:or} together.
{p 4 8 2}{cmd:. xtselvar y x1 z1_1-z1_20, indate(2015) cdate(2020) ksmpl(100) hor(3) uphor}{p_end}
{title:Example 9. Creating principal components as control variables from different sets of variables}
If we want to create three principal components from three group of variables with 20 variables each, e.g. groups z2 and z3: z2_1, z2_2...z2_20
and z3_1, z3_2...z3_20, we should use the options {opt groups()} and options {opt pca1()}...{opt pca3()}.
The option {opt groups()} defines how many groups of variables are and thus how many principal components should be estimated
and included in the specification. The options {opt pca1()}... to {opt pca#groups()} should list the variables within each group.
There should be as many lists as groups of variables.
{p 4 8 2}{cmd:. xtselvar y x4 z4*, indate(2015) cdate(2020) ksmpl(100) groups(3) pca1(z1*) pca2(z2*) pca3(z3*)}{p_end}
{title:Example 10. Creating principal components as control variables from one set of variables}
We can also generate various principal components from just one large group of variables, for instance if we do not have
an a priori classifications of the predictors.
In such case we can create, for example, 6 components from all possible variables, i.e all variables whose name starts with z,
using also the option {opt ncomp()}.
Additionally, we specify only one group in option {opt groups()} and list all variables z* in the option {opt pca1()}:
{p 4 8 2}{cmd:. xtselvar y x4 z4*, ind(2015) cd(2020) o(100) k(100) r(0) qui groups(1) pca1(z*) ncomp(6)}{p_end}
{marker results}{...}
{title:Stored results}
{pstd}
{cmd:xtselvar} stores the following in {cmd:r()}:
{synoptset 18 tabbed}{...}
{p2col 5 18 22 2: Matrices}{p_end}
{synopt:{cmd:r(summary)}} Summary of results showing all the seven statistics estimated for
each candidate variable, the ranking of each variable according to each criterion, and the composite ranking.
The matrix is ranked according to the composite ranking or the criterion chosen by the user{p_end}
{title:Author}
Alfonso Ugarte-Ruiz
alfonso.ugarte@bbva.com
{title:References}
. Paul Millar, 2005. "MATSORT: Stata module to sort a matrix by a given column,"
Statistical Software Components S449504, Boston College Department of Economics, revised 28 Jan 2009.
. Alfonso Ugarte-Ruiz, 2019. "XTOOS: Stata module for evaluating the out-of-sample prediction performance of panel-data models,"
Statistical Software Components S458710, Boston College Department of Economics, revised 09 Jun 2020.