{smcl}
{hline}
{hi:help xtcse2}{right: v. 1.01 - 15. July 2019}
{hline}
{title:Title}

{p 4 4}{cmd:xtcse2} - estimating the exponent of cross-sectional dependence in large panels.{p_end}

{title:Syntax}

{p 4 13}{cmd:xtcse2} [{varlist}] [if] [{cmd:,}
{cmd:pca(integer)}
{cmdab:stand:ardize}
{cmd:nocd}]

{p 4 4}Data has to be {cmd:xtset} before using {cmd:xtcse2}; see {help tsset}.
{it:varlist} may contain time-series operators, see {help tsvarlist}. 
If {it:varlist} if left empty, {cmd:xtcse2} predicts residuals from the
last estimation command,
see {help predict}.
{cmd:xtcse2} restricts the panel to the same number of time series per cross-sectional 
unit if the panel is unbalanced.{p_end}

{title:Contents}

{p 4}{help xtcse2##description:Description}{p_end}
{p 4}{help xtcse2##options:Options}{p_end}
{p 4}{help xtcse2##model:Econometric Model and Estimation}{p_end}
{p 4}{help xtcse2##saved_vales:Saved Values}{p_end}
{p 4}{help xtcse2##examples:Examples}{p_end}
{p 4}{help xtcse2##references:References}{p_end}
{p 4}{help xtcse2##about:About}{p_end}

{marker description}{title:Description}

{p 4 4}{cmd:xtcse2} estimates the exponent of cross-sectional dependence in a panel
with a large number of observations over time (T) and cross-sectional units (N).
The estimation method follows Bailey, Kapetanios, Pesaran (2016) (henceforth BKP).{break}
A variable or a residual is cross-sectional dependent if it inhibits an
across cross-sectional units common factor.{p_end}

{p 4 4}{cmd:xtcse2} estimates the strength of the factor, for a residual or 
one or more variables. 
It outputs a standard error and confidence interval in the usual estimation output fashion,
however it does not show a t or z statistic and p-value. Generally speaking strong cross-sectional 
dependence occurs if alpha is above 0.5. 
Testing this is done by a separate test of weak cross-sectional dependence.
Therefore a confidence interval is more informative when estimating alpha.{p_end}

{p 4 4}{cmd:xtcse2} is intend to support the decision whether to include cross-sectional averages when using
{help xtdcce2} and accompanies {help xtcd2} in testing for weak cross-sectional dependence.
As a default it uses {help xtcd2} to test for weak cross-sectional dependence.
For a discussion of {cmd:xtdcce2} and {cmd:xtcd2} see Ditzen (2018,2019).{p_end}

{p 4 4}If the panel is unbalanced or observations are missing for a specific cross-section 
unit-time combination, then the sample is restricted to the union of all time periods across
cross-sectional units. 
For unbalanced panels with many missings or a variable with many missings, many
observations might be lost.{p_end}

{marker options}{title:Options}

{p 4 8}{cmd:pca(integer)} sets the number of principle components for the 
calculation of {it:cn}. Default is to use the first 4 components.{p_end}

{p 4 8}{cmdab:stand:ardize} standardizes variables.{p_end}

{p 4 8}{cmd:nocd} suppresses test for cross-sectional dependence using {help xtcd2}.{p_end}

{marker model}{title:Econometric Model and Estimation of the Exponent}

{ul:Econometric Model}

{p 4 4}For the following assume a general factor model with {it:m} factors:{p_end}

{col 10} x(i,t) = sum(j=1,m) b(j,i) f(j,t) + u(i,t)
{col 10} i = 1,...,N and t = 1,...,T

{p 4 4}where {it:x(i,t)} depends on unobserved {it:m} common factors f(j,t) with loading
{it:b(j,i)} and a cross sectionally independent error term {it:u(i,t)}. 
The time dimension (T) and the number of cross-sectional units (N) increases to infinity;
(N,T) -> infinity.{p_end}

{p 4 4}Chudik et al (2011) specify the factors as weak or strong using a constant {it:0<=alpha<=1} such that: 
{p_end}

{col 10}lim N^(-alpha) sum(j=1,m) abs(b(j,i)) = K < infinity.

{p 4 4}The type of dependence of the factors and thus the series then depends on 
the characteristics of {it:b(j,i)}:{p_end}

{col 12}alpha {col 40} dependence
{col 12}{hline 40}
{col 12}alpha = 0 {col 40} weak
{col 12}0 < alpha < 0.5 {col 40} semi weak
{col 12}0.5 <= alpha < 1 {col 40} semi strong
{col 12}alpha = 1 {col 40} strong

{p 4 4}Weak cross-sectional dependence can be thought of as the following: Even if
the number of cross-sectional units increases to infinity, the sum of the effect of the
common factors on the dependent variable remain constant. In the case of strong
cross-sectional dependence, the sum of the effect of the common factors becomes
stronger with an increase in the number of cross-sectional units.{p_end}

{p 4 4}In an estimation ignoring (semi-) strong dependence in the dependent or 
independent variables can cause an omitted
variable bias and therefore lead to inconsistent estimates. 
Pesaran (2015) proposes a test to test for weak cross-sectional dependence,
see {help xtcd2}. 
Pesaran (2006) and Chudik, Peasaran (2015) develop a method to estimate models
with cross-sectional dependence by adding time averages of the dependent and 
independent variables (cross-sectional averages). 
This estimator is implemented in Stata by {help xtdcce2}.{p_end}

{p 4 4}{cmd:xtcse2} estimates {it:alpha} in the equation above.
An {it:alpha} above 0.5 implies strong cross-sectional dependence 
and the appropriate when using a variable is required.{p_end}

{ul:Exponent Estimation (alpha)}

{p 4 4}Bailey, Kapetanios and Pesaran (2016) [BKP] propose a method 
for the estimation of the exponent. This section summarizes their approach,
a careful reading of the assumptions and theorems is strongly encouraged.{p_end}

{p 4 4}BKP derive a bias-adjusted estimator for {it:alpha} in a panel with
{it:N_g} cross-sectional units (see Eq. 13):{p_end}

{col 10} alpha = 1 + 1/2 ln(sigma_x^2)/ln(N_g) - 1/2 ln(mu^2)/ln(N_g) - 1/2 cn / [N_g * ln(N_g) * sigma_x^2]  

{p 4 4}where {it:sigma_x^2} is the variance of the cross-sectional averages. 
{it:mu^2} is average variance of significant regression coefficients of {it:x(i,t)} on
standardized cross-sectional averages. 
{it:cn} is the variance of scaled errors from a regression of the {it:x(i,t)} on 
its first {it:K(PC)} principle components. 
The number of principle components can be set using the option 
{cmd:pca(integer)}. The default is to use the first 4 principle components.{p_end}

{p 4 4}{cmd:xtcse2} outputs a standard error for alpha and a confidence interval in the 
usual Stata estimation fashion. 
A t- or z-test statistic with p-value is however omitted, because the test is done by the
test for weak cross-sectional dependence (CD-test), see {help xtcd2}. 
{cmd:xtcse2} automatically calculates the CD-test statistic and posts its results.
For the estimation of {it:alpha} a confidence interval is therefore more informative.{p_end}

{p 4 4}The calculation of the standard error of alpha follows the equation B47, Section VI of 
the online appendix of BKP, available 
{browse "https://onlinelibrary.wiley.com/doi/abs/10.1002/jae.2476":here}:{p_end}

{col 10} sigma(alpha) = [1/T V(q) + 4/N^(alpha) S]^(1/2) * 1/2 * 1/ln(N)

{p 4 4}{it:V(q)} is the regression standard error over the square of the sum of 
q coefficients of an AR(q) process of the square of the deviation of standardized 
cross-sectional averages. q is the third root of T. {it:S} is the squared sum divided by 
N^(alpha-1) of OLS coefficients of x(it) on standardized cross-sectional averages 
sorted according to their absolute value.{p_end} 

{marker saved_vales}{title:Saved Values}

{cmd:xtcse2} stores the following in {cmd:r()}:

{col 4} Matrices
{col 8}{cmd: r(alpha)}{col 27} matrix with estimated alphas
{col 8}{cmd: r(alphaSE)}{col 27} matrix with estimated standard errors of alphas
{col 8}{cmd: r(alphas)}{col 27} matrix with estimated alpha tilde, alpha hat and alpha
{col 8}{cmd: r(N_g)}{col 27} matrix with number of cross-sectional units
{col 8}{cmd: r(T)}{col 27} matrix with number of time periods
{col 8}{cmd: r(CD)}{col 27} matrix with values of CD test statistic (if requested)
{col 8}{cmd: r(CDp)}{col 27} matrix values of p value of CD test statistic (if requested)

{marker examples}{title:Examples}

{p 4 4}An example dataset of the Penn World Tables 8 is available for download {browse "https://www.dropbox.com/s/0087vh8brhid5ws/xtdcce2_sample_dataset.dta?dl=0":here}.
The dataset contains yearly observations from 1960 until 2007 and is already tsset.
To estimate a growth equation the following variables are used:
log_rgdpo (real GDP), log_hc (human capital), log_ck (physical capital) and log_ngd (population growth + break even investments of 5%).{p_end}

{p 4 4}Before running the growth regression the exponent of the cross-sectional 
dependence for the variables is estimated:{p_end}

{p 8}{stata xtcse2 d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd}.{p_end}

{p 4 4}All variables are highly cross-sectional dependent with alphas close or even 
above 1. Therefore an 
estimation method taking cross-sectional dependence is required. 
{help xtdcce2} is uses such an estimation method by adding cross-sectional averages 
to the model. After running {cmd:xtdcce2} it is possible to use {cmd:xtcse2} to estimate
the strength of the exponent of the residual.{p_end}

{p 8}{stata xtdcce2135 log_rgdpo L.log_rgdpo log_ck log_ngd  log_hc , cr(log_rgdpo log_ck log_ngd  log_hc)  }.{p_end}
{p 8}{stata xtcse2}{p_end}

{p 4 4}{cmd:xtcse2} automatically predicts the residuals using {help predict} 
({help xtdcce2#postestimation:predict after xtdcce2}). 
The CD statistic is still in a rejection region, therefore the residuals 
exhibit strong cross-sectional dependence. 
This is confirmed by the confidence interval of {it:alpha} which just overlaps
with 0.5.{p_end}

{p 4 4}The estimated model above is mis-specified as it is a dynamic model, but no lags
of the cross-sectional averages are added. The number of lags should be in the
region of T^(1/3), so with 47 periods 3 lags are added. Then {cmd:xtcse2} is used 
to estimate alpha again, this time the CD test is omitted:{p_end}

{p 8}{stata xtdcce2135 log_rgdpo L.log_rgdpo log_ck log_ngd  log_hc , cr(log_rgdpo log_ck log_ngd  log_hc) cr_lags(3) }.{p_end}
{p 8}{stata xtcse2,nocd}{p_end}

{p 4 4}The value of the CD test statistic is 1.32 and in a non-rejection region.
The estimate of {it:alpha} is considerably small the confidence interval does not 
overlap with 0.5{p_end}

{p 4 4}As a second exercise the first row of Table 1. in BKP is reproduced. 
The data is available on Pesaran's 
{browse "http://www.econ.cam.ac.uk/people/emeritus/mhp1/published-articles#2016":webpage}
and for download {browse "http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp15/BKP_GAUSS_procedures.zip":here}.{p_end}

{p 4 4}After the data is loaded, reshaped (it comes in a matrix) and renamed as variable gdp,
the option {cmd:standardize} is used to standardize the variable as done in BKP:{p_end}

{p 8}{stata xtcse2 gdp , standardize}.{p_end}
 
{marker references}{title:References}

{p 4 8}Bailey, N., G. Kapetanios and M. H. Pesaran. 2016.
Exponent of cross-sectional dependence: estimation and inference.
Journal of Applied Econometrics 31: 929-960.{p_end}

{p 4 8}Chudik, A., M. H. Pesaran and E. Tosetti. 2011. 
Weak and strong cross-section dependence and estimation of large panels.
The Econometrics Journal 14(1):C45–C90.{p_end}

{p 4 8}Chudik, A., and M. H. Pesaran. 2015.
Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors.
Journal of Econometrics 188(2): 393-420.{p_end}

{p 4 8}Ditzen, J. 2018. Estimating Dynamic Common Correlated Effcts in Stata. The Stata Journal, 18:3, 585 - 617.{p_end}

{p 4 8}Ditzen, J. 2019. Estimating long run effects in models with cross-sectional dependence using xtdcce2. 
CEERP Working Paper Series: 7.{p_end}

{p 4 8}Pesaran, M. H. 2006.
Estimation and inference in large heterogeneous panels with a multifactor error structure.
Econometrica 74(4): 967-1012.{p_end}

{p 4 8}Pesaran, M. H. 2015. 
Testing Weak Cross-Sectional Dependence in Large Panels. 
Econometric Reviews 34(6-10):1089–1117.{p_end}

{marker about}{title:Author}

{p 4}Jan Ditzen (Heriot-Watt University){p_end}
{p 4}Email: {browse "mailto:[email protected]":[email protected]}{p_end}
{p 4}Web: {browse "www.jan.ditzen.net":www.jan.ditzen.net}{p_end}

{p 4 8}I am grateful to Sean Holly for the suggestion to implement the test 
to {help xtdcce2} and {help xtcd2}.
Part of the code is taken from BKP and transferred from Gauss to Stata.
The original Gauss code is available onPesaran's 
{browse "http://www.econ.cam.ac.uk/people/emeritus/mhp1/published-articles#2016":webpage}
and for download {browse "http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp15/BKP_GAUSS_procedures.zip":here}.
 All remaining errors are my own.{p_end}

{p 4 4}In the fashion of {cmd:xtdcce2} and {cmd:xtcd2} {cmd:xtcse2} has a {cmd:2} as a suffix.
There is no such program called {help xtcsee}.{p_end}

{p 4 8}Please cite as follows:{break}
Ditzen, J. 2019. xtcse2: Estimating Exponent of Cross-Sectional Dependence in large panels.
{p_end}

{p 4 8}The latest versions can be obtained as a part of {cmd:xtdcce2} via {stata "net from https://github.com/JanDitzen/xtdcce2"} 
.{p_end}

{marker ChangLog}{title:Changelog}
{p 4 8}This version: 1.0 - 13. July 2019{p_end}

{title:Also see}
{p 4 4}See also: {help xtdcce2}, {help xtcd2}{p_end}