{smcl} {hline} {cmd:help: {helpb xtregdhp}}{space 50} {cmd:dialog:} {bf:{dialog xtregdhp}} {hline} {bf:{err:{dlgtab:Title}}} {bf:xtregdhp: Han-Philips (2010) Linear Dynamic Panel Data Regression"} {marker 00}{bf:{err:{dlgtab:Table of Contents}}} {p 4 8 2} {p 5}{helpb xtregdhp##01:Syntax}{p_end} {p 5}{helpb xtregdhp##02:Description}{p_end} {p 5}{helpb xtregdhp##03:Options}{p_end} {p 5}{helpb xtregdhp##04:Model Selection Diagnostic Criteria}{p_end} {p 5}{helpb xtregdhp##05:Heteroscedasticity Tests}{p_end} {p 5}{helpb xtregdhp##06:Saved Results}{p_end} {p 5}{helpb xtregdhp##07:References}{p_end} {p 1}*** {helpb xtregdhp##08:Examples}{p_end} {p 5}{helpb xtregdhp##09:Author}{p_end} {p2colreset}{...} {marker 01}{bf:{err:{dlgtab:Syntax}}} {p 5 5 6} {opt xtregdhp} {depvar} {indepvars} {ifin} , {bf:{err:id(var)}} {bf:{err:it(var)}}{p_end} {p 3 5 6} {err: [} {opt lmh:et} {opt diag be fe re} {opt mfx(lin|log)} {opt pred:ict(new_var)} {opt res:id(new_var)}{p_end} {p 4 5 6} {opt iter(#)} {opt nocons:tant} {opt coll tolog} {opt l:evel(#)} {err:]}{p_end} {p2colreset}{...} {marker 02}{bf:{err:{dlgtab:Description}}} {p 2 2 2} {cmd:xtregdhp} estimates Han-Philips (2010) Linear Dynamic Panel Data Regression{p_end} {p 2 2 2} with be, fe, re Effects{p_end} {p 3 4 2} R2, R2 Adjusted, and F-Test, are obtained from 4 ways:{p_end} {p 5 4 2} 1- (Buse 1973) R2.{p_end} {p 5 4 2} 2- Raw Moments R2.{p_end} {p 5 4 2} 3- squared correlation between predicted (Yh) and observed dependent variable (Y).{p_end} {p 5 4 2} 4- Ratio of variance between predicted (Yh) and observed dependent variable (Y).{p_end} {p 5 4 2} - Adjusted R2: R2_a=1-(1-R2)*(N-1)/(N-K-1).{p_end} {p 5 4 2} - F-Test=R2/(1-R2)*(N-K-1)/(K).{p_end} {p2colreset}{...} {marker 03}{bf:{err:{dlgtab:Options}}} {col 3}* {cmd: {opt id(var)}{col 20}Cross Sections ID variable name} {col 3}* {cmd: {opt it(var)}{col 20}Time Series ID variable name} {col 3}{opt be} Between Effects {col 33}(BE) {col 3}{opt fe} Fixed-Effects {col 33}(FE) {col 3}{opt re} Random-Effects {col 33}(RE) {col 3}{opt coll}{col 20}keep collinear variables; default is removing collinear variables. {col 3}{opt nocons:tant}{col 20}Exclude Constant Term from Equation {col 3}{opt iter(#)}{col 20}number of iterations; Default is iter(100) {col 3}{opt level(#)}{col 20}confidence intervals level. Default is level(95) {col 3}{opt mfx(lin, log)}{col 20}functional form: Linear model {cmd:(lin)}, or Log-Log model {cmd:(log)}, {col 20}to compute Marginal Effects and Elasticities - In Linear model: marginal effects are the coefficients (Bm), and elasticities are (Es = Bm X/Y). - In Log-Log model: elasticities are the coefficients (Es), and the marginal effects are (Bm = Es Y/X). - {opt mfx(log)} and {opt tolog} options must be combined, to transform linear variables to log form. {col 3}{opt tolog}{col 20}Convert dependent and independent variables {col 20}to LOG Form in the memory for Log-Log regression. {col 20}{opt tolog} Transforms {depvar} and {indepvars} {col 20}to Log Form without lost the original data variables {col 3}{opt pred:ict(new_variable)}{col 30}Predicted values variable {col 3}{opt res:id(new_variable)}{col 30}Residuals values variable {col 15} computed as Ue=Y-Yh ; that is known as combined residual: [Ue = U_i + E_it] {col 15} overall error component is computed as: [E_it] {col 15} see: {help xtreg postestimation##predict} {p2colreset}{...} {marker 04}{bf:{err:{dlgtab:Model Selection Diagnostic Criteria}}} {synopt :{opt diag} Model Selection Diagnostic Criteria:}{p_end} - Log Likelihood Function LLF - Akaike Information Criterion (1974) AIC - Akaike Information Criterion (1973) Log AIC - Schwarz Criterion (1978) SC - Schwarz Criterion (1978) Log SC - Amemiya Prediction Criterion (1969) FPE - Hannan-Quinn Criterion (1979) HQ - Rice Criterion (1984) Rice - Shibata Criterion (1981) Shibata - Craven-Wahba Generalized Cross Validation (1979) GCV {p2colreset}{...} {marker 05}{bf:{err:{dlgtab:Groupwise Panel Heteroscedasticity Tests}}} {synopt :{opt lmh:et} Groupwise Panel Heteroscedasticity Tests:}{p_end} * Ho: Panel Homoscedasticity - Ha: Panel Groupwise Heteroscedasticity - Lagrange Multiplier LM Test - Likelihood Ratio LR Test - Wald Test {p2colreset}{...} {marker 06}{bf:{err:{dlgtab:Saved Results}}} {p 2 4 2 }{cmd:xtregdhp} saves the following results in {cmd:e()}: {err:*** Model Selection Diagnostic Criteria:} {col 4}{cmd:e(N)}{col 20}number of observations {col 4}{cmd:e(r2bu)}{col 20}R-squared (Buse 1973) {col 4}{cmd:e(r2bu_a)}{col 20}R-squared Adj (Buse 1973) {col 4}{cmd:e(r2raw)}{col 20}Raw Moments R2 {col 4}{cmd:e(r2raw_a)}{col 20}Raw Moments R2 Adj {col 4}{cmd:e(f)}{col 20}F-test {col 4}{cmd:e(fp)}{col 20}F-test P-Value {col 4}{cmd:e(wald)}{col 20}Wald-test {col 4}{cmd:e(waldp)}{col 20}Wald-test P-Value {col 4}{cmd:e(r2h)}{col 20}R2 Between Predicted (Yh) and Observed DepVar (Y) {col 4}{cmd:e(r2h_a)}{col 20}Adjusted r2h {col 4}{cmd:e(fh)}{col 20}F-test due to r2h {col 4}{cmd:e(fhp)}{col 20}F-test due to r2h P-Value {col 4}{cmd:e(r2v)}{col 20}R2 Variance Ratio Between Predicted (Yh) and Observed DepVar (Y) {col 4}{cmd:e(r2v_a)}{col 20}Adjusted r2v {col 4}{cmd:e(fv)}{col 20}F-test due to r2v {col 4}{cmd:e(fvp)}{col 20}F-test due to r2v P-Value {col 4}{cmd:e(sig)}{col 20}Root MSE (Sigma) {col 4}{cmd:e(llf)}{col 20}Log Likelihood Function{col 62}LLF {col 4}{cmd:e(aic)}{col 20}Akaike Information Criterion{col 62}(1974) AIC {col 4}{cmd:e(laic)}{col 20}Akaike Information Criterion{col 62}(1973) Log AIC {col 4}{cmd:e(sc)}{col 20}Schwarz Criterion{col 62}(1978) SC {col 4}{cmd:e(lsc)}{col 20}Schwarz Criterion{col 62}(1978) Log SC {col 4}{cmd:e(fpe)}{col 20}Amemiya Prediction Criterion{col 62}(1969) FPE {col 4}{cmd:e(hq)}{col 20}Hannan-Quinn Criterion{col 62}(1979) HQ {col 4}{cmd:e(rice)}{col 20}Rice Criterion{col 62}(1984) Rice {col 4}{cmd:e(shibata)}{col 20}Shibata Criterion{col 62}(1981) Shibata {col 4}{cmd:e(gcv)}{col 20}Craven-Wahba Generalized Cross Validation (1979) GCV {err:*** Groupwise Heteroscedasticity Tests:} {col 4}{cmd:e(lmhglm)}{col 20}Lagrange Multiplier LM Test {col 4}{cmd:e(lmhglmp)}{col 20}Lagrange Multiplier LM Test P-Value {col 4}{cmd:e(lmhglr)}{col 20}Likelihood Ratio LR Test {col 4}{cmd:e(lmhglrp)}{col 20}Likelihood Ratio LR Test P-Value {col 4}{cmd:e(lmhgw)}{col 20}Wald Test {col 4}{cmd:e(lmhgwp)}{col 20}Wald Test P-Value Matrixes {col 4}{cmd:e(b)}{col 20}coefficient vector {col 4}{cmd:e(V)}{col 20}variance-covariance matrix of the estimators {col 4}{cmd:e(mfxlin)}{col 20}Marginal Effect and Elasticity in Lin Form {col 4}{cmd:e(mfxlog)}{col 20}Marginal Effect and Elasticity in Log Form {p2colreset}{...} {marker 07}{bf:{err:{dlgtab:References}}} {p 4 8 2}Breusch, Trevor & Adrian Pagan (1980) {cmd: "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics",} {it:Review of Economic Studies 47}; 239-253. {p 4 8 2}Greene, William (2007) {cmd: "Econometric Analysis",} {it:6th ed., Macmillan Publishing Company Inc., New York, USA.}. {p 4 8 2} Han, Chirok & Peter C.B. Phillips (2010) {cmd: "GMM Estimation for Dynamic Panels with Fixed Effects and Strong at Unity"} {it:Econometric Theory, 26}; 119–151. {p 4 8 2}Judge, Georege, R. Carter Hill, William . E. Griffiths, Helmut Lutkepohl, & Tsoung-Chao Lee (1988) {cmd: "Introduction To The Theory And Practice Of Econometrics",} {it:2nd ed., John Wiley & Sons, Inc., New York, USA}. {p 4 8 2}Judge, Georege, W. E. Griffiths, R. Carter Hill, Helmut Lutkepohl, & Tsoung-Chao Lee(1985) {cmd: "The Theory and Practice of Econometrics",} {it:2nd ed., John Wiley & Sons, Inc., New York, USA}. {p2colreset}{...} {marker 08}{bf:{err:{dlgtab:Examples}}} {stata clear all} {stata sysuse xtregdhp.dta, clear} {stata db xtregdhp} {stata xtset id t} {stata xtregdhp y x1 x2 , id(id) it(t) mfx(lin) diag lmh predict(Yh) resid(Eu)} {stata xtregdhp y x1 x2 , id(id) it(t) mfx(log) diag lmh predict(Yh) resid(Eu) tolog} {stata xtregdhp y x1 x2 , id(id) it(t) mfx(lin) diag lmh be} {stata xtregdhp y x1 x2 , id(id) it(t) mfx(lin) diag lmh fe} {stata xtregdhp y x1 x2 , id(id) it(t) mfx(lin) diag lmh re} {hline} . clear all . sysuse xtregdhp.dta, clear . xtregdhp y x1 x2 , id(id) it(t) mfx(lin) diag lmh re ============================================================================== * Han-Philips (2010) Linear Dynamic Panel Data Regression ============================================================================== y = x1 + x2 ------------------------------------------------------------------------------ Sample Size = 42 | Cross Sections Number = 7 Wald Test = 52.9984 | P-Value > Chi2(3) = 0.0000 F-Test = 17.6661 | P-Value > F(3 , 39) = 0.0000 (Buse 1973) R2 = 0.5761 | Raw Moments R2 = 0.9579 (Buse 1973) R2 Adj = 0.5543 | Raw Moments R2 Adj = 0.9558 Root MSE (Sigma) = 14.4009 | Log Likelihood Function = -147.0867 ------------------------------------------------------------------------------ - R2h= 0.4053 R2h Adj= 0.3748 F-Test = 8.63 P-Value > F(3 , 39) 0.0002 - R2v= 0.3197 R2v Adj= 0.2848 F-Test = 5.95 P-Value > F(3 , 39) 0.0019 ------------------------------------------------------------------------------ y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- y | L1. | -.1309041 .2819316 -0.46 0.642 -.6834798 .4216717 | x1 | -.2835875 .0871885 -3.25 0.001 -.4544739 -.1127011 x2 | -1.186583 .3002919 -3.95 0.000 -1.775144 -.5980212 _cons | 71.78851 6.02257 11.92 0.000 59.98449 83.59253 ------------------------------------------------------------------------------ ============================================================================== * Panel Model Selection Diagnostic Criteria ============================================================================== - Log Likelihood Function LLF = -147.0867 --------------------------------------------------------------------------- - Akaike Information Criterion (1974) AIC = 75.9077 - Akaike Information Criterion (1973) Log AIC = 4.3295 --------------------------------------------------------------------------- - Schwarz Criterion (1978) SC = 88.5841 - Schwarz Criterion (1978) Log SC = 4.4840 --------------------------------------------------------------------------- - Amemiya Prediction Criterion (1969) FPE = 75.1008 - Hannan-Quinn Criterion (1979) HQ = 80.4881 - Rice Criterion (1984) Rice = 77.0535 - Shibata Criterion (1981) Shibata = 74.9996 - Craven-Wahba Generalized Cross Validation (1979) GCV = 76.4447 ------------------------------------------------------------------------------ ============================================================================== * Panel Groupwise Heteroscedasticity Tests ============================================================================== Ho: Panel Homoscedasticity - Ha: Panel Groupwise Heteroscedasticity - Lagrange Multiplier LM Test = 8.4465 P-Value > Chi2(6) 0.2072 - Likelihood Ratio LR Test = 38.0427 P-Value > Chi2(6) 0.0000 - Wald Test = 14.7346 P-Value > Chi2(7) 0.0396 ------------------------------------------------------------------------------ * Marginal Effect - Elasticity: Linear * +---------------------------------------------------------------------------+ | Variable | Marginal_Effect(B) | Elasticity(Es) | Mean | |------------+--------------------+--------------------+--------------------| | L.y | -0.1309 | -0.1306 | 35.0262 | | x1 | -0.2836 | -0.3229 | 39.9877 | | x2 | -1.1866 | -0.4879 | 14.4425 | +---------------------------------------------------------------------------+ Mean of Dependent Variable = 35.1209 {p2colreset}{...} {marker 09}{bf:{err:{dlgtab:Author}}} {hi:Emad Abd Elmessih Shehata} {hi:Professor (PhD Economics)} {hi:Agricultural Research Center - Agricultural Economics Research Institute - Egypt} {hi:Email: {browse "mailto:emadstat@hotmail.com":emadstat@hotmail.com}} {hi:WebPage:{col 27}{browse "http://emadstat.110mb.com/stata.htm"}} {hi:WebPage at IDEAS:{col 27}{browse "http://ideas.repec.org/f/psh494.html"}} {hi:WebPage at EconPapers:{col 27}{browse "http://econpapers.repec.org/RAS/psh494.htm"}} {bf:{err:{dlgtab:XTREGDHP Citation}}} {p 1}{cmd:Shehata, Emad Abd Elmessih (2012)}{p_end} {p 1 10 1}{cmd:XTREGDHP: "Han-Philips (2010) Linear Dynamic Panel Data Regression"}{p_end} {browse "http://ideas.repec.org/c/boc/bocode/s457456.html"} {browse "http://econpapers.repec.org/software/bocbocode/s457456.htm"} {title:Online Help:} {bf:{err:* Econometric Regression Models:}} {bf:{err:* (1) (OLS) * Ordinary Least Squares Regression Models:}} {helpb olsreg}{col 12}OLS Econometric Ridge & Weighted Regression Models: Stata Module Toolkit {helpb ridgereg}{col 12}OLS Ridge Regression Models {helpb gmmreg}{col 12}OLS Generalized Method of Moments (GMM): Ridge & Weighted Regression {helpb chowreg}{col 12}OLS Structural Change Regressions and Chow Test --------------------------------------------------------------------------- {bf:{err:* (2) (2SLS-IV) * Two-Stage Least Squares & Instrumental Variables Regression Models:}} {helpb reg2}{col 12}2SLS-IV Econometric Ridge & Weighted Regression Models: Stata Module Toolkit {helpb gmmreg2}{col 12}2SLS-IV Generalized Method of Moments (GMM): Ridge & Weighted Regression {helpb limlreg2}{col 12}Limited-Information Maximum Likelihood (LIML) IV Regression {helpb meloreg2}{col 12}Minimum Expected Loss (MELO) IV Regression {helpb ridgereg2}{col 12}Ridge 2SLS-LIML-GMM-MELO-Fuller-kClass IV Regression {helpb ridge2sls}{col 12}Two-Stage Least Squares Ridge Regression {helpb ridgegmm}{col 12}Generalized Method of Moments (GMM) IV Ridge Regression {helpb ridgeliml}{col 12}Limited-Information Maximum Likelihood (LIML) IV Ridge Regression {helpb ridgemelo}{col 12}Minimum Expected Loss (MELO) IV Ridge Regression --------------------------------------------------------------------------- {bf:{err:* (3) * Panel Data Regression Models:}} {helpb regxt}{col 12}Panel Data Econometric Ridge & Weighted Regression Models: Stata Module Toolkit {helpb xtregdhp}{col 12}Han-Philips (2010) Linear Dynamic Panel Data Regression {helpb xtregam}{col 12}Amemiya Random-Effects Panel Data: Ridge & Weighted Regression {helpb xtregbem}{col 12}Between-Effects Panel Data: Ridge & Weighted Regression {helpb xtregbn}{col 12}Balestra-Nerlove Random-Effects Panel Data: Ridge & Weighted Regression {helpb xtregfem}{col 12}Fixed-Effects Panel Data: Ridge & Weighted Regression {helpb xtregmle}{col 12}Trevor Breusch MLE Random-Effects Panel Data: Ridge & Weighted Regression {helpb xtregrem}{col 12}Fuller-Battese GLS Random-Effects Panel Data: Ridge & Weighted Regression {helpb xtregsam}{col 12}Swamy-Arora Random-Effects Panel Data: Ridge & Weighted Regression {helpb xtregwem}{col 12}Within-Effects Panel Data: Ridge & Weighted Regression {helpb xtregwhm}{col 12}Wallace-Hussain Random-Effects Panel Data: Ridge & Weighted Regression {helpb xtreghet}{col 12}MLE Random-Effects Multiplicative Heteroscedasticity Panel Data Regression --------------------------------------------------------------------------- {bf:{err:* (4) (MLE) * Maximum Likelihood Estimation Regression Models:}} {helpb mlereg}{col 12}MLE Econometric Regression Models: Stata Module Toolkit {helpb mleregn}{col 12}MLE Normal Regression {helpb mleregln}{col 12}MLE Log Normal Regression {helpb mlereghn}{col 12}MLE Half Normal Regression {helpb mlerege}{col 12}MLE Exponential Regression {helpb mleregle}{col 12}MLE Log Exponential Regression {helpb mleregg}{col 12}MLE Gamma Regression {helpb mlereglg}{col 12}MLE Log Gamma Regression {helpb mlereggg}{col 12}MLE Generalized Gamma Regression {helpb mlereglgg}{col 12}MLE Log Generalized Gamma Regression {helpb mleregb}{col 12}MLE Beta Regression {helpb mleregev}{col 12}MLE Extreme Value Regression {helpb mleregw}{col 12}MLE Weibull Regression {helpb mlereglw}{col 12}MLE Log Weibull Regression {helpb mleregilg}{col 12}MLE Inverse Log Gauss Regression --------------------------------------------------------------------------- {bf:{err:* (5) * Autocorrelation Regression Models:}} {helpb autoreg}{col 12}Autoregressive Least Squares Regression Models: Stata Module Toolkit {helpb alsmle}{col 12}Beach-Mackinnon AR(1) Autoregressive Maximum Likelihood Estimation Regression {helpb automle}{col 12}Beach-Mackinnon AR(1) Autoregressive Maximum Likelihood Estimation Regression {helpb autopagan}{col 12}Pagan AR(p) Conditional Autoregressive Least Squares Regression {helpb autoyw}{col 12}Yule-Walker AR(p) Unconditional Autoregressive Least Squares Regression {helpb autopw}{col 12}Prais-Winsten AR(p) Autoregressive Least Squares Regression {helpb autoco}{col 12}Cochrane-Orcutt AR(p) Autoregressive Least Squares Regression {helpb autofair}{col 12}Fair AR(1) Autoregressive Least Squares Regression --------------------------------------------------------------------------- {bf:{err:* (6) * Heteroscedasticity Regression Models:}} {helpb hetdep}{col 12}MLE Dependent Variable Heteroscedasticity {helpb hetmult}{col 12}MLE Multiplicative Heteroscedasticity Regression {helpb hetstd}{col 12}MLE Standard Deviation Heteroscedasticity Regression {helpb hetvar}{col 12}MLE Variance Deviation Heteroscedasticity Regression {helpb glsreg}{col 12}Generalized Least Squares Regression --------------------------------------------------------------------------- {bf:{err:* (7) * Non Normality Regression Models:}} {helpb robgme}{col 12}MLE Robust Generalized Multivariate Error t Distribution {helpb bcchreg}{col 12}Classical Box-Cox Multiplicative Heteroscedasticity Regression {helpb bccreg}{col 12}Classical Box-Cox Regression {helpb bcereg}{col 12}Extended Box-Cox Regression --------------------------------------------------------------------------- {bf:{err:* (8) (NLS) * Nonlinear Least Squares Regression Regression Models:}} {helpb autonls}{col 12}Non Linear Autoregressive Least Squares Regression {helpb qregnls}{col 12}Non Linear Quantile Regression --------------------------------------------------------------------------- {bf:{err:* (9) * Logit Regression Models:}} {helpb logithetm}{col 12}Logit Multiplicative Heteroscedasticity Regression {helpb mnlogit}{col 12}Multinomial Logit Regression --------------------------------------------------------------------------- {bf:{err:* (10) * Probit Regression Models:}} {helpb probithetm}{col 12}Probit Multiplicative Heteroscedasticity Regression {helpb mnprobit}{col 12}Multinomial Probit Regression --------------------------------------------------------------------------- {bf:{err:* (11) * Tobit Regression Models:}} {helpb tobithetm}{col 12}Tobit Multiplicative Heteroscedasticity Regression --------------------------------------------------------------------------- {bf:{err:Panel Data Tests:}} {bf:{err:* (1) * Autocorrelation Tests:}} {helpb lmaxt}{col 12}Panel Data Autocorrelation Tests {helpb lmabxt}{col 12}Panel Data Autocorrelation Baltagi Test {helpb lmabgxt}{col 12}Panel Data Autocorrelation Breusch-Godfrey Test {helpb lmabpxt}{col 12}Panel Data Autocorrelation Box-Pierce Test {helpb lmabpgxt}{col 12}Panel Data Autocorrelation Breusch-Pagan-Godfrey Test {helpb lmadurhxt}{col 12}Panel Data Autocorrelation Dynamic Durbin h and Harvey LM Tests {helpb lmadurmxt}{col 12}Panel Data Autocorrelation Dynamic Durbin m Test {helpb lmadwxt}{col 12}Panel Data Autocorrelation Durbin-Watson Test {helpb lmavonxt}{col 12}Panel Data Von Neumann Ratio Autocorrelation Test {helpb lmawxt}{col 12}Panel Data Autocorrelation Wooldridge Test {helpb lmazxt}{col 12}Panel Data Autocorrelation Z Test --------------------------------------------------------------------------- {bf:{err:* (2) * Heteroscedasticity Tests:}} {helpb lmhxt}{col 12}Panel Data Heteroscedasticity Tests {helpb lmhgwxt}{col 12}Panel Data Groupwise Heteroscedasticity Tests {helpb ghxt}{col 12}Panel Groupwise Heteroscedasticity Tests {helpb lmhlmxt}{col 12}Panel Data Groupwise Heteroscedasticity Breusch-Pagan LM Test {helpb lmhlrxt}{col 12}Panel Data Groupwise Heteroscedasticity Greene LR Test {helpb lmharchxt}{col 12}Panel Data Heteroscedasticity Engle (ARCH) Test {helpb lmhcwxt}{col 12}Panel Data Heteroscedasticity Cook-Weisberg Test {helpb lmhglxt}{col 12}Panel Data Heteroscedasticity Glejser Test {helpb lmhharvxt}{col 12}Panel Data Heteroscedasticity Harvey Test {helpb lmhhpxt}{col 12}Panel Data Heteroscedasticity Hall-Pagan Test {helpb lmhmssxt}{col 12}Panel Data Heteroscedasticity Machado-Santos-Silva Test {helpb lmhwaldxt}{col 12}Panel Data Heteroscedasticity Wald Test {helpb lmhwhitext}{col 12}Panel Data Heteroscedasticity White Test --------------------------------------------------------------------------- {bf:{err:* (3) * Non Normality Tests:}} {helpb lmnxt}{col 12}Panel Data Non Normality Tests {helpb lmnadxt}{col 12}Panel Data Non Normality Anderson-Darling Test {helpb lmndhxt}{col 12}Panel Data Non Normality Doornik-Hansen Test {helpb lmndpxt}{col 12}Panel Data Non Normality D'Agostino-Pearson Test {helpb lmngryxt}{col 12}Panel Data Non Normality Geary Runs Test {helpb lmnjbxt}{col 12}Panel Data Non Normality Jarque-Bera Test {helpb lmnwhitext}{col 12}Panel Data Non Normality White Test --------------------------------------------------------------------------- {bf:{err:* (4) * Panel Data Error Component Tests:}} {helpb lmecxt}{col 12}Panel Data Error Component Tests --------------------------------------------------------------------------- {bf:{err:* (5) * Panel Data Diagonal Covariance Matrix Test:}} {helpb lmcovxt}{col 12}Panel Data Breusch-Pagan Diagonal Covariance Matrix LM Test --------------------------------------------------------------------------- {bf:{err:* (6) * Panel Data ModeL Selection Diagnostic Criteria:}} {helpb diagxt}{col 12}Panel Data ModeL Selection Diagnostic Criteria --------------------------------------------------------------------------- {bf:{err:* (7) * Panel Data Specification Tests:}} {helpb lmhausxt}{col 12}Panel Data Hausman Specification Test {helpb resetxt}{col 12}Panel Data REgression Specification Error Tests (RESET) --------------------------------------------------------------------------- {bf:{err:* (8) * Panel Data Identification Variables:}} {helpb idt}{col 12}Create Identification Variables in Panel Data {helpb xtidt}{col 12}Create Identification Variables in Panel Data --------------------------------------------------------------------------- {psee} {p_end}