+-------+ ----+ Title +------------------------------------------------------------
chowreg: Structural Change Regressions and Chow Test
+-------------------+ ----+ Table of Contents +------------------------------------------------
Syntax Options Description Saved Results References
*** Examples
Author
+--------+ ----+ Syntax +-----------------------------------------------------------
chowreg depvar indepvars [if] [in] [weight] , dum(#) [ type(#) noconstant vce(vcetype)]
+---------+ ----+ Options +----------------------------------------------------------
type(1, 2, 3) Functional Form Dummy Variables Type
(1) Y = X + D0 (2) Y = X + DX (3) Y = X + D0 + DX where: D0 = Dummy variable (0,1), takes (0) in first period, and (1) in second > period. DX = Cross product of each Xi times in D0
dum(#) Number of First Period Observations
noconstant Exclude Constant Term
+-------------+ ----+ Description +------------------------------------------------------
chowreg Estimates structural change regressions and compute Chow test"}
+---------------+ ----+ Saved Results +----------------------------------------------------
chowreg saves the following in r():
r(chow) Chow Test r(chowp) Chow Test P-Value r(fisher) Fisher Test r(fisherp) Fisher Test P-Value r(wald) Wald Test r(waldp) Wald Test P-Value r(lr) Likelihood Ratio Test r(lrp) Likelihood Ratio Test P-Value r(lm) Lagrange Multiplier Test r(lmp) Lagrange Multiplier Test P-Value
+------------+ ----+ References +-------------------------------------------------------
Damodar Gujarati (1995) "Basic Econometrics" 3rd Edition, McGraw Hill, New York, USA.
Greene, William (1993) "Econometric Analysis", 2nd ed., Macmillan Publishing Company Inc., New York, USA.
Greene, William (2007) "Econometric Analysis", 6th ed., Upper Saddle River, NJ: Prentice-Hall.
Maddala, G. (1992) "Introduction to Econometrics", 2nd ed., Macmillan Publishing Company, New York, USA.
+----------+ ----+ Examples +---------------------------------------------------------
clear all
sysuse chowreg.dta , clear
db chowreg
chowreg y x1 x2 , dum(9) type(1) chowreg y x1 x2 , dum(9) type(2) chowreg y x1 x2 , dum(9) type(3) -------------------------------------------------------------------------------
. clear all . sysuse chowreg.dta , clear . chowreg y x1 x2 , dum(9) type(1)
============================================================================== * Structural Change Regression * ==============================================================================
Source | SS df MS Number of obs = 17 -------------+------------------------------ F( 3, 13) = 86.07 Model | 8467.92983 3 2822.64328 Prob > F = 0.0000 Residual | 426.320328 13 32.7938714 R-squared = 0.9521 -------------+------------------------------ Adj R-squared = 0.9410 Total | 8894.25016 16 555.890635 Root MSE = 5.7266
------------------------------------------------------------------------------ y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .9479218 .3688768 2.57 0.023 .1510118 1.744832 x2 | -1.483711 .2345698 -6.33 0.000 -1.990468 -.9769534 D0 | -3.91322 8.474379 -0.46 0.652 -22.221 14.39456 _cons | 151.9528 53.80284 2.82 0.014 35.7188 268.1867 ------------------------------------------------------------------------------
( 1) D0 = 0
F( 1, 13) = 0.21 Prob > F = 0.6519 ============================================================================== * Structural Change Test: Y = X + D0 * ============================================================================== Ho: no Structural Change - Chow Test = 0.2132 P-Value > F(1 , 13) 0.6519
. chowreg y x1 x2 , dum(9) type(2)
============================================================================== * Structural Change Regression * ==============================================================================
Source | SS df MS Number of obs = 17 -------------+------------------------------ F( 4, 12) = 78.86 Model | 8568.30591 4 2142.07648 Prob > F = 0.0000 Residual | 325.944251 12 27.1620209 R-squared = 0.9634 -------------+------------------------------ Adj R-squared = 0.9511 Total | 8894.25016 16 555.890635 Root MSE = 5.2117
------------------------------------------------------------------------------ y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .9828138 .3162965 3.11 0.009 .2936629 1.671965 x2 | -1.35305 .2050101 -6.60 0.000 -1.799729 -.9063713 Dx_x1 | .6241894 .334729 1.86 0.087 -.1051225 1.353501 Dx_x2 | -1.029671 .5229864 -1.97 0.073 -2.169161 .1098183 _cons | 136.4496 45.26946 3.01 0.011 37.81594 235.0833 ------------------------------------------------------------------------------
( 1) Dx_x1 = 0 ( 2) Dx_x2 = 0
F( 2, 12) = 1.98 Prob > F = 0.1812 ============================================================================== * Structural Change Test: Y = X + DX * ============================================================================== Ho: no Structural Change - Chow Test = 1.9765 P-Value > F(2 , 12) 0.1812
. chowreg y x1 x2 , dum(9) type(3)
============================================================================== * Structural Change Regression * ==============================================================================
Source | SS df MS Number of obs = 17 -------------+------------------------------ F( 5, 11) = 75.75 Model | 8643.23205 5 1728.64641 Prob > F = 0.0000 Residual | 251.018117 11 22.8198288 R-squared = 0.9718 -------------+------------------------------ Adj R-squared = 0.9589 Total | 8894.25016 16 555.890635 Root MSE = 4.777
------------------------------------------------------------------------------ y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .3623291 .4486735 0.81 0.436 -.6251947 1.349853 x2 | -1.683503 .2618556 -6.43 0.000 -2.259843 -1.107162 D0 | -154.5432 85.2883 -1.81 0.097 -342.2615 33.17507 Dx_x1 | 1.732067 .6840704 2.53 0.028 .2264386 3.237696 Dx_x2 | -.5282025 .5535151 -0.95 0.360 -1.746481 .6900761 _cons | 231.5499 66.90452 3.46 0.005 84.29409 378.8058 ------------------------------------------------------------------------------
( 1) D0 = 0 ( 2) Dx_x1 = 0 ( 3) Dx_x2 = 0
F( 3, 11) = 2.66 Prob > F = 0.0998 ============================================================================== * Structural Change Tests: Y = X + D0 + DX ============================================================================== Ho: no Structural Change
- Chow Test [K, N-2*K] = 2.6628 P-Value > F(3 , 11) 0.0998 - Fisher Test [N2,(N1-K)] = 4.5197 P-Value > F(8 , 6) 0.0412 - Wald Test = 12.3458 P-Value > Chi2(8) 0.0021 - Likelihood Ratio Test = 9.2809 P-Value > Chi2(8) 0.0097 - Lagrange Multiplier Test = 7.1519 P-Value > Chi2(8) 0.0280
+--------+ ----+ Author +-----------------------------------------------------------
Emad Abd Elmessih Shehata Professor (PhD Economics) Agricultural Research Center - Agricultural Economics Research Institute - Eg > ypt Email: emadstat@hotmail.com WebPage: http://emadstat.110mb.com/stata.htm WebPage at IDEAS: http://ideas.repec.org/f/psh494.html WebPage at EconPapers: http://econpapers.repec.org/RAS/psh494.htm
+------------------+ ----+ CHOWREG Citation +-------------------------------------------------
Shehata, Emad Abd Elmessih (2012) CHOWREG: "Structural Change Regressions and Chow Test"
http://ideas.repec.org/c/boc/bocode/s457383.html
http://econpapers.repec.org/software/bocbocode/s457383.htm
Online Help:
* Econometric Regression Models:
* (1) (OLS) * Ordinary Least Squares Regression Models: olsreg OLS Econometric Ridge & Weighted Regression Models: Stata Module Too > lkit ridgereg OLS Ridge Regression Models gmmreg OLS Generalized Method of Moments (GMM): Ridge & Weighted Regression chowreg OLS Structural Change Regressions and Chow Test --------------------------------------------------------------------------- * (2) (2SLS-IV) * Two-Stage Least Squares & Instrumental Variables Regression M > odels: reg2 2SLS-IV Econometric Ridge & Weighted Regression Models: Stata Module > Toolkit gmmreg2 2SLS-IV Generalized Method of Moments (GMM): Ridge & Weighted Regres > sion limlreg2 Limited-Information Maximum Likelihood (LIML) IV Regression meloreg2 Minimum Expected Loss (MELO) IV Regression ridgereg2 Ridge 2SLS-LIML-GMM-MELO-Fuller-kClass IV Regression ridge2sls Two-Stage Least Squares Ridge Regression ridgegmm Generalized Method of Moments (GMM) IV Ridge Regression ridgeliml Limited-Information Maximum Likelihood (LIML) IV 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Weighted Regressi > on xtreghet MLE Random-Effects Multiplicative Heteroscedasticity Panel Data Regr > ession --------------------------------------------------------------------------- * (4) (MLE) * Maximum Likelihood Estimation Regression Models: mlereg MLE Econometric Regression Models: Stata Module Toolkit mleregn MLE Normal Regression mleregln MLE Log Normal Regression mlereghn MLE Half Normal Regression mlerege MLE Exponential Regression mleregle MLE Log Exponential Regression mleregg MLE Gamma Regression mlereglg MLE Log Gamma Regression mlereggg MLE Generalized Gamma Regression mlereglgg MLE Log Generalized Gamma Regression mleregb MLE Beta Regression mleregev MLE Extreme Value Regression mleregw MLE Weibull Regression mlereglw MLE Log Weibull Regression mleregilg MLE Inverse Log Gauss Regression --------------------------------------------------------------------------- * (5) * Autocorrelation Regression Models: autoreg Autoregressive Least Squares Regression Models: Stata Module Toolkit alsmle Beach-Mackinnon AR(1) Autoregressive Maximum Likelihood Estimation R > egression automle Beach-Mackinnon AR(1) Autoregressive Maximum Likelihood Estimation R > egression autopagan Pagan AR(p) Conditional Autoregressive Least Squares Regression autoyw Yule-Walker AR(p) Unconditional Autoregressive Least Squares Regress > ion autopw Prais-Winsten AR(p) Autoregressive Least Squares Regression autoco Cochrane-Orcutt AR(p) Autoregressive Least Squares Regression autofair Fair AR(1) Autoregressive Least Squares Regression --------------------------------------------------------------------------- * (6) * Heteroscedasticity Regression Models: hetdep MLE Dependent Variable Heteroscedasticity hetmult MLE Multiplicative Heteroscedasticity Regression hetstd MLE Standard Deviation Heteroscedasticity Regression hetvar MLE Variance Deviation Heteroscedasticity Regression glsreg Generalized Least Squares Regression --------------------------------------------------------------------------- * (7) * Non Normality Regression Models: robgme MLE Robust Generalized Multivariate Error t Distribution bcchreg Classical Box-Cox Multiplicative Heteroscedasticity Regression bccreg Classical Box-Cox Regression bcereg Extended Box-Cox Regression --------------------------------------------------------------------------- * (8) (NLS) * Nonlinear Least Squares Regression Regression Models: autonls Non Linear Autoregressive Least Squares Regression qregnls Non Linear Quantile Regression --------------------------------------------------------------------------- * (9) * Logit Regression Models: logithetm Logit Multiplicative Heteroscedasticity Regression mnlogit Multinomial Logit Regression --------------------------------------------------------------------------- * (10) * Probit Regression Models: probithetm Probit Multiplicative Heteroscedasticity Regression mnprobit Multinomial Probit Regression --------------------------------------------------------------------------- * (11) * Tobit Regression Models: tobithetm Tobit Multiplicative Heteroscedasticity Regression ---------------------------------------------------------------------------