help: chowreg dialog:

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 - Egypt
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 Toolkit
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 Models:
reg2 2SLS-IV Econometric Ridge & Weighted Regression Models: Stata Module Toolkit
gmmreg2 2SLS-IV Generalized Method of Moments (GMM): Ridge & Weighted Regression
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 Ridge Regression
ridgemelo Minimum Expected Loss (MELO) IV Ridge Regression
---------------------------------------------------------------------------
* (3) * Panel Data Regression Models:
regxt Panel Data Econometric Ridge & Weighted Regression Models: Stata Module Toolkit
xtregdhp Han-Philips (2010) Linear Dynamic Panel Data Regression
xtregam Amemiya Random-Effects Panel Data: Ridge & Weighted Regression
xtregbem Between-Effects Panel Data: Ridge & Weighted Regression
xtregbn Balestra-Nerlove Random-Effects Panel Data: Ridge & Weighted Regression
xtregfem Fixed-Effects Panel Data: Ridge & Weighted Regression
xtregmle Trevor Breusch MLE Random-Effects Panel Data: Ridge & Weighted Regression
xtregrem Fuller-Battese GLS Random-Effects Panel Data: Ridge & Weighted Regression
xtregsam Swamy-Arora Random-Effects Panel Data: Ridge & Weighted Regression
xtregwem Within-Effects Panel Data: Ridge & Weighted Regression
xtregwhm Wallace-Hussain Random-Effects Panel Data: Ridge & Weighted Regression
xtreghet MLE Random-Effects Multiplicative Heteroscedasticity Panel Data Regression
---------------------------------------------------------------------------
* (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 Regression
automle Beach-Mackinnon AR(1) Autoregressive Maximum Likelihood Estimation Regression
autopagan Pagan AR(p) Conditional Autoregressive Least Squares Regression
autoyw Yule-Walker AR(p) Unconditional Autoregressive Least Squares Regression
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
---------------------------------------------------------------------------