help suchowtest-------------------------------------------------------------------------------

Title

suchowtestperforms successive Chow tests on cross section data

Syntax

suchowtestdepvar[indepvars] [if] [in] [weight] [,options]

optionsDescription ------------------------------------------------------------------------- Modelthresv(varname)indicates the threshold variablestub(string)designates a string name from which new variable names will be createdfpctile(#)specifies the lower bound percentile of the threshold variablelpctile(#)specifies the upper bound percentile of the threshold variablestep(#)indicates the step by which we want to move the sample's break pointsig(#)designates the significance level we want to set for the p-value of the Chow testReporting

nographssuppress the display of graphs after the estimations are performedAdditional Options

regress_optionsIn addition to the options listed above, all options of theregresscommand can be used -------------------------------------------------------------------------aweights,fweights,iweights, andpweights are allowed; see weight.byis not allowed withsuchowtest; see[D] byfor more details onby.indepvarsand thethresv(varname)option may contain factor variables; see fvvarlist.depvar,indepvarsand thethresv(varname)option may contain time-series operators; see tsvarlist.

Description

suchowtestperforms successive Chow tests on cross section data. Habitually, when we are doing the Chow test, we split the sample of study in two subsamples using an exogenous break point. Unlike the previous method, the commandsuchowtestfinds the break point endogenously. We do not have to supply a break point. If there is a threshold, the command finds it by using the information given by the data. If there is no break point the command will inform us too. This method of finding thresholds appears to be more reasonable in cases where the researcher does not know a priori the breaking point. The theory behind the commandsuchowtestis provided by Berthelemy and Varoudakis (1996).

Options+-------+ ----+ Model +------------------------------------------------------------

thresv(varname)indicates the threshold variable. To form this option, you put inside the brackets the variable name representing the threshold variable. You must specify this option in order to get a result. Hence this option is not optional.

stub(string)designates a string name from which new variable names will be created. To form this option, you put inside the brackets a string name. Then new variable names will be created from this string. You must specify this option in order to get a result. Hence this option is not optional.

fpctile(#)specifies the lower bound percentile of the threshold variable that must be included in the search for a break point. The default value of this option is 10. Hence the search for the break point starts at the 10th percentile of the threshold variable.

lpctile(#)specifies the upper bound percentile of the threshold variable that must be included in the search for a break point. The default value of this option is 90. Hence the search for the break point starts at the 10th percentile and goes up to the 90th percentile of the threshold variable.

step(#)indicates the step by which we want to move the sample's break point. The default value of this option is 1. This means that the sample's break point is moved forward by one observation every time.

sig(#)designates the significance level we want to set for the p-value of the Chow test. The default value of this option is 0.10. This means that we want the p-value of the Chow test to be significant at most at the 10% level.+-----------+ ----+ Reporting +--------------------------------------------------------

nographssuppress the display of graphs after the estimations are performed. This option is used when we do not want to display the graphs after the estimations are done.+--------------------+ ----+ Additional Options +-----------------------------------------------

regress_options:noconstant,hascons,tsscons,vce(vcetype),level(#),beta,eform(string), etc. See[R] regress. All options of theregresscommand can be used.You can use all the options of the command

regress. To use them, enter them in the same manner that you would do with theregresscommand.

Saved results

suchowtestsaves the following inr():Scalars

r(maxchowfh)Chow Test (F-Test)r(maxpvchowtest)P-Value of the Chow Testr(maxbreakpt)Value of the break point parameterr(maxobsvalue)Observation number corresponding to the break pointr(qlstat)Maximum of the QL StatisticMacros

r(chowfh)Variable containing all the Chow F-Statisticsr(chowstatpv)Variable containing all the P-Values of the Chow Testr(breakptpar)Variable containing all the Break Point Parametersr(qlvariable)Variable containing all the QL Statistics

ExamplesBefore beginning the estimations, we use the

set more offinstruction to tellStatanot to pause when displaying the results.set more off

We load the data we are going to use and describe them. The description shows that we have cross section data which represent the average from 1975 to 2004.

use http://fmwww.bc.edu/repec/bocode/s/suchowtestdata.dta, clear

describe

We estimate a standard conditional convergence growth regression in which the real GDP per capita growth rate is regressed on initial real GDP per capita, stock market capitalization (financial development) and human capital. We use financial development as threshold variable with the option

thresv(). We also specify the optionstub()in which we put the string "sct". Note that these two options are required.suchowtest croisspibt lninitgdppc lnstmktcap lnyr_sch_sec, thresv(lnstmktcap) stub(sct)

The estimation firstly gives some statistics concerning the threshold. The first statistic is the observation number at which the break point occurs. The second is the maximum of the QL statistic. The third is the Chow test and its p-value and the last one is the value of the threshold variable at the break point. Secondly, the estimation provides the OLS regression below the break point parameter. Thirdly, the estimation gives the OLS regression above the break point parameter. The estimation also offers three graphs. The first titled "QL STATISTIC" draws the QL statistic against the break point parameter. The Greene vertical line represents the observation at which the break point occurs. The second titled "P-VALUES OF THE CHOW TEST" graphs the p-values of the Chow test against the break point parameter. The Greene horizontal line is the significance level of the p-value of the Chow test. The third titled "PV. CHOW TEST AND QL STAT." is the combination of the two previous graphs. The left y-axis graphs the p-values of the Chow test while the right y-axis provides the QL statistic.

The estimation also generates variables containing the previously provided statistics. These variables contain: The break point parameter, the QL statistic, the p-values of the Chow test and the Chow F-statistic. To see these variables, we type:

describe sct_*

We illustrate the use of options

fpctile(#)andlpctile(#). By default the search for the break point starts at the 10th percentile and goes up to the 90th percentile of the threshold variable. Now we extend the search range of the threshold. We start from the 5th percentile and goes up to the 95th percentile.suchowtest croisspibt lninitgdppc lnstmktcap lnyr_sch_sec lngconsgdp lnopenwb, thresv(lnstmktcap) stub(sct) fpctile(5) lpctile(95)

We show the use of the option

sig(). We enter the value 0.01 as the significance level of the p-value of the Chow test.suchowtest croisspibt lninitgdppc lnstmktcap lnyr_sch_sec lngconsgdp lnopenwb, thresv(lnstmktcap) stub(sct) sig(0.01)

The command

suchowtestindicates that there is no break point at this significance level. It suggests us to increase the significance level to augment the chance of obtaining a threshold. Hence we increase the significance level to 0.05.suchowtest croisspibt lninitgdppc lnstmktcap lnyr_sch_sec lngconsgdp lnopenwb, thresv(lnstmktcap) stub(sct) sig(0.05)

If we do not want to display the graphs, we type:

suchowtest croisspibt lninitgdppc lnstmktcap lnyr_sch_sec lngconsgdp, thresv(lnstmktcap) stub(sct) nographs

In addition to the options cited above, all options of the

regresscommand can be used. For instance we show here how to compute robust standard errors with the optionvce(robust).suchowtest croisspibt lninitgdppc lnstmktcap lnyr_sch_sec lngconsgdp vtotopen, thresv(lnstmktcap) stub(sct) vce(robust)

ReferencesBerthelemy, J. C. and A. Varoudakis: 1996, "Economic Growth, Convergence Clubs, and the Role of Financial Development",

Oxford Economic Papers48(2), 300-328.

AuthorDiallo Ibrahima Amadou, zavren@gmail.com

Also seeOnline: help for

regress,chowreg(if installed)