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{title:Engle-Granger (EG) and Augmented Engle-Granger (AEG) cointegration tests and 2-step ECM estimation}
{p 8 14}{cmd:egranger} {it:varlist} [, {cmd:ecm} {cmdab:L:ags(}{it:#}{cmd:)} {cmdab:TR:end} {cmdab:QTR:end} {cmdab:REG:ress} ]
{title:Description}
{p}{cmd:egranger} conducts tests for cointegration proposed by Engle and Granger (1987),
reporting test statistics plus critical values calculated by MacKinnon (1990, 2010).
{cmd:egranger} will also estimate an ECM (Error Correction Mechanism) model
using the 2-step procedure proposed by Engle and Granger (1987).
{p}{cmd:egranger} requires Stata 9.0 or higher.
{title:Engle-Granger tests for cointegration}
{p}The Engle-Granger (EG) test for cointegration is a two-step residual-based test.
Say that we suspect that the variables y, x_1, ..., x_k are cointegrated.
The simplest form of the test is performed as follows.
First, y is regressed on a constant and x_1, ..., x_k
and the residuals are calculated.
Then, the first difference of the residuals is regressed on
the lagged level of the residuals without a constant.
The test statistic is the traditional OLS t-statistic
on the lagged residual.
Under the null hypothesis that y and x_1, ..., x_k
are not cointegrated, the residual should be non-stationary,
hence the resemblance of the EG test to
the Dickey-Fuller test for non-stationarity (see {help dfuller}).
Rejection of the null is evidence the residual is stationary,
i.e., that the series are indeed cointegrated.
{p}The EG test requires the error terms to be serially independent.
If this is not the case, lags of the first difference of the residual
can be included in the second (test) regression
so that the residuals in that regression appear serially uncorrelated.
This version of the test is usually known as
the Augmented Engle-Granger (AEG) test.
The number of desired lags is specified by the {cmd:lags(#)} option.
{p}A linear or quadratic trend may be included in the first-step regression
by using the {cmd:trend} or {cmd:qtrend} options.
{p}The distributions of the EG/AEG test statistics are non-standard,
and depend on the number of series being tested, the sample size,
and whether a linear or quadratic trend is included.
MacKinnon (1990, 2010) has computed response surface regressions
which provide critical values for all these cases.
These are the critical reported by {cmd:egranger}.
{p}The EG/AEG test can be seen as a robust alternative
to the Johansen tests for cointegration
available via Stata's {cmd:vecrank}.
The EG test makes fewer distributional assumptions,
but, unlike {cmd:vecrank}, does not allow the identification of the number
of cointegrating vectors.
In some applications this last limitation may not be important,
e.g., in the bivariate case when there are only two variables
and hence at most one cointegrating relationship.
{title:Engle-Granger two-step ECM model estimation}
{p}Engle and Granger (1987) also proposed a two-step method for estimating
ECM (Error Correction Mechanism) models.
{p}The first step is identical to the first step in the EG/AEG test procedure described above.
In the second step, the first difference of y
is regressed on the lagged level of the first-step residual
and the lagged first differences of x_1, ..., x_k using OLS.
The coefficient on the lagged residual is an estimate
of the ECM "speed of correction" parameter.
{p}The EG two-step ECM estimation is obtained by specifying
the {cmd:ecm} option.
Lags of y and x can be included by specifying the {cmd:lags(#)} option:
{cmd:lags(1)} causes the lagged first difference of y to be added
to the second-step ECM alongside the lagged first difference of x;
{cmd:lags(tau)} where tau>1 causes lags 1..tau of the first differences
of y and x to appear in the second-step ECM.
{title:Saved results and postestimation options}
{p}{cmd:egranger} uses Stata's {cmd:regress} to estimate
the test regression and ECM.
All the main {cmd:regress} results are preserved after {cmd:egranger} has run.
This allows the user to employ Stata's built-in postestimation commands
for {cmd:regress} after {cmd:egranger} is used.
In particular, the standard {cmd:regress} postestimation commands
can be used to obtain information criteria and to test for serial correlation
in the EG/AEG test regression or ECM estimation regression.
{p}In addition to the standard {cmd:regress} results,
{cmd:egranger} saves the following:
Variables
{col 4}{cmd:_egresid}{col 18}First-step residuals
Scalars
{col 4}{cmd:e(lags)}{col 18}Number of lags in the test or second-step regression
{col 4}{cmd:e(N1)}{col 18}Sample size in the first-step regression
{col 4}{cmd:e(N2)}{col 18}Sample size in the second-step regression
Scalars (EG and AEG tests only)
{col 4}{cmd:e(Zt)}{col 18}EG/AEG test statistic
{col 4}{cmd:e(cv1)}{col 18}EG/AEG 1% critical value
{col 4}{cmd:e(cv5)}{col 18}EG/AEG 5% critical value
{col 4}{cmd:e(cv10)}{col 18}EG/AEG 10% critical value
{title:Options}
{p 0 4}{cmd:ecm} requests that the Engle-Granger two-step ECM is estimated.
The default is to report the EG/AEG cointegration test.
{p 0 4}{cmd:lags(#)} specifies the number of lags
of the first difference of the residuals to include in the AEG test regression
or the number of lags of first differences of the potentially cointegrating variables
to include in the ECM second-step regression.
{p 0 4}{cmd:trend} specifies that a linear trend is included in the first-step regression.
{p 0 4}{cmd:qtrend} specifies that a quadratic trend is included in the first-step regression.
{p 0 4}{cmd:regress} requests that the first-step and (if applicable) EG/AEG test regressions are reported.
{title:Examples}
{col 0}(Cointegration test)
{phang}{stata webuse rdinc : . webuse rdinc}
{phang}{stata egranger ln_ne ln_se: . egranger ln_ne ln_se}
{phang}{stata egranger ln_ne ln_se, lags(2): . egranger ln_ne ln_se, lags(2)}
{phang}{stata egranger ln_ne ln_se, lags(2) trend: . egranger ln_ne ln_se, lags(2) trend}
{phang}{stata egranger ln_ne ln_se ln_me, lags(1) qtrend: . egranger ln_ne ln_se ln_me, lags(1) qtrend}
{col 0}(Report the internal 1st-step and test regressions)
{phang}{stata egranger ln_ne ln_se, lags(2) regress: . egranger ln_ne ln_se, lags(2) regress}
{col 0}(Replicate the above test by hand)
{phang}{stata regress ln_ne ln_se: . regress ln_ne ln_se}
{phang}{stata predict double resid, res: . predict double resid, res}
{phang}{stata regress D.resid L.resid L(1/2)D.resid, nocons: . regress D.resid L.resid L(1/2)D.resid, nocons}
{col 0}(Two-step ECM estimation)
{phang}{stata egranger ln_ne ln_se, ecm: . egranger ln_ne ln_se, ecm}
{col 0}(Use {cmd:lags(#)} option)
{phang}{stata egranger ln_ne ln_se, ecm lags(2): . egranger ln_ne ln_se, ecm lags(2)}
{col 0}(Report the internal 1st-step regression)
{phang}{stata egranger ln_ne ln_se, ecm lags(2) regress: . egranger ln_ne ln_se, ecm lags(2) regress}
{col 0}(Replicate the above 2-step ECM estimation by hand)
{phang}{stata regress ln_ne ln_se: . regress ln_ne ln_se}
{phang}{stata capture drop resid: . capture drop resid}
{phang}{stata predict double resid, res: . predict double resid, res}
{phang}{stata regress D.ln_ne L.resid L(1/2)D.(ln_ne ln_se): . regress D.ln_ne L.resid L(1/2)D.(ln_ne ln_se)}
{title:Citation}
{p}{cmd:egranger} is not an official Stata command.
It is a free contribution to the research community, like a paper.
Please cite it as such:{p_end}
{phang}Schaffer, M.E. 2010. egranger: Engle-Granger (EG) and Augmented Engle-Granger (AEG)
cointegration tests and 2-step ECM estimation.
{browse "http://ideas.repec.org/c/boc/bocode/s457210.html":http://ideas.repec.org/c/boc/bocode/s457210.html}{p_end}
{title:References}
{p 0 4}Engle, R.F. and Granger, C.W.J. 1987.
"Co-integration and Error Correction: Representation, Estimation and Testing"
Econometrica, Vol. 55, pp. 251–276.
{p 0 4}MacKinnon, James G. 1990, 2010. Critical Values for Cointegration Tests.
Queen's Economics Department Working Paper No. 1227, Queen's University, Kingston, Ontario, Canada.
Available at {browse "http://ideas.repec.org/p/qed/wpaper/1227.html":http://ideas.repec.org/p/qed/wpaper/1227.html}.
{title:Author}
{col 4}Mark E Schaffer, Heriot-Watt University, UK
{col 4}m.e.schaffer@hw.ac.uk
{title:Acknowledgements}
{p}Thanks to Julia Darby and the students in
the {browse "http://www.sgpe.ac.uk":SGPE} QM1 course for the impetus for writing this program,
and to Kit Baum for feedback on the program and help file.
{title:Also see}
{p 1 14}Manual: {hi:[TS] dfuller}; {hi:[TS] vec intro}; {hi:[TS] vecrank}{p_end}
{p 0 19}On-line: help for {help dfuller}; {help vec intro}; {help vecrank}{p_end}