hetero and serial corr however GLS is not a dynamic estimator but can correct for hetero, serial

corr and cross sectional dependence. GMM cannot correct for CD. GLS cannot account for IV (and

systems of equations) and differenced data and hence can only estimate using data at level while

GMM can do both at level and difference accounting for IV and systems of equations. Thus GLS is

weaker with respect to endogeneity.

GMM in STATA can be done either using menu driven or command.

Using menu:

1. Having imported your data into STATA, using any of the ways you are familiar with.

2.Then go to statistics in the menu bar, scroll down to longitudinal/panel data, click on it

3. It wil give u drop down menu where u will see dynamic panel data, click on it, it will also show u

drop down list of Arrelano & Bond, Arrelano & Bover/ Blundell & Bond

4. Click on the one you want to do, it open dialog box for u showing dependent and independent

variables as well as one-step or two-step and other information

5. It will display results for u then u can go for sargan test and AB Serial correlation test. Aliter,

Using command:

1. First declare the data to be panel

2.Arellano and Bond (1991) 1st Difference GMM estimator

xtabond i f c, lag(1)

xtabond i f c, lag(1) artests(2)

xtabond i f c, lag(1) twostep

3.Arellano and Bover (1995) unifying GMM is the same as Blundell – Bond System GMM

Blundell and Bond (1998) System GMM

xtdpdsys i f c, lags(1) twostep

xtdpdsys i f c, lags(1) twostep artests(2)

4. Sargan test of overidentifying restrictions

estat sargan

5. Arellano-Bond test for zero autocorrelation in first-differenced errors

estat abond

For more on the command there are STATA journals that free

hetero and serial corr however GLS is not a dynamic estimator but can correct for hetero, serial

corr and cross sectional dependence. GMM cannot correct for CD. GLS cannot account for IV (and

systems of equations) and differenced data and hence can only estimate using data at level while

GMM can do both at level and difference accounting for IV and systems of equations. Thus GLS is

weaker with respect to endogeneity.

1) One-step difference GMM with robust std. error:

xtabond2 y x1 x2, gmm(l.y x1 x2) iv(i.year) nol robust small

2) Two-step difference GMM with corrected std. error:

xtabond2 y x1 x2, gmm(l.y x1 x2) iv(i.year) nol twostep robust small

3) One-step system GMM with robust std error:

xtabond2 y x1 x2, gmm(l.y x1 x2) iv(i.year) robust small

4) Two-step system GMM with corrected std error:

xtabond2 y x1 x2, gmm(l.y x1 x2) iv(i.year) twostep robust small

can do it in eviews as follows: 1. Having imported d data into Eviews, then go to estimate equation

an specify d equation.

2. Then change d default from OLS to GMM

3. Then click on Wizard tab, then it wil guide u stepwise

4 u will com 2 a stage where it will ask to specify either diagonal or diffrence

5. U shud undertd dat Eviews has only Arrelano and Bond and Arrelano and Bover unlke Stata that

has BB and xtabond2

to be applied on your data that have endogenity

GMM" as such>>

Difference GMM:

All variables in the model are first-differenced to eliminate time-invariant country effects, and then

lagged level of endogenous explanatory variables are used as the instruments. For lagged

dependent variable that may be correlated with error term, higher order lags of dependent variable

are used as instrument for lagged (one) dependent variable. Validity of moment conditions is

required for GMM estimator to yield unbiased and consistent estimators, i.e. the instruments (i.e.

the lagged dependent variables, and lagged vectors of endogenous explanatory variables) must not

be correlated with the error terms. There are however conceptual and statistical shortcomings with

this difference estimator. Alonso-Borrego and Arellano (1999), and Blundell and Bond (1998) point

out that when explanatory variables are persistent over time, lagged levels of these variables make

weak instruments for regression in differences, and instrument weakness in turn influences the

asymptotic and the small-sample performance of the difference estimator. Asymptotically, variance

of the coefficients will rise, and in small sample, Monte Carlo experiments show that weak

instruments can produce biased coefficients

System GMM:

To reduce potential biases and imprecision associated with difference estimator, a new estimator

that combines regression in differences with regression in levels is proposed by Arellano and Bover

(1995) and Blundell and Bond (1998) called system GMM. Whilst the instruments for regression in

differences remain the same, the instruments for regressions in levels will be the lagged differences

of the corresponding variables. These are appropriate instruments under an additional assumption i.

e. the differences of these variables must be uncorrelated with the country specific effect

notwithstanding the possible correlation between levels of the explanatory variables and the

country specific effect. This is because we assume the country specific effect is constant across

times (time-invariant).

actually used for GMM. Since you have mix of i(0) and i(1), you better go for mg,pmg, DFE etc.

method to test for institutional impact on various economic indicators due to the fact that institutions

are frequently assumed to be endogenous.

time. 1 endogenity bias 2 panel hetrogenity bias 3 over identifcation problem

usually in born heterogeneity prevails. No other techniques can successfully eliminate the problem

of heterogeneity except panel GMM. Yes it is true that most of the researchers are still using FE

OLS or RE OLS or Pooled OLS or FGLS or PCSE OLS. Empirical evidences suggests these

techniques can not successfully eliminate the heterogeneity problems. In this regard panel GMM

works better. One more thing, sometimes robust regression itself can not remove

heteroscedasticity. If T is greater than 30, one should go for panel long and short run analysis. In

this regard DOLS (Stock and Watson, 1993) works better for long run equation estimation.

Hossain Academy Note

Ade Kutu

Afolabi Luqman

Abdullah Sonnet

Asad Zaman

Atiq Rehman

Burcu Özcan

Ghumro Niaz Hussain

Muhammad Anees

Mohammad Zhafran

Muzammil Bhatti

Monis Syed

Mine PD

Moulana N. Cholovik

Muili Adebayo Hamid

Nicat Gasim

Najid Iqbal

Nasiru Inuwa

Noman Arshed

Rapelanoro Nady

Seye Olasehinde-Williams

Suborno Aditya

Sayed Hossain

Shishir Sakya

Sheikh Muzammil Naseer

Tella Oluwatoba Ibrahim

Younes Azzouz

Univariate Models |

Multivariate Models |

Panel Data Model |