Time Sereis Analysis Using Stata

“Welcome to meo school of research”

WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan

TIME SEREIS ANALYSIS USING STATA

PLEASE DONT EDIT THIS FILE OR MY NAME , THANKS. if you want to download this file search following link.

How to run regression using stata Step #1: Import data into STATA

https://drive.google.com/open? id=0B5lNKqneWZwhYWlUQy04NFRKNXc

Setp#2:

At first step, always set time otherwise u may get error, set time with the help of following command tsset years, yearly step#3: If u need to see summary of variables type in stata command bar summarize CO2 GDP OIL fdi PP or simpley write sum these are my variables)

(CO2 GDP OIL fdi PP

For detail of data give this command describe or list or br

(these are three different commands)

step #4: If u wishes to run correlation test then u may run by typing following command correlate CO2 GDP OIL fdi step#5: If u wishes to run regression then u can with the help of following command regress CO2 GDP OIL fdi {note: CO2 GDP OIL fdi are my variables first I wrote my dependent variable then all Independent variables} step#6 If u wants to check normality then u has to perform two steps after regression means run two commands consecutively  predict myResiduals, r  sktest myResiduals

PRAY FOR MY TEACHERS AND FAMILY VISIT MY BLOG FOR RESEAR RESEAR TIPS AND AND ECONOMETRIC ECONOMETRIC TECHNIQUES [email protected]



step#7 If u have run regression now, if u wish to check serial correlation then apply following command dwstat or estat bgodfrey step#7 Suppose now u want to test heteroskedasticity estat hettest, fstat or estat hettest step#8 Suppose u now want to test multicollenearity estat vif Setep#8 Suppose now u want to see either model is miss specified or not /either we have omitted variables or not/Ramsey RESET test estat ovtest Note all diagnostic tests can be run from post estimation option (statistics-----post estimation) How to test about structural breaks in data? Statistics > Postestimation

Step#1 At first step run simple regression, normally we check structural break individually in each variable, so run one by one regression like this, suppose I want to check structural breaks in my dependent variable co2. So first I should run simple regression with only co2 regress co2 step#2 Now set time with following command tsset year

(if u have monthly data then write month )

step#3 Run following command to know about structural breaks. PRAY FOR MY TEACHERS AND FAMILY VISIT MY BLOG FOR RESEAR RESEAR TIPS AND AND ECONOMETRIC ECONOMETRIC TECHNIQUES [email protected]


WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan estat sbsingle

Few old commands with new names

Out-of-date commands These commands continue to work but are out-of-date as of Stata 9. Their replacements are Old command New command -----------------------------hettest estat hottest for HSK(HETEROSCADESTICITY) imtest estat imtest (meron & Trivedi's decomposition of IM-test) ovtest estat ovtest (Ramsey RESET test using powers of the fitted values of CO2 Ho: model has no omitted variables) szroeter estat szroeter vif estat vif -----------------------------See regress postestimation. Old command New command -----------------------------archlm estat archlm bgodfrey estat bgodfrey durbina estat durbinalt (FOR SERIAL CORRELATION ALTERNATIVE TO DURBINWATSON TEST) dwstat estat dwatson (DURBINWATSON TEST FOR S.C) -----------------------------How to run time series ARDL MODEL? Step#1 Import data into stata Step#2 set times first otherwise u will get error message for time write the following command.(if u have annually data otherwise u can change frequency like monthly etc. tsset years, yearly



WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan Step#2 First install following package

net install ardl, from(http://www.kripfganz.de/stat from(http://www.kripfganz.de/stata/) a/) Write following command into command bar ardl P YU EX HE, lag(1 1 2 3) ec . ardl P YU EX HE, lag(1 1 2 3) ec

ARDL regression Model: ec

Sample: 1983 - 2013 Number of obs

=

31

This is error

Log likelihood = -388.34477 R-squared

= .91635127

Adj R-squared

= .87452691

Root MSE

= 83070.046

D.P

Coef.

correction term

Std. Err.

t

P>|t|

[95% Conf. Interval]

ADJ P L1.

-.0059958

.0017579

-3.41

0.003

-.0096627

-.0023289

YU

1797.459

14701.89

0.12

0.904

-28870.14

32465.06

EX

.0646155

.0122439

5.28

0.000

.0390751

.0901558

HE

.6086697

1.082744

0.56

0.580

-1.649894

2.867234

30.83755

26.00734

1.19

0.250

-23.41281

85.08792

LR

Long run results

SR YU D1.

Short run results

EX D1.

-.0001549

.0000722

-2.14

0.045

-.0003056

-4.17e-06

LD.

-.0002417

.0000773

-3.13

0.005

-.000403

-.0000804

D1.

-.0011703

.0065507

-0.18

0.860

-.0148348

.0124942

LD.

-.0027493

.002675

-1.03

0.316

-.0083294

.0028307

L2D.

.0003703

.0015138

0.24

0.809

-.0027874 -.0027874

.003528

_cons

3314510

169730.1

19.53

0.000

2960459

3668561

HE

.

(not p,yu ex and he I have my variable first p is dependent variable while remaining are independent variables , further I have space between all variables, and after comma I have also space and after bracket close I have also space good luck,,, lags 1,1,2,3 indicating for dependent variable there must be one lag and after dependendent variable for the first independent variable also must be lag 1 and so one ) Step#4 If u wants to conform long run relationship to the help of bound test then write following command in command box. estat btest



WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan second method of running ardl model

step#1 import data into STATA Step#2 ardl CO2 GDP OIL fdi , lags(. . . 3) maxlag(3 3 3 3) (note: here co2 is my dependent variable while other are independent variables, while lags(. . . 3) 3) is showing that for the first three variables means one dependent and other two independent variable’s I am saying to stata that ,it’s all up to stata ,program itself can select optimal lags but 3 indicating that for last independent variable I’m limiting program that there must be lag 3 for last variable, maxlag variable, maxlag (3 3 3 3) showing we can add maximum lags 3333 for all variable’s but it is ignorable Step#3 If want to see how stata chose optimal lags then run following command matrix list e(lags) step#4 Suppose now you want to see error correction term, long run as well as short run results then apply follow owing command ardl CO2 GDP OIL fdi , ec now you want to see bound test, estat btest Third Method of running ARDL in STATA Step#1 first of all install package again command is here “net install ardl, from( from(http://www.kripfganz.de/stata/ http://www.kripfganz.de/stata/))” Step#2 or search ARDL package through stata command box using “help ardl” or “findit ardl” Setp#3 here we are going to run simple ardl like in eviews we get ardl results before bounds tests and long run and short run , run following command in comamd bar first write your dependent variable then all independent variables “ardl co2 he pop pop , aic”

Step#3 As before going to long run and short run r un we go for bound tests values to conform long run cointegration. PRAY FOR MY TEACHERS AND FAMILY VISIT MY BLOG FOR RESEAR RESEAR TIPS AND AND ECONOMETRIC ECONOMETRIC TECHNIQUES [email protected]



“ardl, noctable btest”

Step#4 as in step 3 we conform about long run cointegration now we are going to run long run and short run results with error correction term(ADJ) here first I wrote my dependent variables then all independent independent . “ardl co2 he he pop , aic ec regstore(ecre regstore(ecreg)” g)”

Step#5 as now we have generate all results but we have need now of diagnostic test for the store your step#4 results with this command “estimates restore ecreg” Step#6 after restoring your your results in step 5 ,,, now run “regres” “regres” command you will see your step 4 results will appear and after this you may run following diagnostic test,

Step#7

Frequently ask question about ARDL USING STATA , it is acknowledge that i have copied this post from Aymen Ammari time line “estat dwatson” (Durbin Watson statistics, at 1st order autocorrelation). “estat archlm” (ARCH LM test for higher order autocorrelation) “estat bgodfrey” (Breusch Godfrey LM test for higher order autocorrelation) “estat hottest” (Breusch Pagan Heteroscedasticity test) “estat ovtest” (Ramsey RESET test) “estat vif” (Test for the Multicollinearity)

And finally run after ARDL for the parameters stability . CUSUM TEST Now If you want to run cusum test (parameters stability test) then run following command 



first install this package “ssc “ ssc install cusum6” cusum6 ” (note: internet is necessary for installation) now type this command “cusum6 variable1 variable2 variable3,cs(cusum) lw(lower) uw(upper)



How to select optimal lags Statistics > Multivariate time series > VAR diagnostics and tests > Lag-order selection statistics (preestimation) Or select optimal lags through following command varsoc LOGFDI LOGGDP LOGDD LOGINF LOGEXCHRT, maxlag(8) PRAY FOR MY TEACHERS AND FAMILY VISIT MY BLOG FOR RESEAR RESEAR TIPS AND AND ECONOMETRIC ECONOMETRIC TECHNIQUES [email protected]


WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan . tsset year, yearly time variable: delta:

year, 1984 to 2013 1 year

. varsoc LOGFDIGDP LOGGDP LOGDD

LOGINF LOGEXCHRT, maxlag(8)

Selection-order criteria Sample:

1992 - 2013

lag

LL

LR

Number of obs

df

p

FPE

AIC

HQIC

=

22

SBIC

0

-165.706

3.77983

15.5187

15.5772

15.7667

1

-53.6883

224.04

25

0.000

.001488

7.60803

7.95851

9.09582

2

-15.9819

75.413

25

0.000

.000715

6.4529

7.09544

9.1805

3

68.8475

169.66

25

0.000

.000013

1.01387

1.94847

4.98129

4

1703.49

3269.3

25

0.000

5.6e-66* -145.318

5

3236.36

3065.7

25

0.000

3336.8

6

.

-284.214

-144.091

-140.11

-282.929

-278.759

200.88

25

0.000

.

-293.345

-292.06

-287.89

7

3297.68 -78.245

25

.

.

-289.789

-288.504

-284.333

8

3354.19

25

0.000

.

-294.927* -293.642* -289.471*

Endogenous: Exogenous:

113.04*

LOGFDIGDP LOGGDP LOGDD LOGINF LOGEXCHRT _cons

Or Step#1

Step#2




Set time first otherwise u

may get error Write ur all variables

Chose maximum lags Normally use in between 5-10 and keep all thing unch unchan an ed

How to test cointegration Statistics > Multivariate time series > Cointegrating rank of a VECM Step#1




Step#2

Write ur variables, like first dependent then all indep Chose optimal lags, which u deicide form lag length criteria and ok



WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan How to run VECM MODEL?

Statistics > Multivariate time series > Vector error-correction model (VECM) Step#1

And ok

Step#2




Write your variabels,first dependent then all independent variables Write here number of cointegration equations which u finds from Johansson test Add here

but I would like to

maximum

suggest u add all the

lags or

time 1 for simplisticity

optimal

Step#3

Coef.

Std. Err.

z

P>|z|


negative and in between 0-

D_P

Long run causality value must be

_ce1 L1.

-.0002116

.0000693

-3.05

0.002

-.0003474

-.0000758

LD.

2.641314

.0914989

28.87

0.000

2.46198

2.820649

L2D.

-2.631597

.1808343

-14.55

0.000

-2.986025

-2.277168

L3D.

1.00195

.1026645

9.76

0.000

.8007314

1.203169

LD.

-17.44365

6.310434

-2.76

0.006

-29.81187

-5.075422

L2D.

-10.78266

4.202071

-2.57

0.010

-19.01857

-2.546755

L3D.

-2.692897

1.817526

-1.48

0.138

-6.255183

.8693891

LD.

-4.32e-06

9.15e-06

-0.47

0.637

-.0000222

.0000136

L2D.

-1.80e-06

9.17e-06

-0.20

0.845

-.0000198

.0000162

L3D.

.0000111

9.78e-06

1.13

0.257

-8.09e-06

.0000303

LD.

.0032082

.0011028

2.91

0.004

.0010467

.0053698

L2D.

.0011455

.0005591

2.05

0.040

.0000496

.0022414

L3D.

.0005969

.0003585

1.67

0.096

-.0001057

.0012996

_cons

-164710.4

57151.86

-2.88

0.004

-276726

-52694.85

1..which indicate error correction term ,speed of

P

adjustment

YU

Short run causality

EX

HE

Step#4 PRAY FOR MY TEACHERS AND FAMILY VISIT MY BLOG FOR RESEAR RESEAR TIPS AND AND ECONOMETRIC ECONOMETRIC TECHNIQUES [email protected]


WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan Wald test for short run causalities if you want to see jointly impact of lags variabels on dependent variables

Go to statistics----post estimation---test, contrast, and comparison of parameters,---linear test of parameters How to run IMPULSE RESPONSE FUNTION If u want to run through MANU,, follow these steps Statistics > Multivariate time series > IRF and FEVD analysis > Graphs by impulse or response Step#1 (actually impulse response functions used after VAR models) Run VECM model Step#2

Then use irf create to estimate the IRFs and FEVDs and save them in a file, and finally use irf graph or any of the other irf analysis commands to examine results:, like run following command irf create order1, step(10) set(myirf1) see impulse response function, the following following function will show show over all Step#3 now I want to see impulse response function results irf graph irf, irf(order1) step#4

suppose you are not interest in all variables v ariables response function ,I mean to say I just want to see only independent variables shock’s effect on dependent then apply following command. irf graph irf, irf(order1) impulse(GDP OIL fdi) response(CO2 ) (note here GDP,OIL and fdi are my independent variables and co2 dependent . How to run var model? Statistics > Multivariate time series > Vector autoregression (VAR) Step#1




Step#2




Step#3 Equation

Parms

RMSE

R-sq

chi2

P>chi2

P

11

43425.6

1.0000

2.14e+07

0.0000

YU

11

580.764

0.7792

112.9032

0.0000

EX

11

2.3e+08

0.9691

1003.692

0.0000

HDI

11

1.01628

0.9989

6665.462

0.0000

HE

11

9.9e+06

0.3616

18.12447

0.0529

Coef.

Std. Err.

z

P>|z|


P P L1.

1.859308

.0743769

25.00

0.000

1.713532

2.005084

L2.

-.8601668

.0746806

-11.52

0.000

-1.006538

-.7137955

L1.

4.900434

11.82905

0.41

0.679

-18.28407

28.08494

L2.

.6010501

6.50086

0.09

0.926

-12.1404

13.3425

YU

EX

L1.

.0000743

.0000315

2.36

0.018

.0000125

.000136

L2.

.0000507

.0000366

1.39

0.166

-.000021

.0001223 .0001223

L1.

10483.9

7579.004

1.38

0.167

-4370.678

25338.47

L2.

-13216.11

7908.145

-1.67

0.095

-28715.79

2283.57

HDI

HE L1.

-.0013774

.0007436

-1.85

0.064

-.0028348

.00008

L2.

-.0078436

.0040739

-1.93

0.054

-.0158283

.000141

_cons

477355

214606.1

2.22

0.026

56734.76

897975.3

Step#4 for short run granger causality/wald test Statistics > Multivariate time series > VAR diagnostics and tests > Granger causality tests




Setp#

U have no need to change anything just click ok



WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan Step#6 and finally granger causality test Here results showing that “P” is

. vargranger

dependent variables while YU,EX, HDI ETC INDEPENDNET VARIAELS,

Granger causality Wald tests

Equation

Excluded

chi2

df Prob > chi2

P

YU

.17274

2

0.917

P

EX

21.011

2

0.000

P

HDI

5.4149

2

0.067

P

HE

5.7897

2

0.055

P

ALL

40.749

8

0.000

YU

P

9.9949

2

0.007

YU

EX

8.8705

2

0.012

YU

HDI

3.3333

2

0.189

YU

HE

51.299

2

0.000

YU

ALL

93.069

8

0.000

THAT I HAVE TO ACCEPT ALTERNATIVE

EX

P

8.7329

2

0.013

EX

YU

1.4657

2

0.481

HYPOTHESIS

EX

HDI

4.4102

2

0.110

EX

HE

5.1576

2

0.076

EX

ALL

15.527

8

0.050

HDI

P

12.807

2

0.002

HDI

YU

3.519

2

0.172

HDI

EX

7.3963

2

0.025

HDI

HE

1042.9

2

0.000

HDI

ALL

1346.5

8

0.000

IN THE SECOND ROW OF RIGTHT SIDE FIRST COLUM, SHOWING THT EX JOINTLY GRANGER CUSE P IN SHORT RUN . AS NULL HYPOTHESIS WAS NO GRANGER CASUE AS PROBABLITY VALUE

IS LEST THAN 5% SO I CAN SAY HERE

Panel data Models H ow to take take pane panell u nit root Stati Sta ti stics > L ongitu din al/panel data data > Li nea nearr models models > Li nea nearr r egres gress sion (F E, RE, PA, BE ) Setep#1 Give fi rst id with f ollowing command command.. .. ege gen n countr y1=group( countr y) (note: i f you have countr ies data) ege gen n Company1=grou Compan y1=grou p( Company) panel

(note: (n ote: i f you h ave compani es data) means declar declar e data




Click on unit root test

Step#2

Select variables to which u want to take unit root

Select if u

Select test type

need suppose u want to add

Set time and give panel

time trend

id to cross sections

the check first option

Select optimal lags ,suppose one




Panel data anal ysi ysi s f r om star star t to end.. end.. Poll ed, random ,f i xed ef f ect ,hausman test. test. 1.impor 1.impor t your your data fi le into stata stata 2.now cr eate a pool pool or si mple mpl e stata stata gi ve codes codes to each each cr oss oss se section cti on or enti nt i ty li l i ke if you h ave dif f er ent coun tr i es data or or compani es the u have to give specif pecif i c code all countr coun tr i es or compani es, fu r the th er i f you have as assign code by your your sel f suppose suppose u di d not wr i te company company name

like “nestles” but you indicated nestle with 111 now u see you have already given the code but i f you have si si mple mpl e r i ght th e name of company th en u need need to give also also code code ege gen n countr y1=group( countr y) (note: i f you have countr ies data) ege gen n Company1=grou Compan y1=grou p( Company)

(note: (n ote: if you have compani es data)

3.now se set ti me which is most most i mportant xtset Compan y1 year, xtset year, ye year arly ly (note: h ear I have ye yearl y data and compa company1 ny1 i s new new vari able whi ch I genr genr ate in step tep 2) 4.now l ook at descri descri ptive pti ve stati sti cs Xtsum vari able1 vari vari abe abel2 l2

xtsum ENVC EPS ROA ROE ROC 4.1 suppose u want to make a graph xtline CO2 energy gdp gi 4.2 for description of data xtdescribe 5. Now run fixed effect model Xtreg dependent variable1 independent variable 1234456,fe xtreg ENVC EPS ROA ROE,fe Now store result of fixed effect from this this command 6.estimate 6.estimate store fe

( if u want to run b y Manu Statistics > Postestimation )

7. Now run random effect model xtreg dep indep1 indep2 indep3, re

(replace with your variables name)




now store result of fixed effect from this command 8. estimate store re

9. now the last thing what model is suitable random effect or fixed effect for this run Housman test(note if you do not restore results of random effect and fixed effect may u face error prob) hausman fe re how to run pooled regression in stata reg dep indep indep 10. Further u can double check either random /fixed effect /or polled appropriate. But note, suppose hausaman verified random effect is appropriate, so we can double check for step 9, suppose hausman test verified random effect model is appropriates so in step ten we will conform either really random effect is appropriate or not so we will run test and verify hypothesis between polled model and random effect actually we have already done with fixed effect using hausman test.steps — statistics statistics — longitudinal longitudinal /panel data---linear model--langrangian multiplier and null hypothesis is polled is appropriate. And alternative hypothesis is random effect is appropriate.  How to run 2sls two stage least square

Statistics > Endogenous covariates > Single-equation instrumental-variables regression Step#1 ivregress 2sls consumtion remetence (income = investment) (note here income is my endogenous and investment instrumental is my instrumental variables) Step#2 As I have run 2sls model but now I have to conform that either in reality really endogeniety problem was exist or not estat endog if probability value comes more than 5% then we say there is no endogeniety endo geniety but if prob value comes less than in this case we say sa y there is endogeniety prob,, which is desirable; setp#3 Now I have I have conform either endogeniety problem exist or not now I want to know either my instruments are weak or strong estat firststage PRAY FOR MY TEACHERS AND FAMILY VISIT MY BLOG FOR RESEAR RESEAR TIPS AND AND ECONOMETRIC ECONOMETRIC TECHNIQUES [email protected]



step#4 Now I want to know either my instruments are over identified or not? estat overid { sargan and basman test is used to know about the over identification if probability value comes of thesis test more than5% we say model is correct specified

Null hypothesis for over identified instruments: instrument instrument set is valid and the model is correct specified}

PANEL ARDL USING STATA 1) First of all install this package to run PANEL ARDL “ssc install xtpmg, replace” 2) Suppose you think you have h ave installed this package but still you are not sure then type in command bar” type xtpmg” 3) If u see message of no found then install otherwise you have already install it.  here we shall Run MG (average): xtpmg d.CO2 d.energy d.gdp , lr(l.CO2 energy gdp ) ec(ECT) replace mg  here we shall Run MG (individual): (It allows for all coefficients to vary and be heterogeneous in the long-run and short-run. However, the necessary condition for the co nsistency and validity of this approach is to have a sufficiently large time-series dimension of the data.)

xtpmg d.CO2 d.energy d.gdp , lr(l.CO2 energy gdp ) ec(ECT) replace full mg  here we shall Run PMG (average): xtpmg d.CO2 d.energy d.gdp , lr(l.CO2 energy gdp ) ec(ECT) replace pmg  here we shall Run PMG (individual): (The main characteristic of PMG is that it allows short-run coefficients, including the intercepts, the speed of adjustment to the long-run equilibrium values, and error variances to be heterogeneous country by country, while the long-run slope coefficients are restricted to be homogeneous across countries.)

xtpmg d.CO2 d.energy d.gdp , lr(l.CO2 energy gdp ) ec(ECT) replace full pmg  here we shall Run Hausman test to choose between MG and PMG: hausman mg pmg, sigmamore now if our probability value comes more than 5% we run PMG if our probability value comes less than 5% we run MG  Running DFE:

xtpmg d.CO2 d.energy d.gdp , lr(l.CO2 energy gdp ) ec(ECT) replace dfe * Running Hausman test to choose between MG and DFE: hausman mg DFE, sigmamore Note:




Suppose you want to run all these tests on your data, so simple just import your data into stata and copy command from here into stata command bar and replace my variables name with yours. Good luck. PANEL ARDL Pooled Mean Group (PMG) model The main characteristic of PMG is that it allows short-run coefficients, including the intercepts, the speed of adjustment to the long-run equilibrium values, and error variances to be heterogeneous country by country, while the long-run slope coe fficients are restricted to be homogeneous across countries. This is particularly useful when there are r easons to expect that the long-run e quilibrium relationship between the variables is similar across countries or, at least, a sub-set of them. The shortrun adjustment is allowed to be country-specific, due to the widely different impact of the vulnerability to financial crises and external shocks, stabilization policies, monetary policy and so on. However, there are several requirements for the validity, consistency and efficiency of this methodology. First, the existence of a long-run relationship among the variables of interest requires the coefficient on the error –correction –correction term to be negative and not lower than -2. Second, an important assumption for the consistency of the ARDL model is that the resulting residual of the error -correction model be serially uncorrelated and the explanatory variables can be treated as exogenous. Such conditions can be fulfilled by including the ARDL (p,q) lags for the dependent (p) and independent variables (q) in error correction form. Third, t he relative size of T and N is crucial, c rucial, since when both of them are large t his allows us to use the dynamic panel technique, which helps to avoid the bias in the average estimators and resolves the issue o f heterogeneity. Eberhardt and Teal (2010) argue that the treatment of heterogeneity is central to understanding the growth process. Therefore, failing to fulfil these conditions will produce inconsistent estimation in PMG. The PMG estimator constrains the long term c oefficients to be the same across c ountries and allows only the short-term coefficients to vary. Mean Group (MG) estimator The second technique (MG) introduced by Pesaran and Smith, (1995) c alls for estimating separate regressions for each country and calculating the coefficients as unweight means of the estimated coefficients for the individual countries. This does not impose any restrictions. It allows for all coefficients to vary and be heteroge neous in the long-run and short-run. However, the necessary condition for the consistency and validity of this approach is to have a sufficiently large time-series dimension of the data. The cross-country dimension should also be large (to include about 20 to 30 countries). Additionally, for small N the average estimators (MG) in this approach are quite sensitive to outliers and small model permutations (see Favara, 2003). PRAY FOR MY TEACHERS AND FAMILY VISIT MY BLOG FOR RESEAR RESEAR TIPS AND AND ECONOMETRIC ECONOMETRIC TECHNIQUES [email protected]


WWW.SAEEDMEO.BLOGSPOT.COM Muhammad saeed AAS Khan Meo, Superior university Lahore Pakistan Dynamic Fixed Effects (DFE) model Finally, the dynamic fixed effects estimator (DFE) is v ery similar to the PMG estimator and imposes restrictions on the slope coefficient and error variances to be equal across all countries in t he long run. The DFE model further restricts the speed of adjustment coefficient and the short-run coefficient to be equal too. However, the model features country-specific intercepts. DFE has cluster option to estimate intra-group correlation with the standard error (Blackburne and Frank, 2007). Nevertheless, Baltagi, Gri, and Xiong (2000) point out that this model is subject to a simultaneous equation bias due to the endogeneity between the error term and the lagged dependent variable in case of small sample size.

How to run DOLS model Setpe# import data Step#2 install following package “ssc install ltimbimata, replace” replace” Step#3 Before beginning the estimations, we use the set more off instruction to tell Stata not to pause when displaying the output. “set more off ” Step#4 now run dols model, we regress iskr (dependent variable) on the regressors (gdskr irxmap1 defigd2 ltinflcd opins2 totwdct ltdgdpd). “ xtdolshm iskr gdskr irxmap1 defigd2 ltinflcd opins2 totwdct ltdgdpd” ltdgdpd” Step#5 if you want to increase lags and leads xtdolshm iskr gdskr irxmap1 defigd2 ltinflcd opins2 totwdct, nla(3) nle(4) step#6 if you want estimation at 10 % level of significance xtdolshm iskr gdskr irxmap1 defigd2 ltinflcd opins2 totwdct, nla(3) nle(4) level(90)




Primary data analysis Step#1 Suppose I want to see descriptive de scriptive statistics of my variables summarize cs1 cs2 cs3 cs4

(cs1,2,3 and 4 are my variables)

step#2 *suppose you want to know about correlation among variables correlate cs1 cs2 cs3 cs4

(cs1,2,3 and 4 are my variables)

step#3 *now you want to che ck reliability(cronbach alpha values) of items ,so first write alpha then all items with space alpha cs1 cs2 cs3 cs4

( cs1 cs2 cs3 cs4 are my items)

Step#4 *now we are going to run PCA and want to see egen values /component means from the items how much component we can create pca cs1 cs2 cs3 cs4

( cs1 cs2 cs3 cs4 are my items)

step#5 Now i also want to know about the KMO value of PCA estat kmo, novar step#6 *now i want to make a construct/variables from (4 items)cs1 cs2 cs3 cs4 and suppo se i give name to this new single variable like saeed1 PRAY FOR MY TEACHERS AND FAMILY VISIT MY BLOG FOR RESEAR RESEAR TIPS AND AND ECONOMETRIC ECONOMETRIC TECHNIQUES [email protected]



predict saeed1, score Step#6 Actually I have converted my m y all items into variables now I want to run regressions between these variables regress cs1 cs2 cs3 cs4 PCA)

(suppose cs1,2,3 and 4 are my variables which are made after Last tips and tricks

Finally how to generate new variables 1. Suppose you want to generate a series of square of any variable gen cs1sqrt=sqrt( cs1)

(note cs1 is my variabel)

2. Suppose you want to take log gen cs1log=log( cs1) 3. Suppose u want to add two variables gen cs1pluscs3 = cs1+cs3 4. Suppose you want to generate gen erate series with first difference 5.

generate fdpop = d.pop

How to get help from stata Suppose you are running any test but at some points you got confused/ stuck so how u can precede now, suppose I was running panel unit root but I was not sure about null hypothesis of different test how I can precede now? So to get rid of this problem see following pics and enjoy.




In this you can see description about ab out the null hypothesis of all tests so u can get rid of any an y problem just click on help button





Time Sereis Analysis Using Stata

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