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I am professionally trained and the highly recogized online course provider in Stata, Eviews, SPSS, Nvivo10/11, WinRATS, SAS, GAUSS, Gretl, Minitab, C++, JavaScript and Python. I helped more than 300 clients from around the world in applied econometrics and statistics for corporate governance, financial performance, economics research, business evaluation, Value at Risk, Options Pricing, Stock Evaluation, Pairs Trading and Backtesting through the use of above statistical softwares. It is backed by my education in economics, statistics and econometrics from The University of Sheffield, UK.
I have a teaching and academic research experience of more than 11 years at a QS Ranked University. I teach modules in Economics, Statistics, Econometrics and Quantitative Analysis. Key themes and topics of my teaching are Qualitative Data Analysis, Factor Analysis, Principle Component Analysis, Power and Sample Size determination for Survival Studies, Analysis of Open ended surveys and interviews, Multivariate Time Series techniques in VAR/VECM, VARX, SVAR, Multivariate GARCH, ARDL and Bayesian Multivariate Time Series Methods. So far, more than 70 PhD and MS/MRes candidates completed their courses in Applied Econometrics and Applied Statistics under my supervision.
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Tests for Autocorrelated Errors

In ordinary least square regression model, we specify the equation as
y = b0 + b1 x1 + b2 x2 + b3 x3 + b4 x4 + ut
and we can test the assumption of autocorrelation or we can test whether the disturbances are autocorrelated.
To test the autocorrelation, we can follow the steps below:
(i) Estimate the regression model above using ordinary least square approach/OLS:
sysuse auto, clear
gen t=_n
tsset t
reg price rep78 trunk length
. reg price rep78 trunk length
Source |       SS           df       MS      Number of obs   =        69
-------------+----------------------------------   F(3, 65)        =      6.42
Model |   131790806         3  43930268.8   Prob > F        =    0.0007
Residual |   445006152        65   6846248.5   R-squared       =    0.2285
-------------+----------------------------------   Adj R-squared   =    0.1929
Total |   576796959        68  8482308.22   Root MSE        =    2616.5
------------------------------------------------------------------------------
price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
rep78 |   578.7949   348.6211     1.66   0.102    -117.4495    1275.039
trunk |  -31.78264   108.8869    -0.29   0.771    -249.2447    185.6794
length |   70.17701   22.01136     3.19   0.002     26.21729    114.1367
_cons |  -8596.181   3840.351    -2.24   0.029    -16265.89   -926.4697
------------------------------------------------------------------------------

(ii)  Now calculate the residuals from the above regression:
predict errors, res
(iii)  Run another regression by inserting lagged residuals or the lag values of error terms, (errors) as predicted from above regression model into a regression model of the residuals as a dependent variable. Our regression model will be estimated through the following regression code in Stata: reg errors rep78 trunk length l.errors. We can consider this regression as auxiliary regression. The results from this auxiliary regression is given below
. reg errors rep78 trunk length l.errors
Source |       SS           df       MS      Number of obs   =        63
-------------+----------------------------------   F(4, 58)        =      4.66
Model |   104717963         4  26179490.9   Prob > F        =    0.0025
Residual |   325759819        58   5616548.6   R-squared       =    0.2433
-------------+----------------------------------   Adj R-squared   =    0.1911
Total |   430477782        62  6943190.04   Root MSE        =    2369.9
------------------------------------------------------------------------------
errors |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
rep78 |  -47.23696   332.1243    -0.14   0.887    -712.0559     617.582
trunk |   62.68025   103.8779     0.60   0.549    -145.2539    270.6144
length |   9.373147   20.97416     0.45   0.657     -32.6112    51.35749
|
errors |
L1. |   .5273932   .1225638     4.30   0.000      .282055    .7727313
|
_cons |  -2375.671   3741.339    -0.63   0.528    -9864.774    5113.432
------------------------------------------------------------------------------

(iv) Using the estimated results from auxiliary regression above, note the the R-squared value and multiply it by the number of included observations:
scalar N=_result(1)
scalar R2=_result(7)
scalar NR2= N*R2
scalar list N  R2  NR2
N =         63
R2 =  .24325986
NR2 =  15.325371

(v)  Now, the null hypothesis of BP test is that there is no autocorrelation, we can use the standard Chi-Square distribution to find the tabulated values of the Chi-Square to check if the null hypothesis of no autocorrelation needs to be rejected. According to theory, the Chi-Square statistic calculated using the NRSquare approach above, the test statistic NR2 converges asymptotically where degrees of freedom for the test is s which the number of lags of the residuals included in the auxiliary regression and we have included 1 lagged value of errors/residuals so degrees of freedom in this case is 1. We can use Stata conventional functions for distribution to determine the tabulated values at 5% level of significance using the following code:
scalar chi151=invchi2tail(1, .05)
scalar list chi151
chi15 =  3.8414598
We got from the above tutorial in tests for autocorrelation, NR2 = 15.325371 > 3.84 = Chi-Square (1, 5%). As the calculated value of Chi2 is greater than tabulated values of Chi2, so we reject the null hypothesis of no autocorrelation on the disturbances.

Applied Statistics for Business and Management

Statistics for Business and Management professionals and academic researchers is of critical importance. Although there are specialized courses in Statistics for the students of Business and Management but none of the courses offer specialization in either, Stata, Eviews or SPSS and other computer packages. The current course introduces the application of Stata, Eviews and SPSS and other computer application while emphasis is laid on the development of theoretical background of the learners as well as with strong application of the techniques in Statistics for Business. The contents of the course offered to students include (but are not limited) to:
Basic Data Management and Presentation
Hypothesis Testing and Research Problems
Causal Relationships, Regression Analysis and Applications
Qualitative Data Analysis for Binary and Multi-chotomous variables

Also the students of the course would be provided additional support via other mediums like phone, SMS, email beside the traditional and favorite WizIQ!
This course will benefit the students of MBA, BBA and Doctorate students in Business and Management courses have to study and develop the skills of Quantitative Data Analysis. This course will master these students in their Statistical courses.
Course Highlights
Apply Statistical Techniques to real world data
Apply Data Analysis to relevant research problems
Wide range of contents which are usually required in broader contexts

The course package:
15 LIVE online classes
Duration: Two to Four Weeks (Negotiable)
All Days: 0500 PM to 1000 PM GMT (1000 PM to 0200 AM Pakistan Times)
The course outline:
Deterministic Data and Random Data
Multivariate Tables, Scatter Plots and D Plots
Measures of Association for Continuous Variables
Measures of Association for Ordinal Variables
Measures of Association for Nominal Variables
Estimating Data Parameters
Parametric Tests of Hypotheses
Non-Parametric Tests of Hypotheses
Statistical Classification
Multiple Regression
General Linear Regression Model
General Linear Regression in Matrix Terms
Multiple Correlation
Inferences on Regression Parameters
ANOVA and Extra Sums of Squares
Polynomial Regression and Other Models
Building and Evaluating the Regression Model
Principal Components
Dimensional Reduction
Principal Components of Correlation Matrices
Factor Analysis
Survival Analysis
The Exponential Model
The Weibull Model
The Cox Regression Model

Recommended books for this course:
Probability and Statistics, Fourth Edition by Morris H. DeGroot & Mark J. Schervish
SPSS for Introductory Statistics Second Edition by Morgan et al
A First Course in Business Statistics, Eight Edition by JAMES T. McCLAVE, P. GEORGE BENSON & TERRYSlNClCH
Note: Students must purchase these books themselves. These are not available as part of the course. Discount coupons may be asked from the teacher who would be happy to share if any available right now.

Econometric Models Selection

In this simple outline on the issue of commonly raised douts on how to select an economic model, I would like to sum the issue in this 300 words article. Econometric Models Selection is one key problem for all. The model selection is based on the type of data and type of variables. Here, we mention an order following which helps an econometric model strategy development for us. We assume to use Panel data to develop econometric model selection strategy. Initially, to determine the right set of models, the nature of data structure for the panel data follows like this: A. Check data structure. 1. if N>T, then follows B series of steps. 2. If T>N, then follow the steps in C series.

If we assume the panel data has N sufficiently greater than T, then simple panel data or instrumental/GMM models for panel data can be achieved using the following order. This helps in Econometric Models selection looking into the key steps of the econometric analysis of the given panel data.

For Case 1, follow these steps.
B1. The first stepOLS
B2. RE/FE
B3. Hausman
B4. Assumptions of RE or FE from B3.
B5. Endogeneity Tests
B6. Instrumental Regression
B7. GMM if seens dynamics/autocorrelation in B4.

If the data at hands is such that T is sufficiently larger than N, the panel time series structure will be evidenced and hence our econometric models selection strategy will follow the following procedures. The unit roots and cointegration is for panel time series data which are also sometimes considered as longer time panel data or longer panel data. In such cases, the time series properties in estimators are heavily observed and hence the steps to select an econometric models selection strategy will include these.
For Case 2, follow these steps
C1. Unit Root
C2. Cointegration
C3. VECM


Writing First Program In R

R provides an extensive environment to write all kinds of modelling and statistical programs at the cost of expertise only while nominally the price is less than 0 for you if you can get to work with it because in spite of paying for purchasing costly software, you can earn using R programming skills while spending nothing. In the following article, we will begin our first program using R.
Let us assume we want to test if a number is positive or negative. To test this using R, we need to define what a positive number is and what a negative number looks like in Roman style. It is well known that positive numbers are greater than 0 or 0 itself is a positive number. On the other hands, a negative number is always less than 0. So define the following:
1) A positive number >0 or =0
2) A negative number <0
Now plan what R will require to know to state a number of any value, an integer, a whole number of a rational number being positive or negative. We need to program it. Each R program starts with the definition of the program.
We define our program showing if the number is positive or negative using R. The program will begin typing:
givelesettter = function(thenumber){
The left side of the above definition gives the name of the program, the right side defines what the actual is, function() states R will use left side name as a function to evaluate any number being positive or negative. The syntax will start after the curly braces {.
After that, we will define the above statements for showing the number if positive and should be at least equal to 0 or greater than 0. So we write the first condition to apply in R for our program to use which should be:
if (thenumber >= 0){
followed the curly braces to define the logical value stating the nature of the number which we defined as positive number in the following line of the programe.
theletter = "Positive Number"
and end this logic with the curly bracket as below.
}
After defining the logic for a number to be positive, we can define any number less than 0 as negative using the following piece of code.
if (thenumber < 0){
theletter = "A Negative Number"
}
We end up our code/program by stating return() argument which will display the results of a right inputted argument in the program and put an end of program curly bracket.
return(theletter)
}
After writing our first simple program, we can do a practice of what have intended to by defining a number whether positive or negative. Before doing this, we have to save the written program in a text file with extension .R in the working directory of R. To use the function or program we will set the working directory using:
setwd(“address of working directory folder”) like setwd(“E:/rwork”)
And load the program by typing in R workspace as:
source(“myprograme.R”)
See the example program as given:
## is used to state that here Begins the Comments
## The code will look like the following
giveletter = function(thenumber){
if (thenumber >= 0){
theletter = "Positive Number"
}
if (thenumber < 0){
theletter = "A Negative Number"
}
return(theletter)
}
Type the following example to test the working of the code.
setwd(“E:/rwork”)
source(“giveletter.R”)
giveletter(30) ## will return the response as Positive Number
giveletter(-30) ## will return the response as Negative Number

ARDL and Unit Root Testing using Eviews

ARDL Cointegration using Eviews 9

To estimate ARDL using Eviews 9 on Time Series Data, first open the data file/workfile, Click on your DV, press control key on keyboard, now left click to select all your IVs one by one, once selected then right click on any selected variables and open these as Equations. Once you get the Methods window in Eviews, go the methodology selection from Estimation Setting near to bottom, select ARDL from the list and click Okay. Now you cans elect the lags of DV and IV and any other options for the the methods. You can click on the OK button to get your estimates.

Augmented Dickey Fuller Unit Root Test using Eviews

Augmented Dickey Fuller Unit Root Test using Eviews We can test a time series variable for Unit Root Test following Augmented Dickey Fuller Approach in Eviews following the steps outlined below. First of all open the Eviews workfile or the Excel data in Eviews, then right click on any of the variables we would like to test for unit root based on Augmented Dickey Fuller Approach and click on Open. The series opens in spreadsheet in Eviews. We can click on View in the left upper corner of the new spreadsheet window in Eviews. Then we can click on Unit Root Test in this list that pops down by clicking on View tab. This opens the dialogue box as shown in the inserted screenshot from Eviews itself, we can see that it has mainly four sections. Main section is related to selecting the test type. The Eviews produces unit root test results following 6 methods. We will select the Augmented Dickey Fuller as test type. The we will select either the Level, Difference or Second Difference. Next we can select either to include intercept or both of trend and intercept or none. On the right side of the same window, we can either ask Eviews to use lags automatically or we can insert manually the maximum lags into the model to base our unit root test on. Once we select everything as per our assumed approach to test a series for unit root using Augmented Dickey Fuller Approach, we can click on OK to get the test results.

Phillips Perron Unit Root Test using Eviews

Phillips Perron (PP) Unit Root Test using Eviews We can test a time series variable for Unit Root Test following Phillips Perron (PP) Approach in Eviews following the steps outlined below. First of all open the Eviews workfile or the Excel data in Eviews, then right click on any of the variables we would like to test for unit root based on Phillips Perron (PP) Approach and click on Open. The series opens in spreadsheet in Eviews. We can click on View in the left upper corner of the new spreadsheet window in Eviews. Then we can click on Unit Root Test in this list that pops down by clicking on View tab. This opens the dialogue box as shown in the inserted screenshot from Eviews itself, we can see that it has mainly four sections. Main section is related to selecting the test type. The Eviews produces unit root test results following 6 methods. We will select the Phillips Perron (PP) as test type. The we will select either the Level, Difference or Second Difference. Next we can select either to include intercept or both of trend and intercept or none. On the right side of the same window, we can either ask Eviews to use lags automatically or we can insert manually the maximum lags into the model to base our unit root test on. Once we select everything as per our assumed approach to test a series for unit root using Phillips Perron (PP) Approach, we can click on OK to get the test results.

KPSS Unit Root Test using Eviews

KPSS Unit Root Test using Eviews We can test a time series variable for Unit Root Test following Kwiatkowski-Phillips-Schmidt-Shin Approach in Eviews following the steps outlined below. First of all open the Eviews workfile or the Excel data in Eviews, then right click on any of the variables we would like to test for unit root based on KPSS Approach and click on Open. The series opens in spreadsheet in Eviews. We can click on View in the left upper corner of the new spreadsheet window in Eviews. Then we can click on Unit Root Test in this list that pops down by clicking on View tab. This opens the dialogue box as shown in the inserted screenshot from Eviews itself, we can see that it has mainly four sections. Main section is related to selecting the test type. The Eviews produces unit root test results following 6 methods. We will select the KPSS as test type. The we will select either the Level, Difference or Second Difference. Next we can select either to include intercept or both of trend and intercept or none. On the right side of the same window, we can either ask Eviews to use lags automatically or we can insert manually the maximum lags into the model to base our unit root test on. Once we select everything as per our assumed approach to test a series for unit root using KPSS Approach, we can click on OK to get the test results.

Ng-Perron Unit Root Test using Eviews

We can test a time series variable for Unit Root Test following Ng-Perron Approach in Eviews following the steps outlined below. First of all open the Eviews workfile or the Excel data in Eviews, then right click on any of the variables we would like to test for unit root based on Ng-Perron Approach and click on Open. The series opens in spreadsheet in Eviews. We can click on View in the left upper corner of the new spreadsheet window in Eviews. Then we can click on Unit Root Test in this list that pops down by clicking on View tab. This opens the dialogue box as shown in the inserted screenshot from Eviews itself, we can see that it has mainly four sections. Main section is related to selecting the test type. The Eviews produces unit root test results following 6 methods. We will select the Ng-Perron as test type. The we will select either the Level, Difference or Second Difference. Next we can select either to include intercept or both of trend and intercept or none. On the right side of the same window, we can either ask Eviews to use lags automatically or we can insert manually the maximum lags into the model to base our unit root test on. Once we select everything as per our assumed approach to test a series for unit root using Ng-Perron Approach, we can click on OK to get the test results.

Event Analysis using Stata

Advanced Econometrics using Stata training workshop and tutorial. The following Stata code computes simple t-test to test the hypothesis that the variables are different between two time periods. The time periods have been defined based on pre-event and post-event time periods. Thus, we can employ this method to test an event impact on a few variables in a single script of the Stata code which also produces a single table ready for publication or inserting into a word document.

****Open the data file using the following code.
use "E:\Dropbox\Advanced Econometrics using Stata Trainings\data12.dta", clear

****The following two lines of codes creates a few temprary files in Stata to create and save the results in our analysis. The results will be available to call into Stata from the same temprary files.
tempname econometrics_ttest
tempfile ttests
postfile `econometrics_ttest' str12 name t_statistic swilk rankstat pearson fisher using `ttests'

****Now, loop over the given variables to be used for testing the hypothesis. These are the variables which will make the hypothesis to be tested on using Stata and following the simple t-test to verify the post-event variable values has equal greater average than the pre-event time period sample of the same variable.
foreach var of varlist  y x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 {

****t-test is used to test the null hypothesis that the variable has equal means between post-event and pre-event samples for the variables. The -local-command saves the given statistics saved under the Stata routine r().
ttest `var', by(event)
local tstat = r(t)

****Shapiro Wilk test can be employed and produced results can be saved using the following code.
swilk `var'
local swilk = r(W)

****RankSum test is applied using the following code.
ranksum `var', by(event)
local rankstat = r(z)

****Chi-square tests can be employed using the following code.
median `var', by(event)
local pearson = r(chi2)
median `var', by(event) exact
local fisher = r(chi2_cc)

****The following code writes the produced and saved results in -local-s to the given variables defined under -postfile-.
post `econometrics_ttest' ("`var'") (`tstat') (`swilk') (`rankstat') (`pearson') (`fisher')
}

****These lines of the code closes the above temprary files, calculations and saving the results.
postclose `econometrics_ttest'
clear

****Call in the results to see the output of the above code.
use `ttests'

****Change any variable, their labels or create further statisticals.
label var t_statistic "Unequal Variance type t-Statistic"
rename name variable_name
label var variable_name "Variable Tested for Different in Mean Post minus Pre event"

****Now export the table which is already saved in form of a Stata file. This can be a simple table in a standard format.
export excel using "E:\Dropbox\Advanced Econometrics using Stata Trainings\tables_econometrics_research.xls", firstrow(variables) replace

****See the results.
list


Event Study Stata is one of the most interesting area that we can help you with. You can request a freelance project from our top freelancers in Stata to help you complete the event study even for a complex study. We handle Event Study using Stata for all kinds of data from cross sectional, panel data and time series. You can request a Event Study Stata help from our freelancers in econometrics and Stata using the following proforma.

List of Best Econometrics Books

Most of the times, we need to select a book to understand econometrics more easily. In the following, I have shared my personally recommended set of best books for econometrics which I have been reading frequently. These books on econometrics have helped me develop a strong understanding of some core issues in developing econometric skills for research and data analytics, both for academic and professional research for freelance projects.

Mostly Harmless Econometrics: An Empiricist's Companion
Mostly Harmless Econometrics: An Empiricist's Companion (Paperback)
by Joshua D. Angrist (shelved 11 times as econometrics)
Basic Econometrics 4th Economy Edition
Basic Econometrics 4th Economy Edition
by Damodar N. Gujarati (shelved 9 times as econometrics)
Introductory Econometrics: A Modern Approach
Introductory Econometrics: A Modern Approach (Hardcover)
by Jeffrey M. Wooldridge (shelved 8 times as econometrics)
Econometric Analysis
Econometric Analysis (Hardcover)
by William H. Greene (shelved 8 times as econometrics)
Econometrics
Econometrics (Hardcover)
by Fumio Hayashi (shelved 7 times as econometrics)
A Guide To Econometrics
A Guide To Econometrics (Paperback)
by Peter E. Kennedy (shelved 5 times as econometrics)
A Guide to Modern Econometrics
A Guide to Modern Econometrics (Paperback)
by Marno Verbeek (shelved 4 times as econometrics)
The Econometrics of Financial Markets
The Econometrics of Financial Markets (Hardcover)
by John Y. Campbell (shelved 4 times as econometrics)
Time Series Analysis
Time Series Analysis (Hardcover)
by James D. Hamilton
Econometric Analysis of Cross Section and Panel Data
Econometric Analysis of Cross Section and Panel Data (Hardcover)
by Jeffrey M. Wooldridge (shelved 4 times as econometrics)
Applied Time Series Econometrics
Applied Time Series Econometrics (Paperback)
by Helmut Luetkepohl (Editor) (shelved 3 times as econometrics)
Bayesian Econometrics
Bayesian Econometrics (Paperback)
by Gary L. Koop (shelved 3 times as econometrics)
Advanced Econometrics
Advanced Econometrics (Hardcover)
by Takeshi Amemiya (shelved 3 times as econometrics)
Schaum's Outline of Statistics and Econometrics
Schaum's Outline of Statistics and Econometrics (Paperback)
by Dominick Salvatore (shelved 3 times as econometrics)
A Course in Econometrics
A Course in Econometrics (Hardcover)
by Arthur S. Goldberger (shelved 3 times as econometrics)
Econometric Theory and Methods
Econometric Theory and Methods (Hardcover)
by James G. MacKinnon (shelved 3 times as econometrics)
Introduction to Econometrics
Introduction to Econometrics (Paperback)
by Christopher Dougherty (shelved 3 times as econometrics)
Microeconometrics Using Stata
Microeconometrics Using Stata (Paperback)
by A. Colin Cameron (shelved 2 times as econometrics)
Computational Methods in Statistics and Econometrics
Computational Methods in Statistics and Econometrics (Hardcover)
by Hisashi Tanizaki (shelved 2 times as econometrics)
Nonparametric Econometrics
Nonparametric Econometrics (Paperback)
by Adrian Pagan (shelved 2 times as econometrics)
Econometric Methods
Econometric Methods (Hardcover)
by Jack Johnston (shelved 2 times as econometrics)
Introduction to Modern Bayesian Econometrics
Introduction to Modern Bayesian Econometrics (Hardcover)
by Tony Lancaster (shelved 2 times as econometrics)
Econometrics
Econometrics (Paperback)
by Badi H. Baltagi (shelved 2 times as econometrics)
Using Eviews for Principles of Econometrics
Using Eviews for Principles of Econometrics (Paperback)
by R. Carter Hill (shelved 2 times as econometrics)
Limited-Dependent and Qualitative Variables in Econometrics
Limited-Dependent and Qualitative Variables in Econometrics (Paperback)
by G.S. Maddala (shelved 2 times as econometrics)
The Practice of Econometrics: Classic and Contemporary
The Practice of Econometrics: Classic and Contemporary (Hardcover)
by Ernst R. Berndt (shelved 2 times as econometrics)
Financial Econometrics: Problems, Models, and Methods
Financial Econometrics: Problems, Models, and Methods (Hardcover)
by Christian Gourieroux (shelved 2 times as econometrics)
Estimation and Inference in Econometrics
Estimation and Inference in Econometrics (Hardcover)
by Russell Davidson (shelved 2 times as econometrics)
Analysis of Panel Data
Analysis of Panel Data (Paperback)
by Cheng Hsiao (shelved 2 times as econometrics)
Microeconometrics: Methods and Applications
Microeconometrics: Methods and Applications (Hardcover)
by A. Colin Cameron (shelved 2 times as econometrics)
Introduction to Econometrics (Addison-Wesley Series in Economics)
Introduction to Econometrics (Addison-Wesley Series in Economics)
by James H. Stock (shelved 2 times as econometrics)
Principles of Econometrics
Principles of Econometrics (Hardcover)
by R. Carter Hill (shelved 2 times as econometrics)
Learning and Practicing Econometrics
Learning and Practicing Econometrics (Hardcover)
by William E. Griffiths (shelved 2 times as econometrics)
Econometric Models And Economic Forecasts
Econometric Models And Economic Forecasts
by Robert S. Pindyck (shelved 2 times as econometrics)
Analysis of Financial Time Series
Analysis of Financial Time Series (Hardcover)
by Ruey S. Tsay (shelved 2 times as econometrics)
Poverty Dynamics: Interdisciplinary Perspectives
Poverty Dynamics: Interdisciplinary Perspectives (Hardcover)
by Tony Addison (Editor) (shelved 1 time as econometrics)
Student Solutions Manual To Introductory Econometrics
Student Solutions Manual To Introductory Econometrics
by Jeffrey M. Wooldridge (shelved 1 time as econometrics)
Econometric Methods
Econometric Methods (Hardcover)
by Manoranjan Dutta (shelved 1 time as econometrics)
Evolutionary Dynamics And Extensive Form Games
Evolutionary Dynamics And Extensive Form Games (Hardcover)
by Ross Cressman (shelved 1 time as econometrics)
Theory of econometrics
Theory of econometrics
by A. Koutsoyiannis (shelved 1 time as econometrics)
Mastering 'Metrics: The Path from Cause to Effect
Mastering 'Metrics: The Path from Cause to Effect (Hardcover)
by Joshua Angrist (shelved 1 time as econometrics)
Data Analysis Using Regression and Multilevel/Hierarchical Models
Data Analysis Using Regression and Multilevel/Hierarchical Models (Paperback)
by Andrew Gelman (shelved 1 time as econometrics)
Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models
Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Hardcover)
by Julian James Faraway (shelved 1 time as econometrics)
Probability and Statistics
Probability and Statistics (Paperback)
by Morris H. DeGroot (shelved 1 time as econometrics)
Introduction to Bayesian Econometrics
Introduction to Bayesian Econometrics (Hardcover)
by Edward Greenberg (shelved 1 time as econometrics)
Introductory Econometrics with Applications
Introductory Econometrics with Applications (Hardcover)
by Ramu Ramanathan (shelved 1 time as econometrics)
Mastering 'Metrics: The Path from Cause to Effect
Mastering 'Metrics: The Path from Cause to Effect (Kindle Edition)
by Joshua D. Angrist (shelved 1 time as econometrics)
Mathematical Statistics and Data Analysis
Mathematical Statistics and Data Analysis (Hardcover)
by John A. Rice (shelved 1 time as econometrics)
Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS
Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS (Hardcover)
by John Kruschke (shelved 1 time as econometrics)
Bayesian Data Analysis
Bayesian Data Analysis (Hardcover)
by Andrew Gelman (shelved 1 time as econometrics)

Panel Data, Time Series and Cross Sectional Data

If you have on entity (N=1) like one country, one company, one person etc and you collect data about this entity for many time periods (T=many), it is called Time Series Data. For example if we download data on GDP, Consumption, Investment, Savings, Exports and Imports for from world bank indicators (data.worldbank.org) for Pakistan only and 30 year, this will be N=Pakistan and T=(1970 to 2000) which defines the data is time series data.

If you many entities (N=many) like 10 countries, 100 companies and 1000 persons etc and you collect data about these entities for one time period (T=1), then it is called cross sectional data. Similar to earlier example of (World Bank Data), if we download data for 50 countries for only year 2010, then it becomes cross section data. For example, if I collect data for GDP, Consumption, Investment, Savings, Exports and Imports for Afghanistan, Albania, Algeria, American Samoa, Andorra, Angola, Antigua and Barbuda, Arab World , Argentina , Armenia , Aruba , Australia , Austria , Azerbaijan , Bahamas, The , Bahrain , Bangladesh , Barbados , Belarus , Belgium , Belize , Benin , Bermuda , Bhutan, Bolivia , Bosnia and Herzegovina , Botswana , Brazil , British Virgin Islands, Brunei Darussalam , Bulgaria, Burkina Faso for the year 2010, then it can be cross sectional data.

If you have many entities (N=many) and collect data about these many entities for many time periods (T=many), it is called Cross Sectional Time Series data. So, if we collect data onGDP, Consumption, Investment, Savings, Exports and Imports for Afghanistan, Albania, Algeria, American Samoa, Andorra, Angola, Antigua and Barbuda, Arab World , Argentina , Armenia , Aruba , Australia , Austria , Azerbaijan , Bahamas, The , Bahrain , Bangladesh , Barbados , Belarus , Belgium , Belize , Benin , Bermuda , Bhutan, Bolivia , Bosnia and Herzegovina , Botswana , Brazil , British Virgin Islands, Brunei Darussalam , Bulgaria, Burkina Faso for the year 2000 to 2010, it will be panel data. Note, panel data might include T>N or N>T so selection of a relevant method can decided here:

panel data methods

panel data methods

Note panel data is what entities remains same over the time periods. Logitudinal data might contain some entities to appear only in one or the other time periods.

Why to Select Econometrics Specialists?

All econometrics specialists well aware of the theories of economics related to consumer behaviour, market structure and macroeconomic systems. The econometrics specialists usually work within these domains of economic thinking and plays with the data tools on daily basis. The problems and recent issues are well in their lines of duty because they are the main force within the organizations and teams to define the system for the evaluation and prediction of the future uncertain events to a greater extent. With this simple background, econometrics specialists usually work within the quality agreements and deadlines for each project and hence our econometrics specialists offer 100% gauranteed satisfaction for each project to each client.

Econometrics Specialists, ranked among the top 1% freelancers in Elance.com, Freelancer.com, Odesk.com, Guru.com and Fiverr.com have completed projects in the following areas. See more specific details about the projects detals and reviews for each project at our workplace here. The list is compiled for reference only to show you what kind of freelance skills have been acquired by the for your convinience.

  1. Stata programming for econometric analysis
  2. Econometric Analysis
  3. Time Series Analysis for Economic Data using Eviews
  4. Econometric Analysis using Eviews
  5. Forecasting using Stata
  6. Forecasting using R
  7. Forecastiing using Excel
  8. SSA using JavaScript
  9. Sales Forecasting
  10. Panel Data Analysis
  11. Pricing System Analytics and using Nonlinear Regression Models in Stata
  12. Selecting of Instruments and GMM Regression using Stata Loops
  13. Stata Programming for Healthcare Analytics
  14. Healthcare Analytics for Insurance Premiums through Logistic Regression/Odds Ratio/Marginal
  15. Education Research, Qualitative Data Analysis using Nvivo10
  16. PhD Econometrics Supervision in Econometrics for Healthcare
  17. Data Analysis using R for Public Health Research
  18. Developing PHP based Chi-Square tests using A/B Test Approach in WebAnalytics
  19. Teaching Econometrics using Stata to PhD class
  20. Political Economy and Financial RiskMetrics using Stata
  21. Testing Endogeneity in Panel Data using Stata
  22. Developing a do file for Dynamic Analysis of Data using Correlation and Regression across industries.
  23. Solving custom questions in Econometrics using Stata
  24. Solving custom questions in Econometrics using Eviews
  25. Statistical Analysis of Education data using SPSS
  26. Application of SEM using Stata to Marketing Survey
  27. Application of SEM using Stata to Students Admissions and Droppout Data
  28. Application of SEM using AMOS/SPSS and SEM using Stata for Business Research
  29. Qualitative Analysis of HR Interviews using Nvivo10
  30. Teaching Qualitative Analysis using Nvivo10 to PhD in Organizational Management and HR
  31. Developing Econometric Toolbox using Stata
  32. Econometric Analysis of Brain Drain data using Eviews and Stata
  33. Analysis of Corporate Governance and Financial Performance using Stata for Panel Data (Note, this has been ordered by more than 10 PhD candidates and all have completed their thesis with highest achievements).
  34. Analysis of HR data using SPSS and application of Exploratory Factor Analysis and Confirmatory Factor Analysis/SEM
  35. Partial Least Square using SmartPLS
  36. Moderation SEM using WarpPLS
  37. ANOVA using SPSS for Web Analytics

Econometrics Specialists Skills and Experience

Econometrics Specialists are fully committed to complete each project. Our committment is fully supported by our academic and professional experience and the technical skills in Econometrics and Statistics with Stata, Eviews, SPSS, R, Nvivo10/11, Python, Matlab, Minitab, SAS, RATS, Gretl and HLM. In the following, we can only sum up our experience in the keywords for you.

  1. Assistant Professor in Econometrics and Statistics
  2. Assistant Professor in Economics
  3. Assistant Professor in Research Methods
  4. Assistant Professor in Quantitative Techniques
  5. Professionally trained in Stata, SPSS, R, Matlab, Minitab, RATS, SAS, Excel for Econometrics, Eviews
  6. Professionally trained in Research Methods, Writing Styles, Idea Generation and Publication Management
  7. Professional trainer in Data Analysis using Stata, SPSS and Eviews
  8. Freelance Econometrics Specialist since 2009
  9. Completed more than 500 projects specifically as Econometrics Specialist