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As a member of Data Science Central (DSC), American Economic Association (AES), Royal Economic Society (RES), International Health Economics Association (iHEA) and The Econometrics Society, I have been working closely with top academics in Economics, Econometrics, Statistics and Research Methods. Also, I am providing supervision in Applied Econometrics and Statistics to PhD candidates in Project Management, Business Management, Finance, Corporate Governance and Social Sciences.
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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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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.
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:
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"
Type the following example to test the working of the code.
giveletter(30) ## will return the response as Positive Number
giveletter(-30) ## will return the response as Negative Number

Time Series Econometrics using Stata

Time Series Data is frequently evaluated by faculty and professional researchers to deduce short and long run relationships, to test the trends and volatility, to find any nonlinearities in the relationships or finding any eventual factors affecting the nature of relationships and hence all of the above conditions as affecting the power of their forecasting power. The course introduces and develops from basic to the advanced techniques to deal with different types of  time series data, whether low frequency (yearly, quarterly, monthly, weekly) or high frequency (daily or hourly), identify the exact model to apply and hence deduce the exact relationship for forecasting purposes. Furthermore, the course will help the audience to develop advanced Econometric modeling skills also applicable in a range of areas from Economics to finance, from social to political sciences, from energy to environmental sciences, education and health sciences.

Course Outline

The course aims to achieve the following goals:

1.      Introduce you the basic econometric methods for time series data
2.      Introduce you to advanced techniques to deal with these models and their application
3.      Introduce the use of Stata and R more rigorously for time series models
4.      Provide a detailed introduction to latest theoretical development and applications
5.      Provide extension of developed models to new areas of research

Completing the course will enable you to:

1.      Learn how to use a model suitable for specific type of data
2.      Learn how to evaluate a model if suitable for a type of data
3.      Distinguish the modeling strategy between different types of data
4.      Know the features of Stata and R for time series modeling, estimation and forecasting
5.      Learn to Write and Report Results for Papers, Theses and Dissertations

The course is developed for the following groups:

1.      MS/PhD Students in Economics, Finance, Statistics and Other Social Sciences
2.      Academic Researchers of Universities and Colleges
3.      Researchers of R&D and Policy Research Organizations
4.      Research Officers of NPO and Social Organizations
5.      Consultants, Trainers And Policy Analysts
6.      Management, Exectives of Marketing and Social Research Organizations

100 place in total available for 5 groups of 20 participants. Groups will be formed on the basis of fields of studies and research. Please fill the registration on

·         Topic 1: Stationary Time-Series Models
·         Topic 2: Testing for Trends and Unit Roots
·         Topic 3: Modeling Volatility
·         Topic 4: Multiequation Time-Series Models
·         Topic 5: Cointegration and Error-Correction Models
·         Topic 6: Nonlinear time-series models

The course fee is £500. 15% discount fee will be charged for each member of a group of registrations up to 5 participants. Groups of higher than 5 registrations will be discounted by 30% for each participant.

Supporting Material
The learning materials include EBooks, examples of MS Excel documents and related lecture materials will be provided in form of datasets.

There is Test, Assignment after each Week. Passing this will enable you to request for PASS certificate of the course mentioning your topics, assignment topics, grades in tests and assignments. Hard Copy of the Certificate will mailed at additional £10 to any destination across the world. If you do not need to request your certificate in paper, no charges are applicable. Soft/Scanned copies will be provided free of cost via email and downloadable from course portal.