examining a data set related to education or health using statistical models etc. Impact of some characteristics on our model etc. With R Language

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

1.     The final paper should consist of

For example, examining a data set related to education or health using statistical models etc... Impact of some characteristics on our model... etc. ( With R Language )..

(Time series models, Models with Binary Dependent Variables, Ordered Choice Models, Models for Count Data, Multinomial Logit, Conditional Logit, Limited Dependent Variables (tobit), Panel Data Models .... etc)

Note: Just one model above for should be used for the real data set belonging to the problem definition you find. Whichever model is chosen, extra requirements must also be do. ( Page 2, 3 and 4  )

It is enough to apply one of these models. My prefer is "Models with Binary Dependent Variables" but you can change.

The information on the page must be written in the order specified in the blue part.

The problem and hypothesis should be clearly explained.

The method to be chosen for the problem should be briefly mentioned.

The data set must be obtained from an accessible website. (worldbank, kaggle, etc ...)

Whether we accept the hypothesis or not should be stated for reasons. ( conclusion  part )

(You can already find these extra special requirements for each model below)

If we accept this model, the additional requirements requested from me during the use of this model areas under the red heading.  ( Page 2, 3 and 4 )

1       The final paper should consist of 

            a) Title, Authors, Date

            b) Abstract

            c) Introduction

            d) Literature review

           e) Data

            f) Method/Model

           g) Results

           h) Findings

          i) Bibliography

          j) Appendix

2.      Formal language

3.      Abstract should comprise the main aims of the paper, short description of the method used, and main findings. Cf: https://en.wikipedia.org/wiki/Abstract_(summary) and https://www.springer.com/ (link)

4.      Introduction. Describe the problem. Introduce the main and the secondary hypotheses. Explain the importance of the selected topic. https://www.springer.com/ (link 2)

5.      Data. Describe your data. Where do they come from? Describe the data transformations. If any data was removed, give the reason. A plot, histogram, or some form of data visualisation might be attractive to a reader and might help to better understand your data.

6.      Method/Model. General to specific approach.

7.      Results. Verify your hypotheses. Please formulate an explanation why the hypothesis was re- jected, if any. Publication quality table is necessary.

8.      Findings. Repeat the findings. The next possible ways of handling the topic/problem.

9.      Appendix. R-code with comments.

10.  Title, data, data description, one main hypothesis, one secondary hypothesis 

METHODS SHOULD BE!

Time series models – ARDL models (If this model is selected, the requirements are as follows.)

a) stationarity tests;

b) general-to-specific approach to variables selection;

c) parameters interpretation; d) hypotheses verification;

e) Ramsey-Reset test - linear form of the relationship;

f) Breusch-Pagan’s and White’s tests – homoscedasticity;

g) Breusch-Godfrey test – no autocorrelation;

h) compare the final ARDL model with ARIMA model.

2. Cointegration analysis (If this model is selected, the requirements are as follows.)

a) stationarity tests;

b) cointegration testing;

c) general-to-specific approach to variables selection;

d) parameters interpretation - short-term and long-term part;

e) hypotheses verification;

f) Ramsey-Reset test - linear form of the relationship;

g) Breusch-Pagan’s and White’s tests – homoscedasticity; h) Breusch-Godfrey test – no autocorrelation.

3. Models with Binary Dependent Variables ( If this model is selected, the requirements are as follows.)

2. a)  estimation of linear probability model (OLS with White’s robust matrix), logit model, and probit model, selection of significant variables;
3. b)  choice between logit and probit on the basis of information criteria;
4. c)  general-to-specific method to variables selection;
5. d)  at least one nonlinear relationship (variable to a power) and interaction between variables;
6. e)  present the general model (LPM, logit, and probit), the final model (the specif model) in one quality table. If there is space, at least one intermediate model might be presented;
7. f)  calculation and interpretation of marginal effects for the final model (from the general-to- specific approach);
8. g)  calculation and interpretation of odds ratios;
9. h)  perform the linktest and interpret the result;
10. i)  interpretation of the appropriate R2 statistics (R2 McKelvey-Zavoina, count R2, and adju- sted count R2;
11. j)  hypotheses verification;
12. k)  perform the Hosmer-Lemeshow and alike tests.

Attachments:

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