No word limit.
Simple but concise interpretation of results is also required but nothing theoretical.
The actual r studio lines are required as well,
Looking forward to hearing from you, I really need the help. Thank you.
Your project should be word-processed, well-organised. Your answer (project work) should be concise and focussed.
Relevant econometric results must be typed and reported in Tables. R output should be saved in a file and submitted. DO NOT APPEND R OUTPUT to your project. The project must not be made bulky by appending the R output.
Project for Econometrics and Data Analysis
It is important that you answer all the questions given below. Your work must be clearly presented and concisely explained. This should include, where appropriate, explanation and interpretation of the following:
• Methods and models employed,
• Formulation of the null and alternative hypotheses,
• Hypothesis tests,
• Inference drawn, and
• Explanation of economic intuition of the relationship(s) estimated.
• Unless specified, a conventional 5% significant level is applied through all tests.
Data file: the relevant data file for this section is “data2021.txt”. This file contains data for UK. Download this data. The variables in the file are:
sd = stock of domestic knowledge (i.e., total stock of domestic technology)
sf = stock of foreign knowledge (i.e., total knowledge stock of sample country’s trading partners)
fd = a measure of financial development defined as the ratio of total private sector credit by banks and other financial institution to GDP. A higher ratio implies an increased level of financial development.
gdp = real GDP (Output).
hk = human capital (measured as the number of schooling years of age-group 25-64).
ks = physical capital stock.
lab = labour force employed.
tfp = total factor productivity
yp = real per capita income
All variables are expressed in levels. Data sample is 1970 to 2015. Data frequency is annual.
Consider the following production function (model):
Y_t=aK_t^ß L_t^? H_t^? exp?{e_t} Equation (1)
where e_t is i.i.d. –white noise error, Y_t is real GDP in time t, K_t is physical capital stock, L_t is labour force employed and H_t is human capital. ß, ? and ? are parameters of the production function. Answer the following questions:
Question 1.
Tabulate descriptive statistics (mean, median, maximum, minimum and standard deviation etc.) of all four variables of the model (equation (1)) and describe them carefully. Plot all these data series and comment on their evolution over the sample period. (Marks 10).
Question 2.
Log linearize the production function (equation (1)) and estimate it by the OLS estimator. Interpret the parameters and their statistical significance by using your knowledge of production function. Test if production function follows constant returns to scale on labour and capital stocks. (Marks 10)
Question 3.
Compute and report the following statistics:
Breusch-Godfrey LM test of residual serial correlation,
Breusch-Pagan test of heteroscedasticity,
White’s test of heteroscedasticity,
RESET test of functional form and
Chow test of structural breaks.
In the light of all these diagnostics assess whether your estimated model is reliable. (Marks 20).
Question 4.
Do you find a significant heteroscedasticity in equation (1)? If you do, then report the results of Weighted Least Squares treating each of the regressor, in turn, as the potential cause of heteroscedasticity. Discuss your results. (Marks 10).
Question 5.
Re-estimate the model (1) allowing for the first order residual serial correlation [i.e., AR(1) residual serial correlation] and compare and contrast these results with those found for question (2). (Marks 10)
Question 6:
Economic theory informs us that the level of output (y_t) depends, among other things, on physical capital stock (k_t). Capital stock (k_t) is nothing but the accumulated past investments which in turn depend on the level of output. These imply that y_t and k_t in equation (1) are potentially simultaneously determined. Illustrate the method, report the results and interpret whether k_t could be treated as (weakly) exogenous while estimating equation (1) in this dataset. (Marks 10).
Question 7:
Sequentially implement unit root tests on all the variables of model (1) and clearly interpret your results. Identify the order of integration of all the variables in the model. (Marks 10).
Question 8:
Test whether variables in model (1) are co-integrated. Use Engle-Granger of co-integration tests. Interpret your results. (Marks 10).
Question 9.
Augment Model (1) by the measure of financial development (fd) and test the causality between financial development and economic growth. (Marks 10).
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