Time Series Analysis Assignment

PART 1 (Total Marks: 60%)

In part 1, you are going to carry out a time series analysis to examine the relationship between air pollution and GDP. Select any one country having at least 30 years of observations for all the variables of interest. Divide CO2 emission and GDP by population to obtain per-capita data. In your analysis, CO2 emission per capita will be the dependent variable and GDP per capita will be the explanatory variable.

1. A brief review of existing literature on the relationship between GDP growth and pollution. (10%). (Hint: Search articles using Google scholar)
2. Present some descriptive statistics/charts, highlighting the main features and trend. What does this initial analysis suggest about the stationary of the data? (10%) (Hint: Summary statistics, line chart, auto correlation function and partial auto correlation function)
3. Take the logarithmic transformation of the data and carry out tests for stationarity, using Dickey Fuller and Augmented Dickey Fuller tests. Discuss the findings. Insert a footnote providing at least one reason for taking the logarithmic transformation of your data. (15% )
4. Explain why you might want to carry out a cointegration test. Then carry out the Engle and Granger cointegration test. (10%)
5. Based on the results of the cointegration test, either estimate an Error Correction Model (ECM) or a model in first differences, as appropriate. Discuss your results (15% ).

PART 2 (Total Marks: 40%)

In part 2, you are going to carry out a panel data analysis to examine the relationship between air pollution and GDP. Use the same dependent and explanatory variables as in PART 1. You need to select a total of 15 countries (developed countries/developing counties/mix of countries) to construct a panel data.

1. Estimate the relationship between CO2 emissions and GDP per capita using the panel data estimators and discuss your findings. Can you find any difference in the results produced by the panel data estimators? (15%) (Hint: Pooled OLS, Fixed Effect, First Difference, Random Effect, significance of the slope coefficients, R-square)
2. Test for the presence of heteroskedasticity using either the Breusch-Pagan or the White test. (10%)
3. If you find evidence of heteroskedasticity, re-estimate your model(s) using robust standard errors. Does the use of robust standard errors change your conclusion regarding the statistical significance of your explanatory variable? (10%)
4. How do the panel data results compare with the time series results? (5% ).

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