In-Class-Exercise 1

APPLIED ECONOMETRICS AND ECONOMIC MODELLING EXERCISE: GROWTH DATASET ANALYSIS

• Duration: 1 hour 45 minutes
• Rules: Open-book, individual, No AI tools (e.g., ChatGPT, Deepseek).
• Process: Each student must briefly show me their progress during the session.
• Questions: Priority given to first-time askers—please be concise.
• Submit both the GRETL session file and the pdf file.

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Dataset Description

The data set Growth contains data on average growth rates over the period 1960-1995 for 65 countries, along with variables that are potentially related to growth.

The variables are defined as:

• growth: average annual growth rate, 1960-1995
• rgdp60: real GDP per capita in 1960
• tradeshare: average share of trade in GDP from 1960 to 1995, measured as the sum of exports plus imports, divided by GDP
• yearsschool: average years of schooling in 1960
• rev_coups: average number of revolutions and coups per year
• assasinations: average number of political assassinations per year
• oil: = 1 if oil accounted for at least half of exports in 1960

The data set is a subset of the data used in Robert J. Barro’s 1997 Economic Growth book. The original data sources are listed in the data appendix to his book.

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Questions

1. The data file Growth contains data on average growth rates from 1960 through 1995 for 65 countries, along with variables that are potentially related to growth. In this exercise, you will investigate the relationship between growth and trade.

   1. Construct a scatterplot of average annual growth rate (Growth) on the average trade share (TradeShare). Does there appear to be a relationship between the variables?

   2. One country, Malta, has a trade share much larger than the other countries. Find Malta on the scatterplot. Does Malta look like an outlier?

   3. Using all observations, run a regression of Growth on TradeShare. What is the estimated slope? What is the estimated intercept? Use the regression to predict the growth rate for a country with a trade share of 0.5 and with a trade share equal to 1.0.

   4. Estimate the same regression, excluding the data from Malta. Answer the same questions in (c).

   5. Plot the estimated regression functions from (c) and (d). Using the scatterplot in (a), explain why the regression function that includes Malta is steeper than the regression function that excludes Malta.

   6. Where is Malta? Why is the Malta trade share so large? Should Malta be included or excluded from the analysis?

2. Excluding the data for Malta, run a regression of Growth on TradeShare.

   1. Is the estimated regression slope statistically significant? This is, can you reject the null hypothesis H0: β2 = 0 vs. a two-sided alternative hypothesis at the 5% and 10% significance level.

   2. What is the p-value associated with the coefficient’s t-statistic?

   3. Construct a 95% and 90% confidence interval for β2.

3. Excluding the data for Malta, carry out the following exercises.

   1. Construct a table that shows the sample mean, standard deviation, and minimum and maximum values for the series Growth, TradeShare, YearsSchool, Oil, Rev_Coups, Assassinations, and RGDP60. Include the appropriate units for all entries.

   2. Run a regression of Growth on TradeShare, YearsSchool, Rev_Coups, Assassinations, and RGDP60. What is the value of the coefficient Rev_Coups? Interpret the value of this coefficient. Is it large or small in a real-world sense?

   3. Use the regression to predict the average annual growth rate for a country that has average values for all regressors.

   4. Repeat (c) but now assume that the country’s value for TradeShare is one standard deviation above the mean.

   5. Why is Oil omitted from the regression? What would happen if it were included?

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