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Evaluate the ols assumptions required prior to initiating

Assignment Help Econometrics
Reference no: EM133512372

Question A:

Investigate the dependent variable (GDP per capita):

1. Calculate the descriptive statistics for the outcome variable?

2. How do you interpret the disparity between the median and mean values of the dependent variable? Plot a histogram for GDP per capita and save the graphic (ensure you add this to the answers - and do the same for any subsequent request for diagrams or tables).

3. What's the maximum value? Which nation has the top spot in the dataset in terms of wealth? How do you interpret this? What does it indicate about the income metric we've chosen?

Question B:

Analyze the explanatory variables:

1. Construct a table showing correlations between the dependent variable and all the independent variables. What's the relationship between RDE and GDP? Is this expected?

2. Observe the direct correlation between GDP and RDE. How does the relationship appear now? Can you speculate on the reasons behind it?

3. Inspect and present the correlations between each pair of explanatory variables. Which pair indicates the highest risk of collinearity?

Question C:

Based on the data given:

1. Evaluate the OLS assumptions required prior to initiating the empirical analysis. If these aren't met, detail and explain the necessary measures to address these issues.

2. Determine the most fitting econometric model for GDP per capita. Which independent variables account for a significant proportion of the variation in the dependent variable? Justify why certain independent variables are chosen for the model over others.

3. After deducing the optimal OLS model, revisit the OLS assumptions. If these post-estimation assumptions are not met, outline and explain the remedial steps.

4. What insights does the model provide regarding the factors influencing GDP per capita?

5. In the chosen econometric model for GDP per capita, identify the y-intercept. What does this value signify in practical terms? Is the y-intercept plausible in terms of representing a potential real-world scenario?

6. Are there any variables left out that should be taken into account?

7. Highlight any potential constraints or limitations in our empirical analysis that need consideration.

Reference no: EM133512372

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