MANM467:Provide a brief explanation for the methodology, such as data, the definition of dependent, independent, and control variables: Foundations of Statistics and Econometrics, Assessment, UOS,UK
|University Of Surrey (UniS)
|MANM467:Foundations of Statistics and Econometrics
Content and Structure:
Provide a brief explanation for the methodology, such as data, the definition of
dependent, independent, and control variables, the objective of the analyses, and the baseline model (see the description of the baseline model below.
- Provide a two-way table for summary statistics of the variables for the entire sample and different technology generations subsamples. Provide the correlation matrix of the variables (for the entire sample). Beefily discuss the results.
- Apply a statistical test and evaluate if there is any significant difference (at 0.05 significance level) across the years regarding the unit sale (logged). You may use a relevant graphical illustration to enhance your discussion.
- Inspect the data graphically, such as visual summary statistics across technology generations or years, checking the distribution/skewness of main variables (i.e., dependent and independent variable), pre-checking the possibility of outliers, prechecking the relationship between the dependent and independent variables, the longitudinal trend of the main variables, etc. The details and types of graphs are your decision—the objective is to provide a concise yet informative inspection of the data before running the regression. You may select a few graphs from the list mentioned above (or other graphs), which efficiently describe various aspects of the data.
- Show the trend for console’s market share across periods; each console’s trend with a line; all in one graph. The console’s market share at period t is defined as the console’s unit sale at period t divided by the sum of all console’s unit sale at period.
Main Regression Analysis:
- Conduct an OLS regression to estimate the effect of the number of new games, console’s age, and the number of active consoles in the market on the console’s unit sale while controlling for console’s price, console’s technology generation, console’s RAM capacity, console’s CPU speed, and time-period. This will be the baseline model. The natural logarithm version of unit sale, number of new games, price, RAM capacity, and CPU speed should be used in all models. Other variables should be used as not-logged.
- Carefully interpret and discuss the results (e.g., R-squared, the statistical significance of coefficients and the effect size of independent variables).
- Looking at the baseline model’s results, evaluate whether there is any differential effect of technology generation on the unit sale. You may use a margins plot to enhance your discussion.
- Modify the baseline model to evaluate the differential effect of the number of new games for leader vs non-leader consoles. Based on the results, discuss the statistical significance and effect size of the difference. You may use a relevant graphical illustration to enhance your discussion.
Diagnostics and Robustness Analysis:
- Apply diagnostic analyses on the baseline model to check the potential
heteroskedasticity and apply an appropriate remedy if needed. Briefly compare the new results with the original results of the baseline.
- Investigate the possibility of a quadratic effect of the number of new games (logged) on the unit sale (logged) and clearly discuss the result. You may use graphical illustration to enhance your discussion.
- Run the baseline model with console fixed effects. Briefly compare the new results with the original results of the baseline. Why are some variables dropped from the model with fixed effects? Explain how the fixed effect model can mitigate the endogeneity problem in your baseline model.
- Even after implementing the fixed effect model, what other endogeneity or omitted variable bias (particularly regarding the number of new games and console’s price) may exist in the model.
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