Structure
Undertake the following tasks included in each section (you are not limited to the suggested section headings, and you can use sub sections to help with the flow of your project):
Introduction
From the set research question (How do job and marriage affect life satisfaction?), list at least two hypotheses which you can test with the provided data. You can number your chosen hypotheses so that you can easily refer back to them. You should focus on one or two important factors for which you can make hypotheses and predictions about, and then include other factors as a set of controls. You need to set up any prior expectations in your hypotheses. Following each of your hypotheses, you should clearly justify what you are doing. Although a formal literature review is not required, do make reference to any literature, economic theory and other sources you used to develop your resulting hypotheses.
Data
You should discuss the dependent variable and your chosen key explanatory variables (e.g., what your dependent and independent variables are, and why you have selected them). You should provide relevant descriptive statistics (which may include graphics if you wish) of your dependent and independent variables (such as means, standard deviations, max, min) as this may aid your interpretation (especially if results are unexpected). Demonstrate your understanding of the data and variables through the use of descriptive statistics.
Methodology
Choose one or more estimation method (estimator) from four methods: pooled OLS, random effects, fixed effects, and ordered probit model or random effect ordered probit model. Discuss why you are using this/these estimation method (e.g. do tests for fixed versus random effects), how the estimator can be used to test your hypotheses, and how your chosen variables are measured. You should use regression equations to demonstrate the specification of your population model (i.e. include variables in your regression specification)
Reference should be made to any literature which helped make these decisions.
It is important to demonstrate your understanding of the estimators (estimation methods) applied and justify their use. I expect to see the specification of your population model(s) and the application of the estimator. You should provide enough information about the chosen methods so that the reader could replicate your analysis.
Results and discussion
You should provide well-presented tables of your statistics and regression output; I don’t want to see raw Stata output. Tips for formatting your project, especially tables, are given in the appendix.
Interpret and discuss your results (including any specification tests) to help answer the research question and provide the results of your hypothesis tests. Remember the aim is to use the appropriate estimator(s) to help answer the research question and test your hypotheses, so this is a very important section. It is important to discuss both the statistical and economic significance of the results – i.e., look at the magnitude, does the size of the effect matter, are the results meaningful, what do they mean in reality, are they important, would policy makers care about the results?
A key part of research is to be able to critically analyse your results. Therefore, do link results back to the research question and hypotheses, and more widely discuss them in relation to economic theory (do they support or contradict what economic theory would predict?), findings from past literature (do they back up, contradict or develop further what others have found?) and potentially the intuition behind the results (do they make sense, do they fit with your prior expectations?). Given you are using a sample to make inferences about a population of interest you also need to convince the reader that your results are good estimates of the true population parameters. Therefore, you should discuss any specification tests (fixed vs random effect, test of the assumptions in ordered models, joint significant test, etc), assumptions and any measures to judge the goodness of fit. It is also common to undertake a number of sensitivity/robustness checks to check your results are unchanged – this may involve changing the specification of your model, using a different measure of your variables of interest or using different estimators (e.g. you may show there is little difference between pooled OLS and ordered probit model but much more substantial differences between, say, random and fixed effects).
Conclusions
The conclusions summarise main results and provide answers to the research question and the results of your hypothesis tests. You can discuss any policy implications on the research question. You should discuss any caveats to your results e.g. potential problems with the estimators used, data limitations – these may appear in the conclusions or be discussed at other relevant points (such as in the discussion of the methods
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