Posted: December 15th, 2014

Leading Causes of Homelessness by State in the USA

Leading Causes of Homelessness by State in the USA

Project description
Stats output should be done in EViews if possible.
Paper Guidelines:
2. Abstract: This must be on a separate page and should not exceed one paragraph. The abstract will very briefly summarize your research topic, your results, and your conclusions.

3. Introduction: In this section you must clearly define your research topic and the question you are trying to answer. You must give a brief summary of your methodology, your model, your variables, and any forecasts or policy evaluations your research may include. The contents of following sections should be briefly discussed.

4. Brief Literature Review: Briefly summarize the literature on your topic, by providing the conclusions of previous researchers, if appropriate. Be very careful to cite the work of others that you are including in your paper. You should keep in mind the following points:
Avoid writing every little detail you have encountered in your readings.
You must give an indication that you have done some reading: You cannot summarize everything that has ever been written on the topic.

5. The Model: Here you will discuss the model in detail. You should explain why you selected the particular model. Each of the variables must be carefully defined and discussed in detail. Try to discuss the importance of each variable. What are the expected signs of the various parameters and why? Remember the following points:
Please void long technical discussions and advanced theoretical proofs.
If you manipulate your variables (for example, taking logs), you should explain why.

6. The Data: Along with the previous section and the next one, this is one of the most important parts of your project. The data for each variable, the source, and any potential problems must be discussed in detail. This section should include the following:
A table with a list of all variables with the mean and standard deviation for each variable. Please list the detailed source for each variable.
Scattergrams and / or line charts of the relationships among the dependent variable and the independent variables, if applicable. In preparing your data for use in a regression model, you should be careful about the following issues:
In case of monthly or quarterly time series data, check whether you need to seasonally adjust your series.
Be cautious with your scaling and your units of measurement. You should be consistent: In case of monetary variables, you cannot have one in current (nominal) dollars and another in real (inflation-adjusted) dollars. Likewise, you cannot have one variable in billions of dollars and another in thousands.
If you manually enter your data in a spreadsheet, be careful to avoid typing errors.
Please back up your data. Telling the instructor that you lost your flash drive, or that your computer crashed is not an excuse.
7. Regression Results: Here you must discuss your results in detail. Do your variables have the expected signs? What will happen if you drop an outlier from your regression? (If you have outliers). Are the regression coefficients statistically significant? Is the overall fit of the model satisfactory? Additional analysis beyond the presentation of results will greatly enhance your paper: For example, testing for multicollinearity, heteroscedasticity, autocorrelation, or a structural break in your series. Are your coefficients stable over time? Can you use you model for a forecast? In presenting your results, keep in mind the following points: It is not necessary to draw pictures of your F and t -tests but you must include critical values and levels of significance. If you work with time series data and you correct for autocorrelation, explain how you corrected the problem but you must avoid showing the details. Avoid data mining. Do not play the game of maximizing R2. A low R2 or negative results will not invalidate your paper. Instead, you must explain why you obtained such results. Please do not include every regression you fitted.

8. Summary and Conclusions: Summarize your model, major findings, the tests you did (if any), and compare, if possible, your results to previous research.

9. Appendices: Here you will include your original regression printouts.

10. Bibliography: This final section will include your detailed references for the project.

Will attach paper progress so far for writer to go off of/complete

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