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Econometric Methods
ResEc 702, Spring 2007
Professor Dan Lass

Term Project: one person's (Dan's) idea of an outline.

• Introduction: Present the "problem" you are addressing. What is the economic issue that you wish to address? What is the measurement issue? Motivate your reasons for conducting the analyses in this short section. Get to the point fairly quickly and provide a preview of “coming attractions.”

• Model Specification: Present a conceptual model that follows your problem statement. In this section, you must draw on economic theory. What is the appropriate theory? Review the relevant literature. What theory has been developed? What are the variables in the model and how are they related? Are there other empirical papers with prior results? What did other researchers find? Discuss the importance of each variable. Does theory suggest a form of the model – linear vs. nonlinear? What are the "expected" signs for the parameters in the assumed model you have specified (again according to theory)? These constitute your hypotheses.

• Data: Define each of the variables that will be used in your econometric model. Give a complete set of data sources in your reference section at the end of the text. Identify the data sources that you used in proper bibliographic form. Also, discuss briefly the sample that you used. Was it a time-series? If so for what years were data collected? You must also provide a table listing descriptive statistics (means, standard deviations) for all variables.

• Results: Identify the estimation process and present the results. The parameter estimates must be discussed. What do they mean? Are they consistent with your expectations from theory? Are the parameter estimates statistically different from zero? How about the model? Does the regression model fit the data well? Comment. Does the regression model explain a statistically significant portion of the variation in the dependent variable? What other models did you try to estimate? Are there any other results that should be mentioned? Do there appear to be any problems with the data or estimation methods applied to your data? You should be concerned about the potential problems and consequences of misspecification, random regressors, multicollinearity, heteroskedasticity and/or autocorrelation.

• Summary and Conclusions: What conclusions do you reach? What are the important results that should be summarized? This section should be an effective A stand-alone @ summary of your work. It should inform the reader what you did in a very concise manner. The summary and conclusions should be 1-2 pages at most.