Utilizing the Kauffman Firm Survey, a panel data set with six years of data on businesses that began operations in the United States in 2004, this short-course provides a small-scale setting to explore research techniques in longitudinal analysis of business data.
Longitudinal data offer many opportunities and potential pitfalls. With such data, researchers can examine predictor and response variables at two or more points in time. These kinds of data have two major attractions: the ability to control for unobservables and the determination of causal ordering. However, problems can arise if longitudinal data are not used properly. Repeated observations are typically correlated and this invalidates the usual assumption that observations are independent. In this training we examine methods to deal with this dependence: robust standard errors, generalized estimating equations, random effects models and fixed effects models. We also examine different methods for quantitative outcomes, categorical outcomes, count data outcomes, and survival analysis.
This workshop is for researchers seeking to learn better methods for analyzing longitudinal business data and have a basic statistical background. The primary audience are researchers currently using the KFS for analysis or who have an interest in possible research using the KFS. Viewers should have a good working knowledge of the principles and practice of econometrics and statistics.
This workshop used STATA (release 11) for the empirical examples.
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The following sources provide the foundation for the course:
Alicia Robb was a senior research fellow at the Ewing Marion Kauffman Foundation and was the principal investigator on the Kauffman Firm Survey. Previously, she was an economist at the Division of Research and Statistics, Board of Governors of the Federal Reserve System and an economist at the Office of Economic Research at the U.S. Small Business Administration. A leading expert in small business data in the U.S., she has a Ph.D. in Economics from the University of North Carolina, Chapel Hill with a specialization in econometrics.