Working Paper No. 128

Information Criterion and Estimation of Misspecified Qualitative Choice Models

This paper investigates misspecified estimation and model selection criteria derived from the "Information Criterion (see Akaike (1973))" for qualitative choice models. Four estimators for the "Information Criterion" are derived for general qualitative choice models. Two of these estimators were previously derived by Akaike (1973) and Chow (1981) for arbitrary likelihood functions. The new estimators are derived by taking analytic expectations of the log likelihood function. A number of Monte Carlo experiments are performed using binominal logit models to investigate the behavior of the Information Criterion estimators with realistic sample sizes. The new analytic estimators are more accurate than the more general estimators , but they do not always perform as well in minimizing prediction or estimation error. Monte Carlo results also show that the usual asymptotic distribution properties of the maximum likelihood estimator are poor approximations for sample sizes as large as 1,000 observations with only two variables.

Sick of Inequality?

An Introduction to the Relationship between Inequality and Health

Sick of Inequality.jpg

In this book Andreas Bergh, Therese Nilsson, IFN and Lund University, and Daniel Waldenström, IFN and Paris School of Economics, France, review the latest research on the relationship between inequality and health. What does inequality mean for our health? Does increasing income inequality affect outcomes such as obesity, life expectancy and subjective well-being?


Seminars organized by IFN


To present ongoing research informal brown-bag seminars are held on Mondays at 11:30 am. This is an opportunity for IFN researchers to test ideas and results.

Academically oriented seminars are most of the time held on Wednesdays at 10 am. At these events researchers from IFN and other institutions present their research.

In addition, IFN organizes seminars open to the public. Topics for these are derived from the IFN research.

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