international econometric journal
in Russian language

no. 6

march 2009


Journal news


Econometric literacy: treatment effects


Enikolopov, Ruben. Estimation of treatment effects

The essay contains a short survey of methods used for estimation of average treatment effects, when the independent variable of interest is binary.

Newey, Whitney. Treatment effects

This essay discusses the issues of identification and estimation of the average treatment effect and the average effect of treatment on the treated.

Wooldridge, Jeffrey. Difference-in-differences estimation

This article provides an overview of difference-in-differences estimation, starting with a review of the basic methodology, discussing in some detail recent advances in inference, and concluding with new methods for estimating treatment effects in various nonlinear and semiparametric models.


Econometric literacy: limited dependent variables


Biørn, Erik. Estimation of discrete choice and censoring models

This expository note gives an overview of model specifications, likelihood functions and a structure of maximum likelihood problems for discrete choice and censoring models. One part deals with estimation in a single equation case with unidimensional (cross-sectional) observations. Another part extends the framework to a two-equation case. The last part is concerned with an extension to a panel data situation.


Advice to econometrics students


Anatolyev, Stanislav; Tsyplakov, Alexander. Where to find data on the Web?

We give thematically arranged lists of useful websites where an empirical economist can find data for research.


Problems and solutions


Problems 6.1, 6.2, 6.3

Solutions 5.1, 5.2, 5.3


Articles: econometric theory


Kitov, Victor. Second order bias in a forecast evaluation statistic

We derive the second order asymptotic bias for a statistic evaluating the quality of out-of-sample forecasts by parametric models. The existing literature admits that the quality of the first order asymptotic approximation may be unsatisfactory. We find that the second order asymptotic bias allows one to explain insufficient precision of the asymptotic approximation. Simulations confirm the obtained analytical results.


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