Summary
This note discusses two errors in the approach proposed in Canay (2011) for constructing a computationally simple two-step estimator in a quantile regression model with quantile-independent fixed effects. Firstly, we show that Canay’s assumption about n/Ts → 0 for some s > 1 is not strong enough and can entail severe bias or even the non-existence of the limiting distribution for the estimator of the vector of coefficients. The condition n/T → 0 appears to be closer to the required set of restrictions. These problems are likely to cause incorrect inference in applied papers with large n/T, but the impact is less in applications with small n/T. In an attempt to improve Canay’s estimator, we propose a simple correction that may reduce the bias. The second error concerns the incorrect asymptotic standard error of the estimator of the constant term. We show that, contrary to Canay’s assumption, the within estimator has an influence function that is not i.i.d. and this affects inference. Moreover, the constant term is unlikely to be estimable at rate $\sqrt{nT}$, so a different estimator may not be available. However, the issue concerning the constant term does not have an effect on slope coefficients. Finally, we give recommendations to practitioners and conduct a meta-review of applied papers that use Canay’s estimator.
This paper presents results from an econometric analysis of Russian bank defaults during the period 1997-2003, focusing on the extent to which publicly available information from quarterly bank balance sheets is useful in predicting future defaults. Binary choice models are estimated to construct the probability of default model. We find that preliminary expert clustering or automatic clustering improves the predictive power of the models and incorporation of macrovariables into the models is useful. Heuristic criteria are suggested to help compare model performance from the perspectives of investors or banks supervision authorities. Russian banking system trends after the crisis 1998 are analyzed with rolling regressions.
A considerable amount of research has shown that that carbon tax combined with research subsidy may be regarded as an optimal policy in view of diffusing low carbon technologies for the benefit of the society. The paper exploits the macro economic approach of the endogenous growth models with technological change for a comparative assessment of these policy measures on the economic growth in the US and Japan in the medium and the long run. The results of our micro estimates reveal several important differences across the Japanese and US energy firms: lower elasticity of innovation production function in R&D expenditure, lower probability of a radical innovation, and larger advances of dirty technologies in Japan. This may explain our quantitative findings of stronger reliance on carbon tax than on research subsidies in Japan relative to the US.
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