Since little is known about the degree of bias in estimated fixed effects in panel data models, we run Monte Carlo simulations on a range of different estimators. We find that Anderson-Hsiao IV, Kiviet's bias-corrected LSDV and GMM estimators all perform well in both short and long panels....

At the present time there is no well accepted test for determining whether or not robust regression parameter estimates are significantly different than least squares estimates. Thus. we propose and demonstrate the efficacy of two Wald-like statistical tests for the above purposes using...

We provide a method to estimate resource shares - the fraction of total household expenditure allocated to each household member - using OLS estimation of Engel curves. The method is a linear reframing of the nonlinear model of Dunbar, Lewbel and Pendakur (2013), extended to allow single-parent...

Management of financial risks and sound decision making rely on the accurate information and predictive models. Drawing useful information efficiently from big data with complex structures and building accurate models are therefore crucial tasks. Most commonly used methods for statistical...

Many financial and economic time series exhibit nonlinear patterns or relationships. However, most statistical methods for time series analysis are developed for mean-stationary processes that require transformation, such as differencing of the data. In this paper, we study a dynamic regression...

This paper proposes a joint methodology for the identification and inference of structural vector autoregressive models in the frequency domain. We show that identifying restrictions can be written naturally as an asymptotic least squares problem (Gourieroux, Monfort and Trognon, 1985) in which...