It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size (T ) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in practical applications when T is small and the autoregressive parameter is close to unity. The presen...

Innovations, be they radical new products or technology improvements are widely recognized as a key factor of economic growth. To identify the factors triggering innovative activities is a main concern for economic theory and empirical analysis. As the number of hypotheses is large, the process of model selection becomes a crucial part of the empirical implementation. The problem is complicated...

Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo stu...

This paper studies estimation and inference in a quantile regression dynamic panel model with fixed effects. Panel data fixed effects estimators are typically biased in the presence of lagged dependent variables as regressors. To reduce the dynamic bias in the quantile regression panel data model I develop an instrumental variables approach that employs lagged regressors as instruments. I show ...

Conventional leakage reduction techniques focus primarily on sub-threshold leakage mitigation, while neglecting the effect of gate leakage current. This work focuses on understanding gate leakage current and developing circuit techniques for total leakage minimization. We present an efficient technique for gate leakage of CMOS circuits. Input vector control and circuit reconfiguration technique...

The dynamic range in many real-world environments surpasses the capabilities of traditional display technologies by several orders of magnitude. Recently, a novel display capable of displaying images with a dynamic range much closer to real world situations has been demonstrated. This was achieved through a spatially modulated backlight behind an LCD panel. Combined with the modulating power of...

abstract This paper studies the asymptotic covariance estimation associated with the LIML estimator for large-T dynamic panel structural equations. The proposed simple estimation method is based on the fixed-N or large Kasymptotics relating to the number of backward-filtered instruments. We demonstrate the resulting t-Test which also does not need asymptotic bias correction works well with the ...

Article history: Received 2 February 2012 Received in revised form 12 September 2012 Accepted 17 September 2012 Available online 24 September 2012 Dynamic panel models play a natural role in several important areas of corporate finance, but the combination of fixed effects and lagged dependent variables introduces serious econometric bias. Several methods of counteracting these biases are avail...

This paper investigates stationary β-mixing dynamics in nonlinear panel models and develops nonparametric estimation of dynamic panel models using series approximations. We extend the standard linear dynamic panel model to a nonparametric form that maintains additive fixed effects. Convergence rates and the asymptotic distribution of the series estimator are derived, in which an asymptotic bias...

In this paper, we analyze a dynamic panel probit model with two flexible latent effects: first, unobserved individual heterogeneity that is allowed to vary in the population according to an assumption-free nonparametric distribution, and second, with a latent serially correlated common error component. In doing so, we extend the approach developed in Albert and Chib (1993), Albert and Chib (199...