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...

In this paper I propose a computationally practical simulation estimator for large categories of the dynamic panel Tobit model with complicated dependence structures. I first apply the sequential decomposition methods introduced by Hendry and Richard (1992) to obtain the tractable simulated log-likelihood function of the dynamic panel Tobit model. I then maximize this log-likelihood function si...

For time-ordered functional data, an important yet challenging task is to forecast observations with uncertainty quantification. Scalar predictors are often observed concurrently data and provide valuable information about the dynamics of time series. We develop a fully Bayesian framework for dynamic regression, which employs scalar model time-evolution data. Functional within-curve dependence ...

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...