We consider within-group estimation of higher-order autoregressive panel models with exogenous regressors and fixed effects, where the lag order is possibly misspecified. Even when disregarding the misspecification bias, the fixed-effect bias formula is quite different from the correctly...

It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with .xed eects is inconsistent under .xed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in...

It is well known that (quasi) MLE of dynamic panel data (DPD) models with short panels depends on the assumptions on the initial values; ignoring them or a wrong treatment of them will result in inconsistency or serious bias. This paper introduces a initial-condition free method for estimating...

We show that independently repeated cross-sectional data can reduce the asymptotic bias when instruments are weakly correlated to the endogenous variables. When both N and T go to infinite, we can obtain consistent estimators even if instruments are weak.

We estimate by means of indirect inference a structural economic model where firms' exit and investment decisions are the solution to a discrete-continuous dynamic programming problem. In the model the exit probability depends on the current capital stock and a measure of short-run...

Given a sample from a fully specified parametric model, let $Z_n$ be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of $Z_n$. We call this the maximum...

Given a sample from a fully specified parametric model, let Z_<em>n</em> be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Z_<em>n</em>. We call this the maximum indirect...

Standard indirect Inference (II) estimators take a given finite-dimensional statistic, Z_{n} , and then estimate the parameters by matching the sample statistic with the model-implied population moment. We here propose a novel estimation method that utilizes all available information contained...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...