On the behaviour of the GMM estimator in persistent dynamic panel data models with unrestricted initial conditions
نویسندگان
چکیده
This paper investigates the behavior of the first-difference GMM estimator for dynamic panel data models when persistency of data is (moderately) strong and initial conditions are unrestricted. We show that initial conditions affect the rate of convergence of the GMM estimator under a local to unity system where autoregressive parameter is modeled as αN = 1− c/Np where N is the cross-sectional sample size and 0 < p ≤ 1. Specifically, we show that when initial conditions that renders the data to be mean-stationary are assumed, the GMM estimator is inconsistent when 1/2 ≤ p ≤ 1 while N1/2−p-consistent when 0 < p < 1/2. If initial conditions that renders the data to be mean-nonstationary are assumed, the GMM estimator is shown to be √ N -consistent for 0 < p ≤ 1. We also introduce a notion of “near mean-stationary” to take the closeness to mean-stationarity into consideration, and derive asymptotic distributions. A Monte Carlo simulation is conducted to assess the theoretical results. ∗I acknowledge the financial support from the JSPS Fellowship and the Grant-in-Aid for Scientific Research (KAKENHI 20830056) provided by the JSPS. 1
منابع مشابه
On the Behavior of the GMM estimator in Persistent Dynamic Panel Data Models with Unrestricted Initial Conditions
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ورودعنوان ژورنال:
- Computational Statistics & Data Analysis
دوره 100 شماره
صفحات -
تاریخ انتشار 2016