In this paper, we evaluate several different techniques for estimating dynamic models with panels characteristic of many macroeconomic panel datasets; our goal is to provide a guide to choosing appropriate techniques for panels of various dimensions.

Feb 24, 2020 · In this paper, we present an approach that uses the eigenvalues and eigenvectors of a spatial weight matrix to directly construct consistent least-squares estimators of parameters of a general spatial dynamic panel data model. The proposed methodology is conceptually simple and efficient and can be easily implemented.

This topic introduces the dynamic panel model and demonstrates how to estimate it, given that the estimation methods for panel data (e.g. Fixed Effects) are likely to produce biased results.

The ability of first differencing to remove unobserved heterogeneity also underlies the family of estimators that have been developed for dynamic panel data (DPD) models. These models contain one or more lagged dependent variables, allowing for the modeling of a …

Feb 12, 2021 · We use a Monte Carlo approach to investigate the performance of several different methods designed to reduce the bias of the estimated coefficients for the longer, narrower panels commonly found for macro data.

To forecast dynamic panel data model, it's important to have a \good" estimates of the individual e ects i. \Selection" bias: repeated positive shocks (Uit) lead to overestimation of their corresponding i's, especially when T is small.

Jan 9, 2013 · Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non-spatial estimators and illustrate our approach with an application to new economic geography.

We propose a new estimator for the dynamic panel model, which solves the failure of strict exogeneity by calculating the bias in the rst-order conditions as a function of the autoregressive parameter and solving the resulting equation.

Jun 7, 2021 · pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions Abstract: This paper introduces pdynmc, an R package that provides users sufficient flexibility and precise control over the estimation and inference in linear dynamic panel data models.

Panel data / longitudinal data allows to account for unobserved unit-specific heterogeneity and to model dynamic adjustment / feedback processes. Instrumental variables (IV) / generalized method of moments (GMM) estimation is the predominant estimation technique for models with endogenous variables, in particular lagged dependent variables ...