Estimation of Panel Vector Autoregression in Stata

Uniform Inference in Panel Autoregression∗

This paper considers estimation and inference concerning the autoregressive coefficient (ρ) in a panel autoregression for which the degree of persistence in the time dimension is unknown. The main objective is to construct confidence intervals for ρ that are asymptotically valid, having asymptotic coverage probability at least that of the nominal level uniformly over the parameter space. It is ...

Oil dependence, institutional quality and economic growth: A panel vector autoregression approach

Resources are the foundation of economic growth. With speedy economic and population growth, economic growth is facing a scarcity of resources worldwide. Resource-economy co-ordination has become every government’s main focus in reaching strategic development goals in countries that are on the path of rapid economic development. Sustainable economic development in a country requires resources a...

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

Analysis of causal effects between continuous-valued variables typically uses either autoregressive models or structural equation models with instantaneous effects. Estimation of Gaussian, linear structural equation models poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how to combine the non-Gaussian instantaneous model wi...

Lecture 6 : Vector Autoregression ∗

Spatial panel data models using Stata

xsmle is a new command for spatial analysis using Stata. We consider the quasi-maximum likelihood estimation of a wide set of both fixedand randomeffects spatial models for balanced panel data. Of special note is that xsmle allows to handle unbalanced panels thanks to its full compatibility with the mi suite of commands, to use spatial weight matrices in the form of both Stata matrices and spma...