China's provincial energy‐related carbon emissions‐economy nexus: A two‐stage framework based on decoupling analysis and panel vector autoregression

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...

Chinas provincial disparities and the determinants of provincial inequality

The paper explains the growth –inequality nexus for China’s provinces. The theoretical model of provincial development consists of two regions and studies the interactions of a mutual depending development process. Due to positive externalities, incoming trade and FDI induce imitation and hence productivity growth. The regional government can in‡uence the economy by changing international trans...

Carbon Emissions Abatement Cost in China: Provincial Panel Data Analysis

This paper employs the quadratic directional output distance function to derive shadow prices of China’s aggregate carbon emissions at the province level between 1997 and 2010. The empirical results indicate that the national weighted average shadow price presents an “N-shape” curve across the sample period, experiencing the initial phase of growth followed by a phase of deterioration, and then...

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 ...

Lecture 6 : Vector Autoregression ∗