American Journal of Operations Research

Volume 3, Issue 6 (November 2013)

ISSN Print: 2160-8830   ISSN Online: 2160-8849

Google-based Impact Factor: 0.84  Citations  

Forecasting the Convergence State of per Capital Income in Vietnam

HTML  Download Download as PDF (Size: 223KB)  PP. 487-496  
DOI: 10.4236/ajor.2013.36047    4,207 Downloads   6,807 Views  Citations

ABSTRACT

Convergence problem of an economic variable represents an underlying forecast of neoclassical economic growth model. This paper aims to analyze the convergence of provincial per capita GDP stability in Vietnam over the period of 1991-2007. This can be done by two approaches including bias data-based regression method for testing convergence and Markov chain model for describing features of long-term tendency of per capita income in Vietnam growth in provinces. The regression method results in the signs of convergence. To apply Markov process, we divide total pattern into 5 per capita income classes. Result estimated from the Markov chain model shows the poor convergence.

Share and Cite:

N. Minh and P. Khanh, "Forecasting the Convergence State of per Capital Income in Vietnam," American Journal of Operations Research, Vol. 3 No. 6, 2013, pp. 487-496. doi: 10.4236/ajor.2013.36047.

Cited by

[1] Fiscal decentralization and income convergence: evidence from Vietnam
Journal of the Asia Pacific Economy, 2022
[2] Can foreign direct investment foster the manufacturing industries' spatial total factor productivity convergence in a transition economy? An empirical approach from …
2021
[3] Regional income convergence in an emerging Asian economy: empirical evidence from Vietnam
2020
[4] Productivity Convergence in Vietnamese Manufacturing Industry: Evidence Using a Spatial Durbin Model
Causal Inference in Econometrics, 2016
[5] TFP Convergence, Environmental friendliness and Innovation
2016
[6] Phân tích thống kê dự báo và mô phỏng một số chuỗi thời gian
2015
[7] Expanded Barro Regression in Studying Convergence Problem
American Journal of Operations Research, 2014

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.