Do energy prices converge across Russian regions?

Akhmedjonov, Alisher; Lau, Chi Keung Marco

doi:10.1016/j.econmod.2012.05.023

Cited by 17 publications

(10 citation statements)

References 20 publications

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“…The proportion of converging provinces is found to be only 16%, which suggests that inter-regional inequalities are evidenced in Russia, attributed to variation in economic policies and region-specific factors. Using the same econometric methodology, Akhmedjonov and Lau (2012) find no evidence of price convergence in the Russia energy market from 2003 to 2010, and this finding is attributed to longdistance transportation cost and uneven energy reserves distribution. Suvankulov, Lau, and Ogucu (2012) examine the impact of price regulation on price convergence in Canadian retail gasoline market of New Brunswick and Nova Scotia.…”

Section: Empirical Methodology On Convergencementioning

confidence: 79%

“…In this section, we aim to examine the empirical approach for unconditional military expenditure convergence. There are increasing interests on the nonlinear convergence in the literature (inter alia : Taylor 2006;Lau 2010;Akhmedjonov and Lau 2012;Lau et al 2012;Suvankulov, Lau, and Ogucu 2012;Akhmedjonov, Lau, and İzgi 2013). Akhmedjonov, Lau, and İzgi (2013) examine the regional income convergence from 2000 to 2008 in Russia using Exponential Smooth Auto Regressive Augmented Dickey-Fuller (ESTAR-ADF) unit root test.…”

Section: Empirical Methodology On Convergencementioning

confidence: 99%

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A Nonlinear Model of Military Expenditure Convergence: Evidence From Estar Nonlinear Unit Root Test

Lau

Demir

Bilgin

2015

Defence and Peace Economics

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The paper builds a model to empirically test military expenditure convergence in a nonlinear set up. We assert that country A chooses a military strategy of catching up with the military expenditure of its rivals, subject to public spending constraints on public investments, including health and education, leading to decrease in long-term economic welfare. This implies nonlinear convergence path: only when the military expenditure gap between countries reaches the threshold level, will it provide incentives to catch up with rival's military expenditures. We test this nonlinear catching up hypothesis for 37 countries spanning from 1988 to 2012. Results from individual nonlinear cross-sectionally augmented Dickey-Fuller (NCADF) regression indicate that 53% of countries converge to world's average military expenditure: where 39% of countries converge to Germany; 33% of countries converge to China; 22% of countries converge to the USA, and 11% of countries converge to Russia. Interestingly, USA does not exhibit nonlinear military expenditure convergence toward world's average level. For panel NCADF regression, the result suggests that on average, there is evidence for countries converging to USA's military expenditure at 10% significance level. For the convergence to the world's average, the statistical significance is at the 1% significance level.

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Section: Empirical Methodology On Convergencementioning

confidence: 79%

Section: Empirical Methodology On Convergencementioning

confidence: 99%

A Nonlinear Model of Military Expenditure Convergence: Evidence From Estar Nonlinear Unit Root Test

Lau

Demir

Bilgin

2015

Defence and Peace Economics

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“…A consequence is that the pattern obtained crucially depends on the choice of benchmark region. One more way is to use the national market as the benchmark; that is, the national price (a weighted average of regional prices) serves as the numeraire, like, e.g., in Akhmedjonov and Lau (2012). Here, the same problem of non-transitivity as above arises.…”

Section: Methodsmentioning

confidence: 99%

“…Using time series analysis, regions are broken down into three groups: integrated with the benchmark region, tending towards integration with it, and neither integrated nor tending towards integration. Akhmedjonov and Lau (2012) The reminder of the paper is organized as follows. Section 2 expounds methodology applied.…”

Section: Introductionmentioning

confidence: 99%

Spatial Pattern of Russia's Market Integration

Gluschenko

2017

SSRN Journal

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“…They find evidence of Granger causality from benchmark markers to other crude oil markets. Akhmedjonov and Lau (2012) study the monthly energy prices of four energy products for 83 Russian regions using the Exponential Smooth Auto-Regressive Augmented Dickey-Fuller unit root test and find no evidence of a fully integrated national energy market in Russia. Liu et al (2013) examine the regionalization issue by investigating the integration between China's and four major crude oil markets with a threshold error correction model and find only unidirectional volatility spillover running from benchmark markets to China's oil market.…”

Section: Introductionmentioning

confidence: 99%

Correlation structure and principal components in the global crude oil market

et al. 2016

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This article investigates the correlation structure of the global crude oil market using the daily returns of 71 oil price time series across the world from 1992 to 2012. We identify from the correlation matrix six clusters of time series exhibiting evident geographical traits, which supports Weiner (1991)'s regionalization hypothesis of the global oil market. We find that intra-cluster pairs of time series are highly correlated while inter-cluster pairs have relatively low correlations. Principal component analysis shows that most eigenvalues of the correlation matrix locate outside the prediction of the random matrix theory and these deviating eigenvalues and their corresponding eigenvectors contain rich economic information. Specifically, the largest eigenvalue reflects a collective effect of the global market, other four largest eigenvalues possess a partitioning function to distinguish the six clusters, and the smallest eigenvalues highlight the pairs of time series with the largest correlation coefficients. We construct an index of the global oil market based on the eigenfortfolio of the largest eigenvalue, which evolves similarly as the average price time series and has better performance than the benchmark 1/N portfolio under the buy-and-hold strategy.JEL classification: G1, C15

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Do energy prices converge across Russian regions?

Cited by 17 publications

References 20 publications

A Nonlinear Model of Military Expenditure Convergence: Evidence From Estar Nonlinear Unit Root Test

A Nonlinear Model of Military Expenditure Convergence: Evidence From Estar Nonlinear Unit Root Test

Spatial Pattern of Russia's Market Integration

Correlation structure and principal components in the global crude oil market

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