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Modeling Dependent Structure Among Micro-Economics Variables Through COPAR (1)-Model in Pakistan

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  • Yousaf Ali Khan

    (Jiangxi University of Finance and Economics
    Hazara University Mansehra)

Abstract

A great fluctuations in oil price due to COVID-19 has been observed worldwide. Expertise of complicated relationships among economic indicators has considerable significance for consumers, specialists and strategy producers the same. This exploration work is devoted to investigating the impact of oil price fluctuations due to corona virus pandemic on inflation rate, interest rate and industrial production during lock-down using recent monthly data of Pakistan economic system starting from 2008-01 to 2020-04. At analysis stage, we generally tend to contemplate a novel autoregressive model approach to model non-linear dependence structure amongst a couple of time series. Having gain from the flexibleness of R-vine copulas, the copula autoregression with efficiency investigates the have an impact on of one-time series onto some other: it really is, one-time arrangement normally plays a vital role. Through these qualities of the model, we tend to investigate fuel price effects on industrial production, expansion rate and interest rate in my homeland. One in every of the key finding of this analysis is that there’s a weak tail asymmetry, however some tail dependence, that COPAR-model with efficiency absorbs to account. Furthermore, the fashions monitor lagged reactions of interest rate and industrial production on adjustments in fuel prices inside Pakistan. The oil price result on the inflation rate; on the other hand, is quite rapid.

Suggested Citation

  • Yousaf Ali Khan, 2022. "Modeling Dependent Structure Among Micro-Economics Variables Through COPAR (1)-Model in Pakistan," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 257-279, March.
  • Handle: RePEc:spr:jqecon:v:20:y:2022:i:1:d:10.1007_s40953-021-00284-6
    DOI: 10.1007/s40953-021-00284-6
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    References listed on IDEAS

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    1. Jain, Anshul & Biswal, P.C., 2016. "Dynamic linkages among oil price, gold price, exchange rate, and stock market in India," Resources Policy, Elsevier, vol. 49(C), pages 179-185.
    2. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    3. Waseem ASLAM, 2014. "Relationship Between Stock Market Volatility And Exchange Rate: A Study Of Kse," Journal of Public Administration, Finance and Law, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 5(5), pages 62-72, June.
    4. Dong Hwan Oh & Andrew J. Patton, 2017. "Modeling Dependence in High Dimensions With Factor Copulas," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 139-154, January.
    5. Akbar, Muhammad & Iqbal, Farhan & Noor, Farzana, 2019. "Bayesian analysis of dynamic linkages among gold price, stock prices, exchange rate and interest rate in Pakistan," Resources Policy, Elsevier, vol. 62(C), pages 154-164.
    6. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    7. Jain, Anshul & Ghosh, Sajal, 2013. "Dynamics of global oil prices, exchange rate and precious metal prices in India," Resources Policy, Elsevier, vol. 38(1), pages 88-93.
    8. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    9. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    10. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    11. Katsuya Ito, 2010. "The Impact of Oil Price Volatility on Macroeconomic Activity in Russia," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 9, pages 1-21, July.
    12. Brendan K. Beare & Juwon Seo, 2015. "Vine Copula Specifications for Stationary Multivariate Markov Chains," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 228-246, March.
    13. Eike Christian Brechmann & Claudia Czado, 2015. "COPAR—multivariate time series modeling using the copula autoregressive model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(4), pages 495-514, July.
    14. Stöber, Jakob & Joe, Harry & Czado, Claudia, 2013. "Simplified pair copula constructions—Limitations and extensions," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 101-118.
    15. Sari, Ramazan & Hammoudeh, Shawkat & Soytas, Ugur, 2010. "Dynamics of oil price, precious metal prices, and exchange rate," Energy Economics, Elsevier, vol. 32(2), pages 351-362, March.
    16. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
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