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An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain

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  • Tiwari, Aviral Kumar

Abstract

The paper examines Granger-causality between the producers' and the consumers' price using Australian data within the frequency domain framework. For long run relation, the Johansen and Juselius (1990) maximum likelihood approach to cointegration was utilized. The test is also supplemented by the Breitung and Candelon (2006) and Lemmens et al. (2008) method. The quarterly data for the study covers 1969q3 to 2010q4. The findings suggest that consumers' price Granger-causes producers' price at an intermediate level of frequencies reflecting medium-run cycles, whereas producers' price does not Granger-cause consumers' price at any level of frequencies. Our study shows that consumers' price is a leading indicator of producers' price. Given that producers' price is used in making various macroeconomic indicators in real terms, the findings should help the Australian policymakers to gain control over the factors that affect consumers' price. The major contribution of the paper is to demonstrate unidirectional causality from consumers' price to the producers' price. Specifically, results show that consumers' price in Australian may have a significant predictive content in how the producers' price evolve. Furthermore, the application of the Breitung and Candelon (2006) and Lemmens et al. (2008) methodology in testing the Granger-causality in frequency domain is also relatively new.

Suggested Citation

  • Tiwari, Aviral Kumar, 2012. "An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain," Economic Modelling, Elsevier, vol. 29(5), pages 1571-1578.
  • Handle: RePEc:eee:ecmode:v:29:y:2012:i:5:p:1571-1578
    DOI: 10.1016/j.econmod.2012.05.010
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    Cited by:

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    3. Ritabrata Bose & Ashima Goyal, 2020. "Disaggregated Indian industrial cycles: A Spectral analysis," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2020-033, Indira Gandhi Institute of Development Research, Mumbai, India.
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    7. Tiwari, Aviral Kumar & Oros, Cornel & Albulescu, Claudiu Tiberiu, 2014. "Revisiting the inflation–output gap relationship for France using a wavelet transform approach," Economic Modelling, Elsevier, vol. 37(C), pages 464-475.
    8. Wang, Minggang & Tian, Lixin & Xu, Hua & Li, Weiyu & Du, Ruijin & Dong, Gaogao & Wang, Jie & Gu, Jiani, 2017. "Systemic risk and spatiotemporal dynamics of the consumer market of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 188-204.
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    10. Ülke, Volkan & Ergun, Ugur, 2013. "The Relationship between Consumer Price and Producer Price Indices in Turkey," MPRA Paper 59437, University Library of Munich, Germany.
    11. Jing Sun & Jinhui Xu & Xin Cheng & Jichao Miao & Hairong Mu, 2023. "Dynamic causality between PPI and CPI in China: A rolling window bootstrap approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1279-1289, April.
    12. Xiao, Jiang & Wang, Minggang & Tian, Lixin & Zhen, Zaili, 2018. "The measurement of China’s consumer market development based on CPI data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 664-680.
    13. Tiwari, Aviral Kumar & Suresh K.G., & Arouri, Mohamed & Teulon, Frédéric, 2014. "Causality between consumer price and producer price: Evidence from Mexico," Economic Modelling, Elsevier, vol. 36(C), pages 432-440.
    14. Sharma, Gagan Deep & Tiwari, Aviral Kumar & Talan, Gaurav & Jain, Mansi, 2021. "Revisiting the sustainable versus conventional investment dilemma in COVID-19 times," Energy Policy, Elsevier, vol. 156(C).
    15. Roxana Cristina VILCU (MANACHE), 2015. "Inflation by Producer Price Index – predictive factor for Inflation by Consumer Price Index? The case of Romania," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 63(2), pages 22-37, February.
    16. Sharma, Gagan Deep & Sarker, Tapan & Rao, Amar & Talan, Gaurav & Jain, Mansi, 2022. "Revisiting conventional and green finance spillover in post-COVID world: Evidence from robust econometric models," Global Finance Journal, Elsevier, vol. 51(C).
    17. Tiwari, Aviral Kumar & Jena, Sangram Keshari & Mitra, Amarnath & Yoon, Seong-Min, 2018. "Impact of oil price risk on sectoral equity markets: Implications on portfolio management," Energy Economics, Elsevier, vol. 72(C), pages 120-134.
    18. Joseph, Anto & Sisodia, Garima & Tiwari, Aviral Kumar, 2014. "A frequency domain causality investigation between futures and spot prices of Indian commodity markets," Economic Modelling, Elsevier, vol. 40(C), pages 250-258.
    19. Qingru Sun & Xiangyun Gao & Shaobo Wen & Sida Feng & Ze Wang, 2019. "Modeling the impulse response complex network for studying the fluctuation transmission of price indices," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 835-858, December.
    20. Sun, Qingru & Gao, Xiangyun & Wen, Shaobo & Chen, Zhihua & Hao, Xiaoqing, 2018. "The transmission of fluctuation among price indices based on Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 36-49.
    21. Qingru Sun & Xiangyun Gao & Ze Wang & Siyao Liu & Sui Guo & Yang Li, 2020. "Quantifying the risk of price fluctuations based on weighted Granger causality networks of consumer price indices: evidence from G7 countries," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 821-844, October.

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    More about this item

    Keywords

    Producers' price index; Consumers' price index; Granger-causality in the frequency domain; Australia;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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