Sains Malaysiana 53(9)(2024): 2057-2070

http://doi.org/10.17576/jsm-2024-5309-04

Hubungan Kointegrasi dan Kebersebaban Granger antara Sektor Utama Indeks Harga Pengeluar di Malaysia 

(Cointegration and Granger Causality Relationships between the Major Sectors of the Producer Price Index in Malaysia)

NURULKAMAL MASSERAN*, NUR ATIQA HALIL, ‪NORISZURA ISMAIL & MOHD SABRI ISMAIL              

Jabatan Sains Matematik, Fakulti Sains dan Teknologi, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

Diserahkan: 30 April 2024/Diterima: 5 Julai 2024

Abstrak

Penyesuaian harga adalah salah satu faktor yang boleh digunakan untuk mengawal kecekapan pasaran. Ia merupakan salah satu pendekatan dalam proses pengagihan sumber, kawalan hasil pengeluaran, mahupun pengawalseliaan kerajaan. Bagi tujuan tersebut, Indeks Harga Pengeluar (IHPR) merupakan maklumat penting yang sentiasa dirujuk dalam proses pembuatan keputusan dan polisi berkaitan penyesuaian harga. IHPR adalah indeks berasaskan output yang mengukur perubahan harga komoditi untuk jualan pasaran. Penyelidikan ini mengkaji tingkah laku kointegrasi dan hubungan kebersebaban Granger antara pemboleh ubah dalam IHPR di Malaysia. Data kebangsaan IHPR yang dilaporkan oleh Jabatan Perangkaan Malaysia bagi tempoh  Januari 2010 sehingga Disember 2023 telah dianalisis. Objektif kajian ini adalah untuk memahami hubungan dinamik antara pemboleh ubah IHPR dalam konteks ekonomi negara Malaysia menerusi pendekatan ujian punca unit, model vektor autoregresif, analisis penyebab Granger dan kointegrasi Johansen. Hasil kajian mendapati bahawa wujud tiga hubungan satu hala yang signifikan antara pasangan sektor-sektor  IHPR iaitu; i) perubahan dalam sektor perlombongan adalah penyebab Granger kepada sektor perubahan dalam sektor pembuatan; ii) perubahan dalam sektor bekalan elektrik merupakan penyebab Granger kepada perubahan dalam sektor perlombongan dan iii) perubahan dalam sektor bekalan air merupakan penyebab Granger kepada perubahan dalam sektor bekalan elektrik. Namun, didapati tiada hubungan kebersebaban dua hala yang signifikan bagi perubahan antara mana-mana pasangan sektor pemboleh ubah IHPR. Selain itu, berdasarkan ujian kointegrasi Johansen, didapati kointegrasi antara pasangan pemboleh ubah IHPR; i) sektor pertanian-perlombongan dan ii) sektor pertanian-pembuatan adalah signifikan. Ini mengimplikasikan bahawa kedua-dua pasangan sektor tersebut menunjukkan tingkah laku kestabilan hubungan jangka panjang dari aspek perubahan nilai indeks antara sektor.

Kata kunci: Hubungan kesebaban; indeks harga pengeluar; kointegrasi; penunjuk ekonomi

Abstract

Price adjustment is one of the factors that can be used to control market efficiency. It is one of the approaches in the process of resource distribution, production control, or government regulation. For that purpose, the Producer Price Index (PPI) is important information that is always referred to in the decision-making process and policies related to price adjustments. IHPR is an output-based index, which measures changes in commodity prices for market sales. This study investigates the cointegration behavior and Granger causality between the variables in the IHPR in Malaysia. IHPR national data reported by the Department of Statistics Malaysia for the period of January 2010 to December 2023 has been analyzed. The objective of this study was to understand the dynamic relationship between PPI variables in the context of the Malaysian economy through the approach of Granger causality analysis and Johansen cointegration. The results of the study found that there are three significant one-way relationships between pairs of IHPR sectors namely; i) changes in the mining sector are Granger causes to the changes in the manufacturing sector; ii) changes in the electricity supply sector are Granger causes to changes in the mining sector, and iii) changes in the water supply sector are Granger causes to changes in the electricity supply sector. However, it was found that there was no significant two-way causality for changes between any pair of sectors of the IHPR variable. In addition, based on Johansen's cointegration test, cointegration was found between pairs of IHPR variables; i) the agricultural-mining sector, and ii) the agricultural-manufacturing sector are significant. This implies that these two pairs of sectors show long-term relationship stability behavior from the aspect of index value changes between sectors.

Keywords: Causality relationship; cointegration; economic indicator; producer price index

RUJUKAN

Akkaya, D.M. 2021. Vector autoregressive model and analysis. In Applied Econometrics Handbook of Research on Emerging Theories, Models, and Applications of Financial Econometrics, disunting oleh Adıgüzel Mercangöz, B. Springer, Cham.

Anggraeni, D. & Irawan, T. 2018. Causality analysis of Producer Price Index (PPI) and Consumer Price Index (CPI) in Indonesia. Jurnal Ekonomi dan Kebijakan Pembangunan 7(1): 60-77.

Anghel, M.G., Mirea, M. & Badiu, A. 2018. Analysis of the main aspects regarding the price indices applied in the determination of inflation. International Journal of Academic Research in Accounting, Finance and Management Sciences 8(2): 28-36.

Arce, P., Antognini, J., Kristjanpoller, W. & Salinas, L. 2019. Fast and adaptive cointegration based model for forecasting high frequency financial time series. Computational Economics 54: 99-112.

Conforti, L. 2016. The first 50 years of the producer price index: Setting inflation expectations for today. Monthly Labor Review 139(6): 1-17.

da Rocha Lima Filho, R.I. 2019. Does PPI lead CPI in Brazil? International Journal of Production Economics 214: 73-79.

del Barrio Castro, T., Cubadda, G. & Osborn, D.R. 2022 On cointegration for processes integrated at different frequencies. Journal of Time Series Analysis 43(3): 412-435.

Hakimipoor, N., Alipour, M.S. & Akbaryan, H. 2016. Investigation on causality relationship between consumer price index and producer price index in Iran. Reports on Economics and Finance 2: 37-49.

Javed, Z.H., Farooq, M. & Akram, S. 2010. Cost-push shocks and inflation: An empirical analysis from the economy of Pakistan. Journal of Economics and International Finance 2(12): 308-312.

Jiang, F., Awaitey, J. & Xie, H. 2022. Analysis of construction cost and investment planning using time series data. Sustainability 14: 1703.

Keilbar, G. & Zhang, Y. 2021. On cointegration and cryptocurrency dynamics. Digital Finance 3: 1-23.

Kelly, M. 2013. Emily Dickinson and monkeys on the stair Or: What is the significance of the 5% significance level? Significance 10(5): 21-22.

Khamis, A.B. 2020. Time series forecasting of Malaysia producer price index using ARIMA and Grey models. International Journal for Research in Applied Science and Engineering Technology 8(8): 1343-1348.

Kumari, A. & Sharma, A.K. 2017. Physical & social infrastructure in India & its relationship with economic development. World Development Perspectives 5: 30-33.

Leschinski, C., Voges, M. & Sibbertsen, P. 2020. A comparison of semiparametric tests for fractional cointegration. Statistical Papers 62: 1997-2030.

Lütkepohl, H. 2011. Vector autoregressive models. In International Encyclopedia of Statistical Science, disunting oleh Lovric, M. Berlin, Heidelberg: Springer.

Lütkepohl, H. 2005. Vector error correction models. In New Introduction to Multiple Time Series Analysis. Springer, Berlin, Heidelberg.

Lüutkepohl, H., Saikkonen, P. & Trenkler, C. 2001. Maximum eigenvalue versus trace tests for the cointegrating rank of a VAR process. The Econometrics Journal 4(2): 287-310.

Masseran, N., Ruzelan, N. & Nur-Firyal, R. 2024. Trend behavior of Malaysia consumer price index: Evidence from National Database (2003-2022). Sains Malaysiana 53(4): 953-967.

Masseran, N., Ahmad Farouk, S.N., Nur-Firyal, R.  & Mansor, M. M. 2024. Latent hidden factor model for Malaysia consumer price index. Sains Malaysiana 53(4): 969-982.

Matsumura, M. 2022. What price index should central banks target? An open economy analysis. Journal of International Economics 135: 103554.

Meyer, D.F. & Habanabakize, T. 2018. Analysis of relationships and causality between consumer price index (CPI), the producer price index (PPI) and purchasing manager’s index (PMI) in South Africa. Journal of Economics and Behavioral Studies 10(6): 25-32.

Naidu, S., Pandaram, A. & Chand, A. 2017. A Johansen cointegration test for the relationship between remittances and economic growth of Japan. Modern Applied Science 11(10): 137.

Neusser, K. 2016. Time Series Econometrics. Cham, Switzerland: Springer.

Okyere, F. & Kyei, L. 2014. Temporal modelling of producer price inflation rates of Ghana. IOSR Journal of Mathematics 10(3): 70-77.

OpenDOSM. 2023. Producer Prices.https://open.dosm.gov.my/dashboard/producer-prices (Diakses pada 20 Ogos 2023).

Paparoditis, E. & Politis, D.N. 2018. The asymptotic size and power of the augmented Dickey–Fuller test for a unit root. Econometric Reviews 37(9): 955-973.

Popescu, C.C. & Diaconu, L. 2021. Government spending and economic growth: A cointegration analysis on Romania. Sustainability 13: 6575.

Sidaoui, J., Capistrán, C., Chiquiar, D. & Manuel Ramos-Francia, M. 2010. On the predictive content of the PPI on CPI inflation: The case of Mexico. Bank for International Settlements Papers, No: 49. hlm. 249-257.

Soderlind, P. & Vredin, A. 1996. Applied cointegration analysis in the mirror of macroeconomic theory. Journal of Applied Econometrics 11: 363-381.

Stigler, S. 2008. Fisher and the 5% level. Chance 21(4): 12.

Stroe-Kunold, E. & Werner, J. 2009. A drunk and her dog: A spurious relation? Cointegration tests as instruments to detect spurious correlations between integrated time series. Quality & Quantity 43: 913-940.

Sun, J., Xu, J., Cheng, X., Miao, J. & Mu, H. 2023. Dynamic causality between PPI and CPI in China: A rolling window bootstrap approach. International Journal of Rinance & Economics 28(2): 1279-1289.

Swick, R. 2004. Measuring services in the producer price index. Statistical Journal of the United Nations Economic Commission for Europe 21(3-4): 299-308.

Thome, H. 2015. Cointegration and error correction modelling in time-series analysis: A brief introduction. International Journal of Conflict and Violence 8(2): 199-208.

Topuz, Y.V., Yazdifar, H. & Sahadev, S. 2018. The relation between the producer and consumer price indices: A two-country study. Journal of Revenue and Pricing Management 17: 122-130.

Yussuf, Y.C. 2021. Cointegration test for the long-run economic relationships of East Africa community: Evidence from a meta-analysis. Asian Journal of Economics and Banking 6(3): 314-336.

Zhang, J., Hu, W. & Zhang, X. 2010. The relative performance of VAR and VECM model. In Proceedings of the 2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, Kunming, China, 26-28 November. hlm. 132-135.

*Pengarang untuk surat-menyurat; email: kamalmsn@ukm.edu.my