Sains Malaysiana 53(4)(2024): 969-982
http://doi.org/10.17576/jsm-2024-5304-18
Latent
Hidden Factor Model for Malaysia Consumer Price Index
(Model Faktor Pendam bagi Indeks Harga Pengguna Malaysia)
NURULKAMAL
MASSERAN1,2,*,
SITI NORSALSABILA AHMAD FAROUK1, R. NUR-FIRYAL1,2 &
MAHAYAUDIN M. MANSOR3
1Department of Mathematical Sciences, Faculty of
Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi,
Selangor, Malaysia
2Center for Modelling and Data Analysis (DELTA),
Faculty of Science and Technology,
Universiti Kebangsaan Malaysia, 43600
UKM Bangi, Selangor, Malaysia
3School
of Mathematical Sciences, College of Computing, Informatics & Mathematics, Universiti Teknologi MARA, 40450
Shah Alam, Selangor, Malaysia
Diserahkan: 10 Oktober 2023/Diterima: 1 Mac 2024
Abstract
The consumer price
index (CPI) is one of the significant indicators that can be used to track the
inflation rate of a country and assess changes in the cost of living.
Generally, the CPI measures the change in the average price of goods and
services used by households. The increase in inflation can have a negative
socioeconomic effects and the changes in the CPI value needs to be monitored to
ensure that the country does not experience a serious inflation rate. In this
work, exploratory factor analysis (EFA) was employed to interpret the
importance of each variable in the CPI and determine how the latent factors
influence the CPI structure in Malaysia for the period of 2003–2022. The
findings showed that there are two main latent factors that can be formed.
Factor 1 can be classified as ‘Household Expenditures and Lifestyle Choices’
and consists of six variables, namely, ‘alcoholic beverages and tobacco’,
‘housing, water, electricity, gas, and other fuels’, ‘decoration, hardware, and
household maintenance’, ‘health’, ‘recreational and cultural services’, and
‘education’. In contrast, Factor 2 can be classified as ‘Daily Necessities and
Lifestyle Convenience’ which consists of six variables, namely, ‘food and
non-alcoholic beverages’, ‘clothing and shoes’, ‘transportation’,
‘communication’, ‘restaurants and hotels’, and ‘various goods and
services’. In addition, the results of
our analysis showed that Factor 1 is more dominant in influencing the CPI
structure in Malaysia.
Keywords: Factor
analysis; latent structure; statistical model description
Abstrak
Indeks harga pengguna (IHP) adalah salah satu petunjuk penting yang boleh
digunakan untuk mengesan kadar inflasi sesebuah negara dan juga untuk menilai
perubahan dalam kos sara hidup. Secara amnya, IHP mengukur perubahan dalam
harga purata barangan dan perkhidmatan yang digunakan oleh isi rumah. Kenaikan
inflasi boleh mendatangkan kesan sosioekonomi yang negatif dan perubahan nilai
IHP perlu dipantau bagi memastikan negara tidak mengalami kadar inflasi yang
serius. Dalam kajian ini, analisis faktor terokaan (AFT) digunakan untuk
mentafsir kepentingan setiap pemboleh ubah dalam IHP dan menentukan bagaimana
faktor pendam mempengaruhi struktur IHP di Malaysia untuk tempoh 2003–2022.
Keputusan kajian menunjukkan terdapat dua faktor pendam utama yang boleh
dibentuk. Faktor 1 boleh dikelaskan sebagai ‘Perbelanjaan Isi Rumah dan Pilihan
Gaya Hidup’ dan terdiri daripada enam pemboleh ubah, iaitu, ‘minuman beralkohol
dan tembakau’, ‘perumahan, air, elektrik, gas dan bahan api lain’, ‘perkakasan,
hiasan dan penyelenggaraan isi rumah’, ‘kesihatan’, ‘perkhidmatan rekreasi dan
kebudayaan’ dan ‘pendidikan’. Sebaliknya, Faktor 2 boleh dikelaskan sebagai
‘Keperluan Harian dan Kemudahan Gaya Hidup’ yang terdiri daripada enam pemboleh ubah, iaitu ‘makanan dan minuman
bukan alkohol’, ‘pakaian dan kasut’, ‘pengangkutan’, ‘komunikasi’, ‘restoran
dan hotel’ serta ‘barangan dan perkhidmatan lain’. Di samping itu, hasil
analisis kami menunjukkan Faktor 1 lebih dominan dalam mempengaruhi struktur
IHP di Malaysia.
Kata kunci: Analisis faktor; huraian model berstatistik; struktur pendam
RUJUKAN
Abdul
Karim, Z., Mod Asri, N. & Wajdi,
M.F. 2006. Hubungan antara Indeks Harga Pengguna (CPI) mengikut wilayah di
Malaysia. Jurnal Ekonomi Pembangunan 7(2): 184-201.
Amit,
N., Sapiri, H. & Yusof,
Z.M. 2022. Factors affecting housing price in Malaysia using structural
equation modeling approach. Sains Malaysiana 51(12): 4161-4173.
Allen,
R.C. Bassino, J-P., Ma, D., Moll-Murata, C. & Van Zanden, J.L. 2011. Wages, prices, and living
standards in China, 1738–1925: in comparison with Europe, Japan, and India. Econ. Hist. Rev. 64(s1): 8-38.
Backhaus,
K., Erichson, B., Gensler, S., Weiber,
R. & Weiber, T. 2021. Multivariate Analysis: An Application-Oriented Introduction. Wiesbaden:
Springer.
Bartlett,
M.S. 1951. A further note on tests of significance in factor analysis. Br. J. Res. 4: 1-2.
Beavers,
A.S., Lounsbury, J.W., Richards, J.K., Huck, S.W., Skolits, G.J. & Esquivel, S.L. 2013. Practical
considerations for using exploratory factor analysis in educational research. Pract. Assess. Res. Evaluation. 18(1): 6.
Cattell, R.B. 1996. The scree test for the number of factors. Multivariate Behavioral Research 1(2): 245-276. Newsom 2005
Chen,
M. & Hu, X. 2017. Linkage between consumer price index and purchasing power
parity: Theoretic and empirical study. J.
Int. Trade Econ. Dev. 27(7): 729-760.
Chu,
M.Y., Law, T.H., Hamid, H., Law, S.H. & Lee, J.C. 2022. Examining the
effects of urbanization and purchasing power on the relationship between
motorcycle ownership and economic development: A panel data. Int. J. Transp. Sci. Technol. 11(1):
72-82.
Cúrdia, V. &
Woodford, M. 2011. The central-bank balance sheet as an instrument of monetary
policy. J. Monet. Econ. 58(1): 54-79.
Daud, S. 2021. The COVID-19 pandemic
crisis in Malaysia and the social protection program. J. Dev. Soc. 37(4): 480-501.
Department
of Statistics Malaysia. 2020. Indeks Harga Pengguna. https://www.dosm.gov.my/v1/uploads/files/6_Newsletter/Newsletter%202020/DOSM_BPHPP_5-2020_Siri-44.pdf Accessed on 16 May 2023.
Department
of Statistics Malaysia. 2021. Pendapatan purata isi rumah merosot 10.3 peratus kesan pandemik COVID-19 pada tahun 2020. Laporan Anggaran Pendapatan Isi Rumah dan Insiden Kemiskinan, Malaysia. https: //www.dosm.gov.my/v1/uploads/files/5_Gallery/2_Media/4_Stats%40media/4-Press_Statement/2021/8%20Ogos/Kenyataan %20Media%20Anggaran%20kemiskinan%202020%206%20Ogos%202021 _COMBINE.pdf Accessed on 3 July 2023.
Department
of Statistics Malaysia. 2023. Consumer price index. https://www.dosm.gov.my/admin/images/icon/contentdoc_pdf_20220907235910.pdf
Accessed on 29 January 2024.
Gorsuch,
R.L. 2014. Factor Analysis. New York: Routledge.
Gourinchas, P.O. 2022. Policymakers Need Steady Hand as Storm
Clouds Gather over Global Economy. IMF Blog. https://www.imf.org/en/Blogs/Articles/2022/10/11/policymakers-need-steady-hand-as-storm-clouds-gather-over-global-economy
Graf,
B. 2020. Consumer Price Index Manual: Concepts and Methods: Washington:
International Monetary Fund. https://www.elibrary.imf.org/display/book/9781484354841/9781484354841.xml
Accessed on 29 January 2024.
Hair,
J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. 2010. Multivariate Data Analysis. 7th
ed. New York: Prentice Hall International Inc.
Hassan,
M.S. 2022. Kuasa beli menurun, harga barang naik. Harian Metro. https://www.hmetro.com.my/utama/2022/07/864528/kuasa-beli-menurun-harga-barang-naik
Hobijn, B. & Lagakos, D. 2005. Inflation inequality in the United
States. Rev. Income Wealth 51(4):
581-606.
Howard,
M.C. 2023. A systematic literature review of exploratory factor analyses in
management. J. Bus. Res. 164: 113969.
Husaini, D.H., Puah, C-H. & Lean, H.H. 2019. Energy subsidy and oil
price fluctuation, and price behavior in Malaysia: A time series analysis. Energy 171: 1000-1008.
Jaravel, X. &
O'Connell, M. 2020. Real-time price indices: Inflation spike and falling
product variety during the Great Lockdown. J.
Public Econ. 191: 104270.
Kaiser,
H.F. 1970. A second-generation little jiffy. Psychometrika 35(4): 401-415.
Kassim, S.H. & Manap, T.A.A. 2008. Consumer credit and monetary policy in
Malaysia. Int. J. Consum.
Stud. 32(3): 188-193.
Mhd Ruslan,
S.M. & Mokhtar, K. 2020. Structural break and consumer prices: The case of
Malaysia. Cogent Bus. Manag. 7(1): 1767328.
Murdipi, R. & Law,
S.H. 2016. Dynamic linkages between price indices and inflation in Malaysia. Jurnal Ekonomi Malaysia 50(1): 41-52.
Newsom, J.T. 2005. A quick primer on exploratory factor analysis. Available at: https://web.pdx.edu/~newsomj/semclass/ho_efa.pdf. (Accessed 5 July 2023).
Pérez-Barea, J.J., Fernández-Navarro,
F., Montero-Simó, M.J. & Araque-Padilla,
R. 2018. A socially responsible consumption index based on non-linear
dimensionality reduction and global sensitivity analysis. Appl. Soft Comput. 69: 599-609.
Raza,
H., Laurentjoye, T., Byrialsen,
M.R. & Valdecantos, S. 2023. Inflation and the
role of macroeconomic policies: A model for the case of Denmark. Struct. Change Econ. Dyn. 67: 32-43.
Sek, S.K. 2023. A new look at
asymmetric effect of oil price changes on inflation: Evidence from Malaysia. Energy Environ. 34(5): 1524-1547.
Sek, S.K. 2017. Impact of oil price
changes on domestic price inflation at disaggregated levels: Evidence from
linear and nonlinear ARDL modeling. Energy 130: 204-217.
Shaari, A.F. 2019. Peningkatan kos hidup: Apa kata data? Malaysia Kini. https://www.malaysiakini.com/news/497984
Siswanah, E. 2021.
Mathematical Study: The CPI in the expenditure sub group using factor analysis. J. Phys.: Conf. Ser. 1796: 012118.
Tabachnick, B.G. & Fidell, L.S. 2019. Using
Multivariate Statistics. Boston: Pearson Education.
Victor,
V., Joy Thoppan, J., Jeyakumar Nathan, R. & Farkas Maria, F. 2018. Factors
influencing consumer behavior and prospective purchase decisions in a dynamic
pricing environment - An exploratory factor analysis approach. Soc. Sci. 7: 153.
Watkins,
M. 2021. A Step-by-Step Guide to
Exploratory Factor Analysis with Stata. New York: Routledge.
Yusof, N., Nin, L.F., Kamal, H.K.M., Taslim, J.R.A. & Zainoddin,
A.I. 2021. Factors that influence the inflation rate in Malaysia. Int. J. Acad. Res. Bus. Soc. Sci. 11(9):
626-637.
*Pengarang untuk surat-menyurat; email: kamalmsn@ukm.edu.my
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