DOI: https://doi.org/10.14710/medstat.13.1.1-12

COMPARISON OF ARIMA, TRANSFER FUNCTION AND VAR MODELS FOR FORECASTING CPI, STOCK PRICES, AND INDONESIAN EXCHANGE RATE: ACCURACY VS. EXPLAINABILITY

*Taly Purwa orcid scopus  -  Badan Pusat Statistik (BPS) Provinsi Bali, Indonesia
Ulin Nafngiyana  -  Badan Pusat Statistik (BPS) Kabupaten Trenggalek, Indonesia
Suhartono Suhartono orcid scopus  -  Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember (ITS), Indonesia
Open Access Copyright (c) 2020 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
The Consumer Price Index (CPI), stock prices and the rupiah exchange rate to the US dollar are important macroeconomic variables which their movements show the economic performance and can affect the monetary and fiscal policies of Indonesia. This makes forecasting effort of these variables become important for policy planning. While many previous studies only focus on examining the effect among macroeconomic variables, this study uses ARIMA (univariate method), transfer function and VAR (multivariate methods) to measure the forecasting accuracy and also observing the effect between these macroeconomic variables. The results showed that the multivariate methods gave better explanation about the relationship between variables than the simple one. Otherwise, the results of accuracy comparison showed that the multivariate methods did not always yield better forecast than the simple one, and these conditions in line with the results and conclusions of M3 and M4 competition.

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Keywords: CPI; Stock Price; Exchange Rate; ARIMA; Transfer Function; VAR

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  1. Box, G.E.P. & Jenkins, G. . (1976). Time Series Analysis, Forecasting, and Control. San Francisco: Holden-Day
  2. Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time Series Analysis : Forecasting and Control (3rd Editio). Englewood Cliffs, NJ: Prentice Hall
  3. BPS. (2018). Consumer Price Index of 82 Cities in Indonesia 2017 (2012=100). Retrieved from https://www.bps.go.id/publication/2018/04/05/7adc5bb2f80f4645e0ec6c5c/indeks-harga-konsumen-di-82-kota-di-indonesia--2012-100--2017.html
  4. Firmansyah, F., & Oktavilia, S. (2017). The Stock Market and Exchange Rates in Five South Asian Countries. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan, 18(1), 102. https://doi.org/10.23917/jep.v18i1.4293
  5. Hyndman, R.J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd editio). Retrieved from OTexts.com/fpp2
  6. Imran Hunjra, A., Irfan Chani, M., Shahzad Ijaz, M., Farooq, M., Khan, K., & Shahzad, M. (2014). MPRA The Impact of Macroeconomic Variables on Stock Prices in Pakistan. International Journal of Economics and Empirical Research, 2(21), 13–21. Retrieved from http://www.tesdo.org/Publication.aspx
  7. Indonesia Stock Exchange. (2010). User Guide for Stock Prices Index of Indonesia Stock Exchange (IDX). Jakarta: Bursa Efek Indonesia
  8. Karim, B. A., Hwang, J. Y., Kadri, N., & Husaini, D. H. (2018). Stock Prices and Exchange Rates in Indonesia: Further Evidence. UNIMAS Review of Accounting and Finance, 1(1), 92–98. https://doi.org/10.33736/uraf.1212.2018
  9. Korkmaz, S., Goksuluk, D., & Zararsiz, G. (2014). MVN : An R Package for Assessing Multivariate Normality. The R Journal, 6(December), 151–162. Retrieved from https://journal.r-project.org/archive/2014/RJ-2014-031/RJ-2014-031.pdf
  10. Liang, C. C., Chen, M. Y., & Yang, C. H. (2015). The Interactions of Stock Prices and Exchange Rates in the ASEAN-5 Countries: New Evidence Using a Bootstrap Panel Granger Causality Approach. Global Economic Review, 44(3), 324–334. https://doi.org/10.1080/1226508X.2015.1035300
  11. Lutkepohl, H. (1993). Introduction to Multiple Time Series Analysis (2nd Editio). Berlin: Springer-Verlag
  12. Makridakis, S., & Hibon, M. (2000). The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 16, 451–476
  13. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018a). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLoS ONE, 13(3), 1–26. https://doi.org/10.1371/journal.pone.0194889
  14. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018b). The M4 Competition: Results, findings, conclusion and way forward. International Journal of Forecasting, 34(4), 802–808. https://doi.org/10.1016/j.ijforecast.2018.06.001
  15. Mgammal, M. H. H. (2012). The Effect of Inflation, Interest Rates and Exchange Rates on Stock Prices: Comparative Study Among Two Gcc Countries. International Journal of Finance and Accounting, 1(6), 179–189. https://doi.org/10.5923/j.ijfa.20120106.06
  16. Montgomery, D. C., & Weatherby, G. (1980). Modeling and forecasting time series using transfer function and intervention methods. AIIE Transactions, 12(4), 289–307. https://doi.org/10.1080/05695558008974521
  17. Nkoro, E., & Uko, A. K. (2016). Exchange Rate and Inflation Volatility and Stock Prices Volatility : Evidence from Nigeria , 1986-2012. Journal of Applied Finance and Banking, 6(6), 57–70
  18. Ogboghro, V. I. (2017). The Impart of Inflation and Exchange Rates on Stock Prices in Nigeria. Account and Financial Management Journal, 2(6), 748–757. https://doi.org/10.18535/afmj/v2i6.01
  19. Okechukwu, I. A., Mbadike, N. S., Geoffrey, U., & Ozurumba, B. A. (2019). Effects of Exchange Rate , Interest Rate , and Inflation on Stock Market Returns Volatility in Nigeria. International Journal of Management Science and Business Administration, 5(6), 38–47. https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.56.1005
  20. Pantas, P. E., Ryandono, M. N. H., Munir, M., & Wahyudi, R. (2019). Cointegration of Stock Market and Exchange Rate in Indonesia. Ihtifaz: Journal of Islamic Economics, Finance, and Banking, 2(2), 125. https://doi.org/10.12928/ijiefb.v2i2.886
  21. Parsva, P., & Tang, C. F. (2017). A note on the interaction between stock prices and exchange rates in Middle-East economies. Economic Research-Ekonomska Istrazivanja , 30(1), 836–844. https://doi.org/10.1080/1331677X.2017.1311222
  22. Putra, D. A. A. (2016). The Effect of Rupiah/US$ Exchange Rate, Inflation and SBI Interest Rate on Composite Stock Price Index (CSPI) in Indonesia Stock Exchange. International Conference on Education, 202–214
  23. Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. https://doi.org/10.1017/CBO9781107415324.004
  24. Stephani, C. A., Suharsono, A., & Suhartono. (2015). Peramalan Inflasi Nasional Berdasarkan Faktor Ekonomi Makro Menggunakan Pendekatan Time Series Klasik dan ANFIS. Sains Dan Seni ITS, 4(1), 67–72
  25. Suhartono, S., & Subanar, S. G. (2005). a Comparative Study of Forecasting Models for Trend and Seasonal Time Series Does Complex Model Always Yield Better Forecast Than Simple Models. Jurnal Teknik Industri, 7(1), 22–30. https://doi.org/10.9744/jti.7.1.pp.22-30
  26. Thomakos, D. D., & Guerard, J. B. (2004). Naïve, ARIMA, nonparametric, transfer function and VAR models: A comparison of forecasting performance. International Journal of Forecasting, 20(1), 53–67. https://doi.org/10.1016/S0169-2070(03)00010-4
  27. Wahyudi, S., Hersugondo, H., Laksana, R. D., & Rudy, R. (2017). Macroeconomic Fundamental and Stock Price Index in Southeast Asia Countries A Comparative Study. International Journal of Economics and Financial Issues, 7(2), 182–187
  28. Wei, W. W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods. https://doi.org/10.2307/2289741
  29. Yogaswari, D. D., Nugroho, A. B., & Astuti, N. C. (2012). The Effect of Macroeconomic Variables on Stock Price Volatility: Evidence from Jakarta Composite Index, Agriculture, and Basic Industry Sector. International Proceedings of Economics Development and Research, 46(18), 96–100. https://doi.org/10.7763/IPEDR

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