Time Series Analysis and Forecasting of Household Products’ Prices (A Case Study of Nyeri County):: Science Publishing Group

Time Series Analysis and Forecasting of Household Products’ Prices (A Case Study of Nyeri County)

Muriuki Brian Muriithi¹, Waiguru Samuel²

¹Department of Pure and Applied Sciences, Kirinyaga University, Kerugoya, Kenya

²Department of Mathematics and Statistics, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya

Mathematical Modelling and Applications, Volume 8, Issue 1, March 2023

Pages: 1-12

Received: Mar. 17, 2022

Accepted: Jan. 31, 2023

Published: May 22, 2023

DOI: 10.11648/j.mma.20230801.11

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Abstract

Inflation has a significant impact on both consumable and non-consumable products and plays a critical role in determining the cost of living. The study aimed to investigate the trend of household consumable and non-consumable prices over the past three years and identify the best ARIMA model for future price predictions. The results showed that consumable goods played a greater role in determining the national inflation compared to non-consumable goods. A relationship was found between the changes in local-level prices and national monthly inflation rates, with consumable goods being fitted to an ARIMA (1,2,2) model and national inflation rates to ARIMA (3,1,0). Non-consumable goods were found to be a white noise. The models were found to be adequate in forecasting changes in prices, with their validity confirmed by the Box-Ljung test and autocorrelation coefficients of model residuals. This study demonstrated the importance of analyzing changes in products’ prices at a local level and how it affects the national inflation rate. In future, similar studies can be carried out in different counties and with a more comprehensive model to investigate the impact of the COVID-19 pandemic on the prices of household consumable and non-consumable goods at the local level.

Keywords

ARIMA Model, Consumable Goods, Non-Consumable Goods, Inflation

To cite this article

Muriuki Brian Muriithi, Waiguru Samuel. (2023). Time Series Analysis and Forecasting of Household Products’ Prices (A Case Study of Nyeri County). Mathematical Modelling and Applications, 8(1), 1-12. https://doi.org/10.11648/j.mma.20230801.11

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References

1.	Dornbusch, R. (2001). Fewer monies, better monies. American Economic Review, 91 (2), 238-242.
2.	Etuk, E. H., & Mohamed, T. M. (2014). Full Length Research Paper Time Series Analysis of Monthly Rainfall data for the Gadaref rainfall station, Sudan, by Sarima Methods. 2 (7), 320–327.
3.	Tyralis, H., & Papacharalampous, G. (2017). Variable selection in time series forecasting using random forests. Algorithms, 10 (4), 114.
4.	Carta, S., Medda, A., Pili, A., Reforgiato Recupero, D., & Saia, R. (2019). Forecasting e- commerce products prices by combining an autoregressive integrated moving average (ARIMA) model and google trends data. Future Internet, 11 (1), 5.
5.	Huwiler, M., & Kaufmann, D. (2013). Combining disaggregate forecasts for inflation. The SNB's ARIMA model. Swiss National Bank Economic Studies, (7).
6.	Nti, K. O., Adekoya, A., & Weyori, B. (2019). Random forest-based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16 (7), 200-212.
7.	Budiastuti, I. A., Nugroho, S. M. S., & Hariadi, M. (2017, July). Predicting daily consumer price index using support vector regression method. In 2017 15th International Conference on Quality in Research (QiR). International Symposium on Electrical and Computer Engineering (pp. 23-28). IEEE.
8.	Brockwell, P. J., & Davis, R. A. (n.d.). Introduction to Time Series and Forecasting.
9.	Bryan, M., Cecchetti, S. G. (1993). The Consumer Price Index as a Measure of Inflation. Pdf. In The consumer Price Index as a measure of inflation (pp. 3–23).
10.	Conejo, A. J., Plazas, M. A., Espínola, R., Member, S., & Molina, A. B. (2005). Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. 20 (2), 1035–1042.
11.	Fisher, J. M. D., Liu, C. Te, & Zhou, R. (2002). When can we forecast inflation? Economic Perspectives-Federal Reserve Bank of Chicago, 26.1 (2 SPEC. ISS.), 32–44.
12.	Jenkins, G. M., Reinsel, G. C., Ljung, G. M., Wiley, J., Box, G. E. P., Jenkins, G. M., Reinsel, G. C., Ljung, G. M., & Wiley, J. (2019). Time Series Analysis. Forecasting and Control, 5th Edition, by George E. P. BOOK REVIEW TIME SERIES ANALYSIS. FORECASTING AND CONTROL. March 2016. https.//doi.org/10.1111/jtsa.12194
13.	Jiang, S., Yang, C., Guo, J., & Ding, Z. (2018). ARIMA forecasting of China’s coal 31 consumption, price and investment by 2030. Energy Sources, Part B. Economics, Planning, and Policy, 13 (3), 190–195. https.//doi.org/10.1080/15567249.2017.1423413
14.	Karanja, A. M., Kuyvenhoven, A., & Moll, H. A. J. (2003). Economic reforms and evolution of producer prices in Kenya. An ARCH-M approach. African Development Review, 15 (2–3), 271–296. https.//doi.org/10.1111/j.1467-8268.2003.00074.x
15.	Ke, Z., & Zhang, Z. J. (2018). Testing autocorrelation and partial autocorrelation. Asymptotic methods versus resampling techniques. British Journal of Mathematical and Statistical Psychology, 71 (1), 96–116. https.//doi.org/10.1111/bmsp.12109.
16.	Loayza, N., & Schmidt-hebbel, K. (2002). MONETARY POLICY F UNCTIONS AND T RANSMISSION M ECHANISMS. A N O VERVIEW. 1–20.
17.	Maiti, & Bidinger. (1981). No Title No Title. Journal of Chemical Information and Modeling, 53 (9), 1689–1699.
18.	Mankiw, N. G. (2001). THE INEXORABLE AND MYSTERIOUS TRADEOFF BETWEEN INFLATION AND UNEMPLOYMENT Ã 1. What is the In ¯ ation-unemployment Tradeoff? 111, 45–61.
19.	Meyler, a, Kenny, G., & Quinn, T. (1998). Forecasting Irish inflation using ARIMA models. Central Bank and Financial Services Authority of Ireland Technical Paper Series, 3 (July), 1–48.
20.	Parkin, M., Bryant, R. C., & Jenkins, P. (1993). Inflation in North America. Price Stabilization in the 1990s, 47–93. https.//doi.org/10.1007/978-1-349-12893-8_
21.	Personal, M., & Archive, R. (2011). Determinants of Recent Inflation in Ethiopia Sisay Menji. 29668.
22.	Profile, S. E. E. (2020). MODELLING UNEMPLOYMENT RATE USING BOX-JENKINS PROCEDURE MODELLING UNEMPLOYMENT RATE USING BOX-JENKINS PROCEDURE. January 2008.
23.	Taylor, P., Dickey, D. A., Fuller, W. A., Dickey, D. A., & Fuller, W. A. (2012). Journal of the American Statistical Association Distribution of the Estimators for Autoregressive Time Series with a Unit Root Distribution of the Estimators for Autoregressive Time Series with a Unit Root. March 2013, 37–41.
24.	Moazam, M., & Kemal, M. A. (2016). Inflation in Pakistan: Money or oil prices.
25.	Ozturk, Suat, and Feride Ozturk. Forecasting energy consumption of Turkey by Arima model. Journal of Asian Scientific Research 8.2 (2018): 52-60.