Evaluating the Efficacy of Supervised Machine Learning Models in Inflation Forecasting in Sri Lanka

Authors

DOI:

https://doi.org/10.54536/ajase.v3i1.2385

Keywords:

Cross-Validation, Macro Economic, Hyper-Parameter, Inflation Forecasting, Machine Learning

Abstract

This study aims to forecast the inflation rate using supervised machine learning models (SMLM). While SMLMs are widely used in various fields, they have not been widely applied in forecasting inflation rates. Therefore, the main objective of this study is to identify the best model for forecasting inflation among four different SMLMs: LASSO regression (LR), Bayesian Ridge Regression (BRR), Support Vector Machine Regression (SVR), and Random Forest Regression (RFR) models. To achieve this objective, two different types of cross-validation techniques were employed: The k-fold cross-validation method (CVK) and walk forward validation (WFV) methods. These techniques were used to estimate the parameters and hyper-parameters for each machine learning model with root mean square error. The mean absolute percentage error (MAPE) was used to compare the performance of the different SMLMs. Empirical evidence from Sri Lanka between 1988 and 2021 was used to test the performance of the SMLMs in forecasting inflation rates. The results show that the SVR model with walk-forward validation is the best method for forecasting the future inflation rate of Sri Lanka based on the MAPE value. Overall, this study showcases the effectiveness of Supervised Machine Learning Models (SMLMs) in forecasting inflation rates, emphasizing the critical role of precise cross-validation techniques. These findings are invaluable for policymakers and investors, offering advanced tools for more informed economic decision-making and highlighting the potential of machine learning in enhancing macroeconomic stability and forecasting accuracy.

Downloads

Download data is not yet available.

References

Anagaw, T. (2023). Review on: Effect of Inflation on Economic Growth in Ethiopia. American Journal of Applied Statistics and Economics, 2(1), 7–10. https://doi.org/10.54536/ajase.v2i1.1658

Armstrong, J. S. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of forecasting, 8(1), 69-80.

Atkeson, A. O. (2001). Are Phillips curves useful for forecasting inflation. pp. 2–11.

Bandara, R. (2011). The Determinants of Inflation in Sri Lanka: An Application of the Vector Autoregression Model. South Asia Economic Journal, 12(2), 271-286.https://doi.org/10.1177/139156141101200204

Bandara,W. M. S & De Mel, W. A. R. (2021). ARIMA-Neural Hybrid Estimates of Inflationary Expectations: Some Evidence from Sri Lanka. 20th Academic Sessions Univercity of Ruhuna, 1(1), 1. Retrieved from http://ir.lib.ruh.ac.lk/handle/iruor/13401

Bergmeir, C., & Benítez, J. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213. https://doi.org/ 10.1016/j.ins.2011.12.028

Cristadoro, R. M. (2005). A core inflation indicator for euro area. Journal of Money, Credit and Banking, 37(3), 539-560. Retrieved from http://www.jstor.org/stable/3839167.

Cutler, A., Cutler, D., & Stevens, J. R. (2012). Ensemble Machine Learning: Methods and Applications. (C. Zhang, & Y. Ma, Eds.) New york: Springer. https://doi.org/10.1007/978-1-4419-9326-7_5

Goodhart, C. a. (2000). Do asset prices help to predict consumer price inflation? (Vol. 5). Manchester School, . Retrieved from https://ssrn.com/abstract=242532

Granger, C. a. (2004). Thick modeling. Economic Modelling, 21(2), 323-343. Retrieved from https://EconPapers.repec.org/RePEc:eee:ecmode:v:21:y:2004:i:2:p:323-343

Hoerl, A. E. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1). Retrieved from https://doi.org/10.2307/1267351

Hyndman, R. J. (2006). Another look at measures of forecast accuracy. International Journal of forecasting, 22(4), 679-688.

Inoue, A., & Kilian, L. (2006). On the selection of forecasting models. Journal of Econometrics, 137(2), 273–306. https://doi.org/10.1016/j.jeconom.2005.03.003

Jaehyuk Choi, D. G. (2023). Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach. Journal of Forecasting, 42, 7.

Jayasooriya, D. (2015). MONEY SUPPLY AND INFLATION: EVIDENCE FROM SRI. Asian Studies International Journal, 1(1), 28.

Jere, S. ,. (2016). Forecasting inflation rate of zambia using holt’s exponential. Open Journal of Statistics, 363-372. https://doi.org/ 10.4236/ojs.2016.62031

Jesmy, A. (2010). Estimation of future inflation in Sri Lanka using ARIMA model. ,. Kalam, 21-27.

Maldeni, R. &. (2021). A Machine Learning Approach to CCPI-Based Inflation Prediction. Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, p. 236. https://doi.org/10.1007/978-981-16-2380-6_50

Malladi, R. K. (2023). enchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period. Computational Economics. https://doi.org/10.1007/s10614-023-10436-w

Marcellino, M. (2002). Forecast Pooling for Short Time Series of Macroeconomic Variables. IGIER Innocenzo Gasparini Institute for Economic Research. Retrieved from https://ideas.repec.org/p/igi/igierp/212.html

Marcellino, M. S. (2000). A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU. Ital Econ J 8, 289–338. https://doi.org/10.1007/s40797-021-00155-2

Ogutu, J., Schulz, S., & Torben, P. (2012). Genomic selection using regularized linear regression models: Ridge regression, lasso, elastic net and their extensions. BMC proceedings, 6, S10. https://doi.org/10.1186/1753-6561-6-S2-S10

Rahman, M. A., Kabir, M. A., Haque, M. E., & Hossain, B. M. (2021). A Machine Learning-Based Price Prediction for Cows. merican Journal of Agricultural Science, Engineering, and Technology, 5(1), 64–69. https://doi.org/10.54536/ajaset.v5i1.63

Smola, A. J., & Schölkopf, B. (2003). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88

Stock, J. H. (1999). Forecasting Inflation new index of aggregate activity. Journal of Monetary Economics, 44(2), 293-335. https://doi.org/10.1016/S0304-3932(99)00027-6

Tradingview. (2023). Sri Lanka Inflation Rate YoY. Trading View. Retrieved from https://www.tradingview.com/symbols/economics-lkiryy/

Trevor Hastie, R. T. (2009). The Elements of Statistical Learning (2 ed.). New York: Springer. https://doi.org/10.1007/978-0-387-84858-7

Volkan, U., Afsin, S., & Abdulhamit, S. (2018). Comparison of Time Series and Machine Learning Models for Inflation Forecasting: Empirical Evidence from the US. Neural Computing and Applications, 30, 1519–1527. https://doi.org/10.1007/s00521-016-2766-x

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed.). Melbourne: Springer.

Wright, J. H. (2003, September). Forecasting U.S. Inflation by Bayesian Model Averaging (September 2003). International Finance Discussion Paper, 1-33.http://dx.doi.org/10.2139/ssrn.457360

Downloads

Published

2024-02-12

How to Cite

Bandara, W. M. S., & De Mel, W. A. R. (2024). Evaluating the Efficacy of Supervised Machine Learning Models in Inflation Forecasting in Sri Lanka. American Journal of Applied Statistics and Economics, 3(1), 51–60. https://doi.org/10.54536/ajase.v3i1.2385