Machine learning approaches for forecasting inflation: empirical evidence from Sri Lanka
Abstract
The aim of this study is to forecast the inflation rate using supervised machine learning models (SMLM). While SMLMs are widely used in various fields, they have also been widely applied to forecast 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 used: K-fold cross-validation method (KCV) and walk forward validation (WFV) methods. These techniques were used to estimate the parameters and hyper-parameters for each machine learning model with the aid of 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 LR 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 demonstrates the effectiveness of SMLMs in forecasting inflation rates under the critical conditions and highlights the importance of employing appropriate cross-validation techniques when using these SMLM models. The findings of this study can provide valuable insights for policymakers, investors, and researchers who are interested in forecasting inflation rates. Keywords: Cross-Validation, Hyper-parameter, Inflation Forecasting, Machine Learning Models.References
Atkeson A, Ohanian LE. 2001. Are Phillips curves useful for forecasting inflation? Quarterly Review 25(1): 2-11.
Armstrong JS, Collopy F. 1992. Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting 8(1): 69-80.
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.
Bergmeir C, BenÃtez J. 2012. On the use of cross-validation for time series predictor evaluation. Information Sciences 191: 192-213.
Cristadoro R, Mario F, Reichlin L, Veronese G. 2005. A core inflation indicator for euro area. Journal of Money, Credit and Banking 37(3): 539-560.
Cutler A, Cutler D, Stevens JR. 2012. Ensemble Machine Learning: Methods and Applications. New York: Springer.
Goodhart C, Hofmann B. 2000. Do asset prices help to predict consumer price inflation? The Manchester School 68(s1):122-140:55-67.
Granger C, Jeon Y. 2004. Thick modeling. Economic Modelling 21(2): 323-343.
Hoerl AE, Kennard RW. 1970. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics 12(1).
Hyndman RJ, Koehler AB. 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.
Jere S, Mubita S. 2016. Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing. Open Journal of Statistics 6(2): 363-372.
Jesmy A. 2010. Estimation of future inflation in Sri Lanka using ARIMA model. Kalam 5(1): 21-27.
Marcellino M. 2002. Forecast Pooling for Short Time Series of Macroeconomic Variables. IGIER Innocenzo Gasparini Institute for Economic Research :Working Paper No 213.
Marcellino M, Stock J H, Watson M W. 2000. A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU. Italian Economic Journal 8(2): 289-338.
Ogutu J, Schulz S, Torben P. 2012. Genomic selection using regularized linear regression models: Ridge regression, lasso, elastic net and their extensions. BMC Proceedings 6:10.
Smola AJ, Schölkopf B. 2003. A tutorial on support vector regression. Statistics and Computing 14(3): 199-222.
Stock JH, Watson MW. 1999. Forecasting inflation using a new index of aggregate activity. Journal of Monetary Economics 44(2): 293-335.
Tradingview. 2023. Sri Lanka Inflation Rate YoY. SL: Trading View.
Trevor H, Robert T, Jerome F. 2009. The Elements of Statistical Learning. 2 ed. New York: Springer.
Ulke V, Sahin A, Subasi A. 2018. Comparison of Time Series and Machine Learning Models for Inflation Forecasting: Empirical Evidence from the US. Neural Computing and Applications 30(1): 1519-1527.
Wickham H. 2016. ggplot2: Elegant graphics for data analysis. 2 ed. Melbourne: Springer.
Wright JH. 2003. Forecasting U.S. Inflation by Bayesian Model Averaging. International Finance Discussion Paper 9: 1-33.
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