Main Article Content
Abstract
Implication: The findings are expected to be useful as a guide for central banks and policy-makers in emerging economies with volatile inflation rates.
Originality: We evaluate the forecasting performance of ML models against each other and a time series model, and investigate possible improvements upon the naive model. So, this is the first study in the field, which uses both linear and nonlinear ML methods to make a comparison with the time series inflation forecasts for Turkey.Keywords
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Copyright (c) 2022 Hale Akbulut
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References
- Almosova, A., & Andresen, N. (2019). Nonlinear inflation forecasting with recurrent neural networks (2019/5; European Central Bank Technical Report).
- Altimari, S. N. (2001). Does Money Lead Inflation in the Euro Area? (No. 63; Working Paper Series).
- Ball, L., & Mazumder, S. (2019). A Phillips Curve with Anchored Expectations and Short-Term Unemployment. Journal of Money, Credit and Banking, 51(1), 111–137. https://doi.org/10.1111/jmcb.12502
- Ball, L., & Mazumder, S. (2020). A Phillips curve for the euro area (No. 2354; ECB Working Paper Series).
- Banerjee, A., Cockerell, L., & Russell, B. (2001). An I(2) analysis of inflation and the markup. Journal of Applied Econometrics, 16(3), 221–240. https://doi.org/10.1002/jae.609
- Barkan, O., Benchimol, J., Caspi, I., Cohen, E., Hammer, A., & Koenigstein, N. (2021). Forecasting CPI inflation components with hierarchical recurrent Neural Network. In In preparation: Revise and Resubmit, International Journal of Forecasting.
- Baybuza, I. (2018). Inflation forecasting using machine learning methods. Russian Journal of Money and Finance, 77(4), 42–59. https://doi.org/10.31477/rjmf.201804.42
- Bennouna, H. (2015). A mark-up model of inflation for Morocco. International Journal of Economics and Financial Issues, 5(1), 281–287.
- Brouwer, G. De, & Ericsson, N. R. (1998). Modeling Inflation in Australia. Journal of Business and Economic Statistics, 16(4), 433–449. https://doi.org/10.1080/07350015.1998.10524783
- Bulut, U. (2016). Do financial conditions have a predictive power on inflation in Turkey? International Journal of Economics and Financial Issues, 6(2), 621–628.
- Callen, T., & Chang, D. (1999). Modeling and Forecasting Inflation in India (WP/99/119; IMF Working Paper).
- CBRT. (2021). Central bank of Republic of Turkey, electronical data distribution system.
- Chakraborty, C., & Joseph, A. (2017). Machine learning at central banks (No. 674).
- Chang, J. Y., Pigorini, A., Massimini, M., Tononi, G., Nobili, N., & Van Veen, B. D. (2012). Multivariate autoregressive models with exogenous inputs for intracerebral responses to direct electrical stimulation of the human brain. Frontiers in Human Neuroscience, 6(317). https://doi.org/10.3389/fnhum.2012.00317
- Chen, Y. G. (2019). Inflation, Inflation Expectations, and the Phillips Curve (No. 2019–17; Working Paper Series).
- Christopher, B., & Jansen, E. S. (2004). A markup model of inflation for the euro area (Working Paper Series 306). http://ssrn.com/abstract_id=515068
- Coe, D. T., & McDermott, C. J. (1997). Does the Gap Model Work in Asia? IMF Staff Papers, 44(1), 59–80. https://doi.org/10.2307/3867497
- Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 76–66. https://doi.org/10.2307/2286348
- Garcia, M. G., Medeiros, M. C., & Vasconcelos, G. (2017). Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting, 33(3), 679–693. https://doi.org/10.1016/j.ijforecast.2017.02.002
- Gungor, C., & Berk, A. (2006). Money supply and inflation relationship in the Turkish Economy. Journal of Applied Science, 6(9), 2083–2087. https://doi.org/10.3923/jas.2006.2083.2087
- Hoerl, A. E., & Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634
- Jonsson, G. (2001). Inflation, money demand, and purchasing power parity in South Africa. IMF Staff Papers, 48(2), 243–265. https://doi.org/10.5089/9781451854473.001
- Kumar, V., Leona, R. P., & Gaskins, J. N. (1995). Aggregate and disaggregate sector forecasting using consumer confidence measures. International Journal of Forecasting, 11(3), 361–377. https://doi.org/10.1016/0169-2070(95)00594-2
- Lim, C. H., & Papi, L. (1997). An econometric analysis of the determinants of inflation in Turkey (97/170; IMF Working Paper).
- Medeiros, M. C., Vasconcelos, G. F., & De Freitas, E. H. (2016). Forecasting Brazilian inflation with high dimensional models. Brazilian Review of Econometrics, 36(2), 223–254. https://doi.org/10.12660/bre.v99n992016.52273
- Medeiros, M. C., Vasconcelos, G. F. R., Veiga, Á., & Zilberman, E. (2021). Forecasting inflation in a data-rich environment: The benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98–119. https://doi.org/10.1080/07350015.2019.1637745
- Mishkin, F. S. (2000). Para teorisi ve politikası (The Economics of money, banking, and financial markets, financial times prentice hall). Bilim Teknik Yayınevi.
- MTFRT. (2021). Republic of Turkey Ministry of treasury and finance: Budget Statistics.
- Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: A guide for data scientists. O’Reilly Media, Inc.
- Nakamura, E. (2005). Inflation forecasting using a neural network. Economic Letters, 86(3), 373–378. https://doi.org/10.1016/j.econlet.2004.09.003
- Önder, A. Ö. (2004). Forecasting Inflation in Emerging Markets by Using the Phillips Curve and Alternative Time Series Models. Emerging Markets Finance and Trade, 40(2), 71–82. https://doi.org/10.1080/1540496x.2004.11052566
- Özgür, Ö., & Akkoç, U. (2021). Inflation forecasting in an emerging economy: Selecting variables with machine learning algorithms. In International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-05-2020-0577
- Rodríguez-Vargas, A. (2020). Forecasting Costa Rican inflation with machine learning methods. Latin American Journal of Central Banking, 1(1), 100012. https://doi.org/https://doi.org/10.1016/j.latcb.2020.100012
- Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. In Psychological Review (Vol. 65, Issue 6, pp. 386–408). American Psychological Association. https://doi.org/10.1037/h0042519
- Sims, C. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017
- Stock, J. H., & Watson, M. W. (2013). Phillips Curve Inflation Forecasts. In Understanding Inflation and the Implications for Monetary Policy (No. 14322; NBER Working Paper). https://doi.org/10.7551/mitpress/9780262013635.003.0003
- Stock, J., & Watson, M. (1999). A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. In R. Engle & H. White (Eds.), Cointegration, Causality and Forecasting: A Festschrift for Clive W.J. Granger (pp. 1–44). Oxford University Press.
- Tibshirani, R. (2016). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B ( Methodological ), 58(1), 267–288.
- Ülke, V., Sahin, A., & Subasi, A. (2018). A comparison of time series and machine learning models for inflation forecasting: Empirical evidence from the USA. Neural Computing and Applications, 30(5), 1519–1527. https://doi.org/10.1007/s00521-016-2766-x
- Wojciech, D., & Derek, F. D. (1992). New directions in econometric practice general to spesific modelling, cointegration and vector autoregressions. Edward Elgar Publishing.
References
Almosova, A., & Andresen, N. (2019). Nonlinear inflation forecasting with recurrent neural networks (2019/5; European Central Bank Technical Report).
Altimari, S. N. (2001). Does Money Lead Inflation in the Euro Area? (No. 63; Working Paper Series).
Ball, L., & Mazumder, S. (2019). A Phillips Curve with Anchored Expectations and Short-Term Unemployment. Journal of Money, Credit and Banking, 51(1), 111–137. https://doi.org/10.1111/jmcb.12502
Ball, L., & Mazumder, S. (2020). A Phillips curve for the euro area (No. 2354; ECB Working Paper Series).
Banerjee, A., Cockerell, L., & Russell, B. (2001). An I(2) analysis of inflation and the markup. Journal of Applied Econometrics, 16(3), 221–240. https://doi.org/10.1002/jae.609
Barkan, O., Benchimol, J., Caspi, I., Cohen, E., Hammer, A., & Koenigstein, N. (2021). Forecasting CPI inflation components with hierarchical recurrent Neural Network. In In preparation: Revise and Resubmit, International Journal of Forecasting.
Baybuza, I. (2018). Inflation forecasting using machine learning methods. Russian Journal of Money and Finance, 77(4), 42–59. https://doi.org/10.31477/rjmf.201804.42
Bennouna, H. (2015). A mark-up model of inflation for Morocco. International Journal of Economics and Financial Issues, 5(1), 281–287.
Brouwer, G. De, & Ericsson, N. R. (1998). Modeling Inflation in Australia. Journal of Business and Economic Statistics, 16(4), 433–449. https://doi.org/10.1080/07350015.1998.10524783
Bulut, U. (2016). Do financial conditions have a predictive power on inflation in Turkey? International Journal of Economics and Financial Issues, 6(2), 621–628.
Callen, T., & Chang, D. (1999). Modeling and Forecasting Inflation in India (WP/99/119; IMF Working Paper).
CBRT. (2021). Central bank of Republic of Turkey, electronical data distribution system.
Chakraborty, C., & Joseph, A. (2017). Machine learning at central banks (No. 674).
Chang, J. Y., Pigorini, A., Massimini, M., Tononi, G., Nobili, N., & Van Veen, B. D. (2012). Multivariate autoregressive models with exogenous inputs for intracerebral responses to direct electrical stimulation of the human brain. Frontiers in Human Neuroscience, 6(317). https://doi.org/10.3389/fnhum.2012.00317
Chen, Y. G. (2019). Inflation, Inflation Expectations, and the Phillips Curve (No. 2019–17; Working Paper Series).
Christopher, B., & Jansen, E. S. (2004). A markup model of inflation for the euro area (Working Paper Series 306). http://ssrn.com/abstract_id=515068
Coe, D. T., & McDermott, C. J. (1997). Does the Gap Model Work in Asia? IMF Staff Papers, 44(1), 59–80. https://doi.org/10.2307/3867497
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 76–66. https://doi.org/10.2307/2286348
Garcia, M. G., Medeiros, M. C., & Vasconcelos, G. (2017). Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting, 33(3), 679–693. https://doi.org/10.1016/j.ijforecast.2017.02.002
Gungor, C., & Berk, A. (2006). Money supply and inflation relationship in the Turkish Economy. Journal of Applied Science, 6(9), 2083–2087. https://doi.org/10.3923/jas.2006.2083.2087
Hoerl, A. E., & Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634
Jonsson, G. (2001). Inflation, money demand, and purchasing power parity in South Africa. IMF Staff Papers, 48(2), 243–265. https://doi.org/10.5089/9781451854473.001
Kumar, V., Leona, R. P., & Gaskins, J. N. (1995). Aggregate and disaggregate sector forecasting using consumer confidence measures. International Journal of Forecasting, 11(3), 361–377. https://doi.org/10.1016/0169-2070(95)00594-2
Lim, C. H., & Papi, L. (1997). An econometric analysis of the determinants of inflation in Turkey (97/170; IMF Working Paper).
Medeiros, M. C., Vasconcelos, G. F., & De Freitas, E. H. (2016). Forecasting Brazilian inflation with high dimensional models. Brazilian Review of Econometrics, 36(2), 223–254. https://doi.org/10.12660/bre.v99n992016.52273
Medeiros, M. C., Vasconcelos, G. F. R., Veiga, Á., & Zilberman, E. (2021). Forecasting inflation in a data-rich environment: The benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98–119. https://doi.org/10.1080/07350015.2019.1637745
Mishkin, F. S. (2000). Para teorisi ve politikası (The Economics of money, banking, and financial markets, financial times prentice hall). Bilim Teknik Yayınevi.
MTFRT. (2021). Republic of Turkey Ministry of treasury and finance: Budget Statistics.
Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: A guide for data scientists. O’Reilly Media, Inc.
Nakamura, E. (2005). Inflation forecasting using a neural network. Economic Letters, 86(3), 373–378. https://doi.org/10.1016/j.econlet.2004.09.003
Önder, A. Ö. (2004). Forecasting Inflation in Emerging Markets by Using the Phillips Curve and Alternative Time Series Models. Emerging Markets Finance and Trade, 40(2), 71–82. https://doi.org/10.1080/1540496x.2004.11052566
Özgür, Ö., & Akkoç, U. (2021). Inflation forecasting in an emerging economy: Selecting variables with machine learning algorithms. In International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-05-2020-0577
Rodríguez-Vargas, A. (2020). Forecasting Costa Rican inflation with machine learning methods. Latin American Journal of Central Banking, 1(1), 100012. https://doi.org/https://doi.org/10.1016/j.latcb.2020.100012
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. In Psychological Review (Vol. 65, Issue 6, pp. 386–408). American Psychological Association. https://doi.org/10.1037/h0042519
Sims, C. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017
Stock, J. H., & Watson, M. W. (2013). Phillips Curve Inflation Forecasts. In Understanding Inflation and the Implications for Monetary Policy (No. 14322; NBER Working Paper). https://doi.org/10.7551/mitpress/9780262013635.003.0003
Stock, J., & Watson, M. (1999). A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. In R. Engle & H. White (Eds.), Cointegration, Causality and Forecasting: A Festschrift for Clive W.J. Granger (pp. 1–44). Oxford University Press.
Tibshirani, R. (2016). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B ( Methodological ), 58(1), 267–288.
Ülke, V., Sahin, A., & Subasi, A. (2018). A comparison of time series and machine learning models for inflation forecasting: Empirical evidence from the USA. Neural Computing and Applications, 30(5), 1519–1527. https://doi.org/10.1007/s00521-016-2766-x
Wojciech, D., & Derek, F. D. (1992). New directions in econometric practice general to spesific modelling, cointegration and vector autoregressions. Edward Elgar Publishing.