Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach
Tóm tắt
Từ khóa
Tài liệu tham khảo
Binner, J.M., R.K. Bissoondeeal, T. Elger, A.M. Gazely, and A.W. Mullineux. 2005. A comparison of linear forecasting models and neural networks: An application to Euro inflation and Euro Divisia. Applied Economics 37 (6): 665–680.
Blanchflower, D.G., and C. MacCoille. 2009. The formation of inflation expectations: an empirical analysis for the UK (No. w15388). National Bureau of Economic Research.
Box, G.E., G.M. Jenkins, G.C. Reinsel, and G.M. Ljung. 2015. Time series analysis: Forecasting and control. Hoboken: Wiley.
Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone. 1984. Classification and regression trees. Statistics/probability series.
Brouwer, G.D., and N.R. Ericsson. 1998. Modeling inflation in Australia. Journal of Business & Economic Statistics 16 (4): 433–449.
CMIE. 2022. Dataset of all variables. Retrieved March 22, 2023 from CMIE Economic Outlook.
Das, A., K. Lahiri, and Y. Zhao. 2019. Inflation expectations in India: Learning from household tendency surveys. International Journal of Forecasting 35 (3): 980–993.
Garson, G.D. 1991. Interpreting neural-network connection weights. AI Expert 6 (4): 46–51.
Goh, A.T. 1995. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering 9 (3): 143–151.
Gordon, R.J. 1975. The demand for and supply of inflation. The Journal of Law and Economics 18 (3): 807–836.
Goulet Coulombe, P., M. Leroux, D. Stevanovic, and S. Surprenant. 2022. How is machine learning useful for macroeconomic forecasting? Journal of Applied Econometrics 37 (5): 920–964.
Goyal, A., and P. Parab. 2019. Modeling heterogeneity and rationality of inflation expectations across Indian households. Indira Gandhi Institute of Development Research, Working Paper.
Hall, R.E., and T.J. Sargent. 2018. Short-run and long-run effects of Milton Friedman’s presidential address. Journal of Economic Perspectives 32 (1): 121–134.
Hoerl, A.E., and R.W. Kennard. 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12 (1): 55–67.
Inoue, A., and L. Kilian. 2008. How useful is bagging in forecasting economic time series? A case study of US consumer price inflation. Journal of the American Statistical Association 103 (482): 511–522.
James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. An introduction to statistical learning, vol. 112, 18. New York: Springer.
Liu, L.M. 1986. Identification of time series models in the presence of calendar variation. International Journal of Forecasting 2 (3): 357–372.
Masini, R.P., M.C. Medeiros, and E.F. Mendes. 2023. Machine learning advances for time series forecasting. Journal of Economic Surveys 37 (1): 76–111.
Medeiros, M.C., G.F. Vasconcelos, Á. Veiga, and E. Zilberman. 2021. Forecasting inflation in a data-rich environment: The benefits of machine learning methods. Journal of Business & Economic Statistics 39 (1): 98–119.
Mullainathan, S., and J. Spiess. 2017. Machine learning: An applied econometric approach. Journal of Economic Perspectives 31 (2): 87–106.
Patra, M.D., and P. Ray. 2010. Inflation expectations and monetary policy in India: An empirical exploration. IMF Working Paper. https://doi.org/10.5089/9781451982640.001.
Pradhan, R.P. 2011. Forecasting inflation in India: An application of ANN model. International Journal of Asian Business and Information Management (IJABIM) 2 (2): 64–73.
Pratap, B., and S. Sengupta (2019). Macroeconomic forecasting in India: Does machine learning hold the key to better forecasts?
Raj, J., S. Dhal, and R. Jain. 2008. Imported inflation: The evidence from India. New Delhi: Reserve Bank of India.
Rani, S.J., V.V. Haragopal, and M.K. Reddy. 2017. Forecasting inflation rate of India using neural networks. International Journal of Computer Applications 158 (5): 45–48.
Sharma, N.K., and M. Bicchal. 2018. The properties of inflation expectations: Evidence for India. Economia 19 (1): 74–89.
Siami-Namini, S., N. Tavakoli, and A.S. Namin. 2019. A comparative analysis of forecasting financial time series using ARIMA, LSTM, and BILSTM. arXiv preprint arXiv:1911.09512.
Sims, C.A. 1990. Macroeconomics and reality. Econometrica: Journal of the Econometric Society 1: 1–52. https://doi.org/10.2307/1912017.
Stock, J.H., and M.W. Watson. 1999. Forecasting inflation. Journal of Monetary Economics 44 (2): 293–335.
Stock, J.H., and M.W. Watson. 2002. Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics 20 (2): 147–162.
Stock, J.H., and M.W. Watson. 2007. Why has US inflation become harder to forecast? Journal of Money, Credit and Banking 39: 3–33.
Thakur, G.S.M., R. Bhattacharyya, and S.S. Mondal. 2016. Artificial neural network based model for forecasting of inflation in India. Fuzzy Information and Engineering 8 (1): 87–100.
Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (methodological) 58 (1): 267–288.
Zhang, G., B.E. Patuwo, and M.Y. Hu. 1998. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14 (1): 35–62.
Zou, H. 2006. The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101 (476): 1418–1429.
