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ECONOMETRICS

Forecasting Indian Goods and Services Tax revenue using TBATS, ETS, Neural Networks, and hybrid time series models

ORCID Icon, , ORCID Icon, & ORCID Icon
Article: 2285649 | Received 27 Feb 2023, Accepted 15 Nov 2023, Published online: 03 Dec 2023

References

  • Abhishek, K., Singh, M. P., Ghosh, S., & Anand, A. (2012). Weather forecasting model using artificial neural network. Procedia Technology, 4, 311–23. https://doi.org/10.1016/j.protcy.2012.05.047
  • Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5–6), 594–621. https://doi.org/10.1080/07474938.2010.481556
  • Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37. https://doi.org/10.1016/0304-4076(77)90052-5
  • Aminian, F., Suarez, E. D., Aminian, M., & Walz, D. T. (2006). Forecasting economic data with neural networks. Computational Economics, 28(1), 71–88. https://doi.org/10.1007/s10614-006-9041-7
  • Aranha, M., & Bolar, K. (2023). Efficacies of artificial neural networks ushering improvement in the prediction of extant credit risk models. Cogent Economics & Finance, 11(1), 2210916. https://doi.org/10.1080/23322039.2023.2210916
  • Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: A decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521–530. https://doi.org/10.1016/S0169-2070(00)00066-2
  • Bhattacharyya, A., Chakraborty, T., & Rai, S. N. (2022). Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. Non-Linear Dynamics, 107(3), 1–16. https://doi.org/10.1007/s11071-021-07099-3
  • Bhattacharyya, A., Chattopadhyay, S., Pattnaik, M., & Chakraborty, T. (2021, July). Theta autoregressive neural network: A hybrid time series model for pandemic forecasting. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China (pp. 1–8). IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533747
  • Blöthner, S., & Larch, M. (2022). Economic determinants of regional trade agreements revisited using machine learning. Empirical Economics, 63(4), 1771–1807. https://doi.org/10.1007/s00181-022-02203-x
  • Cadenas, E., & Rivera, W. (2010). Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renewable Energy, 35(12), 2732–2738. https://doi.org/10.1016/j.renene.2010.04.022
  • Callen, J. L., Kwan, C. C., Yip, P. C., & Yuan, Y. (1996). Neural network forecasting of quarterly accounting earnings. International Journal of Forecasting, 12(4), 475–482. https://doi.org/10.1016/S0169-2070(96)00706-6
  • Cao, J., & Wang, J. (2019). Stock price forecasting model based on modified convolution neural network and financial time series analysis. International Journal of Communication Systems, 32(12), e3987. https://doi.org/10.1002/dac.3987
  • Chakraborty, T., Ghosh, I., Mahajan, T., & Arora, T. (2022). Nowcasting of COVID-19 Confirmed Cases: Foundations, Trends, and Challenges. In A. T. Azar & A. E. Hassanien (Eds.), Modeling, Control and Drug Development for COVID-19 Outbreak Prevention. Studies in Systems, Decision and Control (Vol. 366). Springer. https://doi.org/10.1007/978-3-030-72834-2_29
  • Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2, 100015. https://doi.org/10.1016/j.dajour.2021.100015
  • Cnossen, S. (2013). Preparing the way for a modern GST in India. International Tax and Public Finance, 20(4), 715–723. https://doi.org/10.1007/s10797-013-9281-0
  • Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017). A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74, 902–924. https://doi.org/10.1016/j.rser.2017.02.085
  • De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771
  • Dey, S. K. (2021). Impact of goods and services tax on indirect tax revenue of India: With special reference to Odisha state. Universal Journal of Accounting and Finance, 9(3), 431–441. https://doi.org/10.13189/ujaf.2021.090318
  • Diamond, P. A., & Mirrlees, J. A. (1971). Optimal taxation and public production I: Production efficiency. The American Economic Review, 61(1), 8–27.
  • Du Plooy, R., Venter, P. J., & McMillan, D. (2021). Pricing vanilla options using artificial neural networks: Application to the South African market. Cogent Economics & Finance, 9(1), 1914285. https://doi.org/10.1080/23322039.2021.1914285
  • Fantazzini, D. (2020). Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries. Applied Econometrics, 59, 33–54. https://doi.org/10.22394/1993-7601-2020-59-33-54
  • Faruk, D. Ö. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), 586–594. https://doi.org/10.1016/j.engappai.2009.09.015
  • Garg, S., Goyal, A., & Pal, R. (2017). Why tax effort falls short of tax capacity in Indian states: A stochastic frontier approach. Public Finance Review, 45(2), 232–259. https://doi.org/10.1177/1091142115623855
  • Ghasemiyeh, R., Moghdani, R., & Sana, S. S. (2017). A hybrid artificial neural network with metaheuristic algorithms for predicting stock price. Cybernetics and Systems, 48(4), 365–392. https://doi.org/10.1080/01969722.2017.1285162
  • Ghimire, S., Deo, R. C., Downs, N. J., & Raj, N. (2019). Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia. Journal of Cleaner Production, 216, 288–310. https://doi.org/10.1016/j.jclepro.2019.01.158
  • Gholami, V., Torkaman, J., & Dalir, P. (2019). Simulation of precipitation time series using tree-rings, earlywood vessel features, and artificial neural network. Theoretical and Applied Climatology, 137(3–4), 1939–1948. https://doi.org/10.1007/s00704-018-2702-3
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice(2nd ed.). OTexts.
  • Hyndman, R. J., & Billah, B. (2003). Unmasking the theta method. International Journal of Forecasting, 19(2), 287–290. https://doi.org/10.1016/S0169-2070(01)00143-1
  • Jha, R. Mohanty, M. S. Chatterjee, S. & Chitkara, P.(1999). Tax efficiency in selected Indian states. Empirical economics, 24(4), 641–654.
  • Karnik, A., & Raju, S. (2015). State fiscal capacity and tax effort: Evidence for Indian states. South Asian Journal of Macroeconomics and Public Finance, 4(2), 141–177. https://doi.org/10.1177/2277978715602396
  • Kawadia, G., & Suryawanshi, A. K. (2023). Tax effort of the Indian states from 2001–2002 to 2016–2017: A stochastic frontier approach. Millennial Asia, 14(1), 85–101. https://doi.org/10.1177/09763996211027053
  • Khashei, M., & Bijari, M. (2011). A novel hybridisation of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664–2675. https://doi.org/10.1016/j.asoc.2010.10.015
  • Kim, H., Cho, H., & Ryu, D. (2022). Corporate bankruptcy prediction using machine learning methodologies with a focus on sequential data. Computational Economics, 59(3), 1231–1249. https://doi.org/10.1007/s10614-021-10126-5
  • Kumar, M., Barve, A., & Yadav, D. K. (2019). Analysis of barriers in implementation of Goods and service tax (GST) in India using interpretive structural modelling (ISM) approach. Journal of Revenue and Pricing Management, 18(5), 355–366. https://doi.org/10.1057/s41272-019-00202-9
  • Lam, K. C., & Oshodi, O. S. (2016). Forecasting construction output: A comparison of artificial neural network and Box-Jenkins model. Engineering, Construction & Architectural Management, 23(3), 302–322. https://doi.org/10.1108/ECAM-05-2015-0080
  • Li, Y., & Dai, W. (2020). Bitcoin price forecasting method based on CNN‐LSTM hybrid neural network model. Journal of Engineering, 2020(13), 344–347. https://doi.org/10.1049/joe.2019.1203
  • Maniati, M., Sambracos, E., & Sklavos, S. (2022). A neural network approach for integrating banks’ decision in shipping finance. Cogent Economics & Finance, 10(1), 2150134. https://doi.org/10.1080/23322039.2022.2150134
  • Mawejje, J., & Sebudde, R. K. (2019). Tax revenue potential and effort: Worldwide estimates using a new dataset. Economic Analysis and Policy, 63, 119–129. https://doi.org/10.1016/j.eap.2019.05.005
  • Meeusen, W., & van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444. https://doi.org/10.2307/2525757
  • Milačić, L., Jović, S., Vujović, T., & Miljković, J. (2017). Application of artificial neural network with extreme learning machine for economic growth estimation. Physica A: Statistical Mechanics and Its Applications, 465, 285–288. https://doi.org/10.1016/j.physa.2016.08.040
  • Milunovich, G. (2020). Forecasting ’Australia’s real house price index: A comparison of time series and machine learning methods. Journal of Forecasting, 39(7), 1098–1118. https://doi.org/10.1002/for.2678
  • Mostafa, M. M. (2010). Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait. Expert Systems with Applications, 37(9), 6302–6309. https://doi.org/10.1016/j.eswa.2010.02.091
  • Mukherjee, S. (2015). Present state of goods and services tax (GST) reform in India. Cambridge University Press.
  • Mukherjee, S. (2019). Value added tax efficiency across Indian states: Panel stochastic frontier analysis. Economic and Political Weekly, 54(22), 40–50.
  • Mukherjee, S. (2020a). Goods and services tax efficiency across Indian states: Panel stochastic frontier analysis. Indian Economic Review, 55(2), 225–251. https://doi.org/10.1007/s41775-020-00097-z
  • Mukherjee, S. (2020b). Possible impact of withdrawal of GST compensation post GST compensation period on Indian state finances, (20/291). https://www.nipfp.org.in/publications/working-papers/1887/
  • Navas Thorakkattle, M., Farhin, S., & Khan, A. A. (2022). Forecasting the trends of COVID-19 and causal impact of vaccines using Bayesian structural time series and ARIMA. Annals of Data Science, 9(5), 1025–1047. https://doi.org/10.1007/s40745-022-00418-4
  • Oommen, M. A. (1987). Relative tax effort of states. Economic and Political Weekly, 22(11), 466–470. https://www.jstor.org/stable/i402605
  • Paliwal, U. L., Saxena, N. K., & Pandey, A. (2019). Analysing the impact of GST on tax revenue in India: The tax buoyancy approach. International Journal of Economics and Business Administration, 7(4), 514–523.
  • Perone, G. (2021). Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalisations in Italy. The European Journal of Health Economics, 1–24. https://doi.org/10.2139/ssrn.3716343
  • Premalatha, N., & Valan Arasu, A. (2016). Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. Journal of Applied Research and Technology, 14(3), 206–214. https://doi.org/10.1016/j.jart.2016.05.001
  • Purohit, M. C. (2006). Tax efforts and taxable capacity of central and state governments. Economic and Political Weekly, 41(8), 747–755. https://www.jstor.org/stable/4417879.
  • Rao, M. G. (2000). Tax reform in India: Achievements and challenges. Asia Pacific Development Journal, 7(2), 59–74.
  • Rao, R. K., Mukherjee, S., & Bagchi, A. (2019). Goods and services tax in India. Cambridge University Press.
  • Revathi, R., & Aithal, P. S. (2019). Review on global implications of goods and service tax and its Indian scenario. Saudi Journal of Business and Management Studies, 4(4), 337–358.
  • Romer, C. D., & Romer, D. H. (2010). The macroeconomic effects of tax changes: Estimates based on a new measure of fiscal shocks. American Economic Review, 100(3), 763–801. https://doi.org/10.1257/aer.100.3.763
  • Sabri, R., Abdul Rahman, A. A., Meero, A., Abro, L. A., & AsadUllah, M. (2022). Forecasting Turkish lira against the US dollars via forecasting approaches. Cogent Economics & Finance, 10(1), 2049478. https://doi.org/10.1080/23322039.2022.2049478
  • Sharma, C. K. (2021). The Political economy of India’s transition to Goods and services tax. GIGA working papers (325). GIGA German Institute of Global and Area Studies.
  • Silva, E. S., Hassani, H., Heravi, S., & Huang, X. (2019). Forecasting tourism demand with denoised neural networks. Annals of Tourism Research, 74, 134–154. https://doi.org/10.1016/j.annals.2018.11.006
  • Singhal, N., Goyal, S., Sharma, D., Kumar, S., & Nagar, S. (2022). Do goods and services tax influence the economic development? An empirical analysis for India. Vision: The Journal of Business Perspective, 09722629221117196. https://doi.org/10.1177/09722629221117196
  • Spiliotis, E., Assimakopoulos, V., & Makridakis, S. (2020). Generalising the theta method for automatic forecasting. European Journal of Operational Research, 284(2), 550–558. https://doi.org/10.1016/j.ejor.2020.01.007
  • Taskaya-Temizel, T., & Casey, M. C. (2005). A comparative study of autoregressive neural network hybrids. Neural Networks, 18(5–6), 781–789. https://doi.org/10.1016/j.neunet.2005.06.003
  • Thayyib, P. V., Mamilla, R., Khan, M., Fatima, H., Asim, M., Anwar, I., Shamsudheen, M. K., & Khan, M. A. (2023). State-of-the-art of artificial intelligence and Big Data Analytics reviews in five different domains: A bibliometric summary. Sustainability, 15(5), 4026. https://doi.org/10.3390/su15054026
  • Vasanthagopal, R. (2011). GST in India: A big leap in the indirect taxation system. International Journal of Trade, Economics and Finance, 2(2), 144. https://doi.org/10.7763/IJTEF.2011.V2.93
  • Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167, 599–606. https://doi.org/10.1016/j.procs.2020.03.326
  • Wagdi, O., Salman, E., & Albanna, H. (2023). Integration between technical indicators and artificial neural networks for the prediction of the exchange rate: Evidence from emerging economies. Cogent Economics & Finance, 11(2), 2255049. https://doi.org/10.1080/23322039.2023.2255049
  • Wang, F., Zheng, X., & McMillan, D. (2018). The comparison of the hedonic, repeat sales, and hybrid models: Evidence from the Chinese paintings market. Cogent Economics & Finance, 6(1), 1443372. https://doi.org/10.1080/23322039.2018.1443372
  • Xue, X. (2017). Prediction of daily diffuse solar radiation using artificial neural networks. International Journal of Hydrogen Energy, 42(47), 28214–28221. https://doi.org/10.1016/j.ijhydene.2017.09.150
  • Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using artificial neural network techniques: A review. Renewable and Sustainable Energy Reviews, 33, 772–781. https://doi.org/10.1016/j.rser.2013.08.055
  • Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0