1. Abdi, M,. Hamidi, A. S, Pourhasan, A. M. (2010) Evaluation of forecasting methods and presentation of the optimal hybrid model regarding the forecasting of tax revenues. J Tax Res, 19 (11), 85-120. (Persian)
2. Abrahart, R. J., & See, L. (2000). Comparing Neural Network and Autoregressive Moving Average Techniques for the Provision of Continuous River Flow Forecasts In Two Contrasting Catchments. Hydrological Processes, 14(11‐12), 2157-2172.
https://doi.org/10.1002/1099-1085(20000815/30)14:11/12<2157::AID-HYP57>3.0.CO;2-S [
DOI:10.1002/1099-1085(20000815/30)14:11/123.0.CO;2-S]
3. Arabmazar A, Zayer A. (2008) Estimation of Potential Economic Capacity of Taxation in Iran. J Tax Res, 16 (2), 5-26 (Persian)
4. Bratten, B., Gleason, C. A., Larocque, S. A., & Mills, L. F. (2017). Forecasting taxes, New evidence from analysts. The Accounting Review, 92(3), 1-29. [
DOI:10.2308/accr-51557]
5. Buettner, T., & Kauder, B. (2010). Revenue forecasting practices, Differences across countries and consequences for forecasting performance. Fiscal Studies, 31(3), 313-340. [
DOI:10.1111/j.1475-5890.2010.00117.x]
6. Chakraborty Lekha S, Sinha Darshy (2008). Budgetary Forecasting in India, Partitioning Errors and Testing for Rational Expectations. Mpra Paper, University Library of Munich, Germany, working paper.
7. Chan, CW, Troutman, CS, O'Bryan, D (2000). An Expanded Model of Taxpayer Compliance, Empirical Evidence from the United States and Hong Kong, J. Int. Account., Auditing Taxation, 9, 83-103. [
DOI:10.1016/S1061-9518(00)00027-6]
8. Chepkoech, N., Gichana, J. O., & Agong, D. (2022). Effect of e-payment systems on sustainable revenue collection in Nairobi City County Government. International Academic Journal of Economics and Finance, 3 (7), 238, 253.
9. Deschamps Elaine (2004). The impact of institutional change on forecast accuracy, A case study of budget forecasting in Washington State. International Journal of Forecasting, Vol 20. pp 647- 657. [
DOI:10.1016/j.ijforecast.2003.11.009]
10. Ezzati shourgholi, A., Sahraiee, P., Alinezhad, M. (2015). Extraction of the Seasonal Inflationary Regimes in Iranian Economy and the Effect of Government Expenditures on Inflation over the Inflationary Regimes. Economic Strategy, 4(15), 37-69.
11. Flostrand, A., Pitt, L., & Bridson, S. (2020). The Delphi technique in forecasting-A 42-year bibliographic analysis (1975-2017). Technological Forecasting and Social Change, 150, 119773. [
DOI:10.1016/j.techfore.2019.119773]
12. Gharaee Nejad, G., Chapardar, E (2012). Investigating factors affecting tax revenues in Iran, Financial Economics, 6(20), 70-92 (Persian).
13. Gorgini, M., Golestani, S., & Hajabbasi, F. (2012). A Comparison of the Predictive Ability of VAR, ARIMA and Artificial Neural Network (ANN) Models: OPEC's Oil Demand. Iranian Energy Economics, 1(4), 145-168.
14. Green, K. C., Graefe, A., & Armstrong, J. S. (2011). Forecasting principles. M. Lovric, International Encyclopedia on Statistical Science (2010). [
DOI:10.1007/978-3-642-04898-2_257]
15. Israilevich, P. R., Hewings, G. J., Sonis, M., & Schindler, G. R. (1997). Forecasting structural change with a regional econometric input‐output model. Journal of Regional science, 37(4), 565-590. [
DOI:10.1111/0022-4146.00070]
16. Januschowski, T., Gasthaus, J., Wang, Y., Salinas, D., Flunkert, V., Bohlke-Schneider, M., & Callot, L. (2020). Criteria for classifying forecasting methods. International Journal of Forecasting, 36(1), 167-177 [
DOI:10.1016/j.ijforecast.2019.05.008]
17. Kamijani, A, Fahim Yahyai, F. (1990) An analysis on the composition of taxes and estimation of Iran's tax capacity. Journal of Economics and Management of Islamic Azad University, 3(1), 67-86. (Persian)
18. Kessy, S. S. (2020). Electronic payment and revenue collection in local government authorities in Tanzania: evidence from Kinondoni municipality. Tanzanian Economic Review, 9(2). [
DOI:10.56279/ter.v9i2.47]
19. Khodavirdi, A (2010) Analysis of the effect of macroeconomic variables on tax revenues using the co-Integration technique, 1(1), 149-180. (Persian)
20. Lahiri, K., & Yang, C. (2022). Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19, The case of New York. International Journal of Forecasting, 38(2), 545-566. [
DOI:10.1016/j.ijforecast.2021.10.005]
21. Lezgi, F., Amini A., Shomali, L., Najafi A. (2008) Forecasting tax revenues of Qazvin province using time series model and intervention methods during 1383-1374. Tax research paper, 16 (3), 104-67. (Persian)
22. Malikov, T. (2021). Methodological approaches to assessing and forecasting the tax potential of the region. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 7056-7060.
23. Mansouri, M., Khezri, M., Zandi, F., & Safavi, B. (2021). Economic factors affecting the components of Iran tax revenue in the context of economic sanctions. Macroeconomics Research Letter, 15(30), 193-209. (Persian)
24. Mertens, K., & Ravn, M. O. (2013). The dynamic effects of personal and corporate income tax changes in the United States. American economic review, 103(4), 1212-47. [
DOI:10.1257/aer.103.4.1212]
25. Moghadisi, R and Rahimi Badr, B (2008), Evaluating the power of different econometric models for forecasting the price of wheat, Journal of Economic Research, 9(4), 239-263. (Persian)
26. Moore Jr, J. R. (1971). Forecasting and scheduling for past-model replacement parts. Management Science, 18(4-part-i), B-200. [
DOI:10.1287/mnsc.18.4.B200]
27. Moosavi Jahromi, Yeganeh, & Zayer, Ayat. (2009). comparison of two multiple attribute decision making models case: ranking the iran's provinces based on the factors affecting tax capacity. economic research review,No 4, 15-44. (Persian)
28. Moshiri, S (2001), forecasting Iran's inflation using structural models, time series and artificial neural network, of Economic Research, No. 58, 147-184. (Persian)
29. Najafi S N, Salehi A K, Amiri H. (2022) Providing a Model for Detecting Tax Fraud Based on the Personality Types of Corporate Financial Managers using the Neural Network Approach. J Tax Res, 30 (53), 71-96. (Persian) [
DOI:10.52547/taxjournal.30.53.3]
30. Olurankinse, F., & Oladeji, O. E. (2018). Self-assessment, electronic-taxation payment system and revenue generation in Nigeria. Accounting and taxation review, 2(1), 39-55.
31. Perry, M. B. (2010). The weighted moving average technique. Wiley Encyclopedia of Operations Research and Management Science. [
DOI:10.1002/9780470400531.eorms0964]
32. Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool, issues and analysis. International journal of forecasting, 15(4), 353-375. [
DOI:10.1016/S0169-2070(99)00018-7]
33. Ruhi. M, Abbasian. E, Momeni. M, Amouzad. M, Hanan. (2017) Prediction of corporate income tax collection in Iran, using Markov chain and discrete spectrum analysis. J Tax Res, 26 (38), 107-129(Persian)
34. Sadeghi S K (2012) The Effects of Control of Corruption and Government Effectiveness Indices on Tax Revenue, The Case of Upper Middle Income Countries. J Tax Res, 20 (14) ,229-248(Persian)
35. Salami, H., & Mafi, H. (2018). Predicting Export prices of the Iranian Pistachio Based on Commercial Cycles: Application of Structural Time Series Model. Iranian Journal of Agricultural Economics and Development Research, 49(4), 559-571.
36. Samadi A, Eidizadeh S. (2014), An Evaluation of the Impact of Economic and Tax Policies on Iran's Tax System Performance in 2025 (System Dynamics Approach). J Tax Res, 22 (21) ,181-210(Persian)
37. Sepehrdoust, H., Barooti, M. (2017). Tanzi Inflation Effect on Iran Tax System Performance, Iranian Journal of Economic Research, 22(72), 1-40. (Persian)
38. Shiranifakhr, Z., Khoshakhlagh, R., & Sharifi, A. M. (2014). Estimating Demand Function for Natural Gas in the Industrial Sector of Iran using Structural Time Series Model (STSM). Journal of Applied Economics Studies in Iran, 3(11), 129-157.
39. Shobana, G., & Umamaheswari, K. (2021, January). Forecasting by machine learning techniques and econometrics, a review. In 2021 6th [
DOI:10.1109/ICICT50816.2021.9358514]
40. Tamizi, A. R. (2018). Investigating determinants of tax revenues in Iran, A Bayesian Econometric Approach, Quarterly Journal of Quantitative Economics, 15(1), 225-244.
41. Tang, Z., De Almeida, C., & Fishwick, P. A. (1991). Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation, 57(5), 303-310. [
DOI:10.1177/003754979105700508]
42. Teera, J. (2002). Tax Performance, A Comparative Study, Bath, UK, University of Bath, Economics working paper.
43. Zaranejad, M. and Shahram, H. (2008), forecasting the inflation rate in Iran's economy using dynamic artificial neural networks (time series perspective). Quantitative Economics. No. 6, 145-167. (Persian)
44. Zareie, P., JALAEE, S. A., & SADEGHI, Z. (2019) Simulation and prediction of the green tax effect on energy consumption and intensity in Iran using a genetic algorithm. J Tax Res 2019; 27 (42) ,103-125(Persian) [
DOI:10.29252/taxjournal.27.42.103]