مقایسه الگوهای پیش‌بینی تورم در ایران: شواهد جدید از الگوی ترکیبی ARDL-D-LSTM

Aghayan, B. S., Bahrami, J., & Jahangard, E. (2018). Forecasting Iran's economy inflation with DSGE-VAR model (theory and technique). Quarterly Journal of Applied Theories of Economics, 5(2), 149-176.[In Persian]

Babu, C. N., & Reddy, B. E. (2015). Performance comparison of four new ARIMA-ANN prediction models on Internet traffic data. Journal of Telecommunications and Information Technology, 1(2), 67-75.

Bao, T. Q., & My, B. T. T. (2019). Forecasting stock index based on hybrid artificial neural network models. Science & Technology Development Journal-Economics-Law and Management, 3(1), 52–57.

Baybuza, I. (2018). Inflation forecasting using machine learning methods. Russian Journal of Money and Finance, 77(4), 42–59.

Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.

Binner, J. M., Tino, P., Tepper, J., Anderson, R., Jones, B., & Kendall, G. (2010). Does money matter in inflation forecasting? Physica A: Statistical Mechanics and Its Applications, 389(21), 4793–4808.

Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B (Methodological), 37(2), 149–163.

Clemen, R.T. (1989). Combining forecasts: A review and annotated bibliography. International journal of forecasting , 5(4), 559-583.

Estiko, F. I., & Wahyuddin, S. (2019). Analysis of Indonesia’s Inflation Using ARIMA and Artificial Neural Network. Economics Development Analysis Journal, 8(2), 151–162.

Graves, A., & Schmidhuber, J. (2005). Frame wise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks, 18(5-6), 602-610.

Granger, C. W. J., & Terasvirta, T. (1993). Modelling non-linear economic relationships. OUP Catalogue.

Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232.

Hanif, M. N., & Malik, M. J. (2015). Evaluating performance of inflation forecasting models of Pakistan, SBP Research Bulletin, 11(1).

Hornik, K. (1993). Some new results on neural network approximation. Neural Networks, 6(8), 1069–1072.

Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen Netzen [in German] Diploma thesis. TU Münich.

Hochreiter, S., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies.

Işığıçok, E., Öz, R., & Tarkun, S. (2020). Forecasting and Technical Comparison of Inflation in Turkey With Box-Jenkins (ARIMA) Models and the Artificial Neural Network. International Journal of Energy Optimization and Engineering (IJEOE), 9(4), 84–103.

Jorgenson, D. W. (1966). Rational distributed lag functions. Econometrica: Journal of the Econometric Society, 135-149.

Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664–2675.

Kim, S. E., & Seo, I. W. (2015). Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers. Journal of Hydro-Environment Research, 9(3), 325–339.

Klein, L. R., & Goldberger, A. S. (1955). Econometric model of the United States, Amsterdam: North-Holland Pub- lishing Co, 66(262) 1929-1952.

Krämer, W., Ploberger, W., & Alt, R. (1988). Testing for structural change in dynamic models. Econometrica: Journal of the Econometric Society, 56(6), 1355–1369.

Krämer, W., & Sonnberger, H. (1986). Diagnostic checking in practice. In The Linear Regression Model Under Test (pp. 123-155). Physica-Verlag HD.

Kumar, M., & Thenmozhi, M. (2012). Stock index return forecasting and trading strategy using hybrid ARIMA-neural network model. International Journal of Financial Management, 2(1), 1-15.

LeBaron, B. (1994). Chaos and nonlinear forecastability in economics and finance. Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences, 348(1688), 397–404.

Leontaritis, I. J., & Billings, S. A. (1985). Input-output parametric models for non-linear systems part I: deterministic non-linear systems. International Journal of Control, 41(2), 303–328.

Lin, T., Horne, B. G., & Giles, C. L. (1998). How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies. Neural Networks, 11(5), 861–868.

Lucas Jr, R. E. (1976). Econometric policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy, 1, 19–46.

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.

McKnight, S., Mihailov, A., & Rumler, F. (2020). Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend. Economic Modelling, 87, 383–393.

Mohammadi, S., Raeie, R., & Rahimi, M. (2018). Interval forecasting for gold price with hybrib model of ARIMA and ANN.[In Persian]

Peirano, R., Kristjanpoller, W., & Minutolo, M. C. (2021). Forecasting inflation in Latin American countries using a SARIMA–LSTM combination. Soft Computing, 25(16), 10851-10862.

Pesaran, H. M., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of long-run relationships (Working paper no. 9907). Cambridge, United Kingdom: Department of Applied Economics, University of Cambridge.

Pesaran, M. H., & Shin, Y. (1995). An autoregressive distributed lag modelling approach to cointegration analysis, Cambridge University Press, Cambridge, 371-413.

Pourkazemi, M. H., Biranvand, A., & Delfan, M. (2016). Designing a warning system for hyperinflation for Iran’s economy.[In Persian]

Raoofi, A., & Mohammadi, T. (2018). Forecasting Tehran stock exchange index returns using a combination of wavelet decomposition and adaptive neural fuzzy inference systems. Iranian Journal of Economic Research, 23(76), 107-136.[In Persian]

Romer, D. (2018). Advanced macroeconomics. Mcgraw-hill.

Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386.

Šestanović, T., & Arnerić, J. (2021). Neural network structure identification in inflation forecasting. Journal of Forecasting, 40(1), 62–79.

Timmermann, A. (2006). Forecast combinations. Handbook of economic forecasting, 1, 135-196.

Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.

Xu, Q., Zhuo, X., Jiang, C., & Liu, Y. (2019). An artificial neural network for mixed frequency data. Expert Systems with Applications, 118, 127–139.

Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.

Zolfaghari, M., & Sahabi, B. (2020). Designing a model for forecasting the stock exchange total index returns (emphasizing on combined deep learning network models and GARCH family models). [In Persian]