Neural network models for inflation forecasting: an appraisal

Choudhary, M. Ali; Haider, Adnan

doi:10.1080/00036846.2011.566190

Cited by 50 publications

(36 citation statements)

References 14 publications

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“…ANNs have been applied in the many fields but only recently to tourism demand forecasting (Kon & Turner, 2005;Palmer et al, 2006;Cho, 2009;Chen, 2011;Teixeira & Fernandes, 2012). Despite that there is no consensus on the most appropriate approach to forecast tourism demand, it is generally believed that the non-linear methods outperform the linear methods in modelling economic behaviour (Choudhary & Haider, 2012;Cang, 2013). These nonlinear models are still limited in that an explicit relationship for the data series has to be hypothesized with little knowledge of the underlying data-generating process (Zhang et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information

Clavería

Monte

Porras

2014

Journal of Tourism Research

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This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi-layer perceptron, a radial basis function and an Elman network. We find that multi-layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results. We find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long-term forecasting.

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Section: Introductionmentioning

confidence: 99%

Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information

Clavería

Monte

Porras

2014

Journal of Tourism Research

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“…The regime-switching models include threshold regression model, smooth transition regression model, and Markov-switching regression model (Hamilton 1996;Rousseau and Wachtel 2002;Ang et al 2007). Artificial intelligence models are defined as the models that make computers do things requiring intelligence, such as artificial neural network (Nakamura 2005;Choudhary and Haider 2012). The integrated models combine different forecast techniques such as econometric models, artificial intelligence methods, and experts' experience together.…”

Section: Inflation Forecast Modelsmentioning

confidence: 99%

A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data

Shang

Wang

et al. 2015

Electronic Commerce Research and Applications

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“…Превосходство нейронных сетей над моделями ARIMA и VAR было продемонстрировано на примере инфляции в Еврозоне [37]. Исследования в данной сфере охватили как инфляцию в глобальной экономике (например, прогноз инфляции в странах ОЭСР [38]), так и флуктуации уровня цен в отдельных странах (Индия [39], Пакистан [40], Турция [41] и др. ).…”

Section: нейронные сетиunclassified

Inflation Forecasting: The Practice of Using Synthetic Procedures

Балацкий¹,

Юрьевич²

2019

Mir novoj jekonomiki

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The article contains a review of inflation forecasting models, including the most popular class of models as one-factor models: random walk, direct autoregression, recursive autoregression, stochastic volatility with an unobserved component and of the integrated model of autoregression with moving average. Also, we discussed the possibilities of various modifications of models based on the Phillips curve (including the “triangle model”), vector autoregressive models (including the factor-extended model of B. Bernanke’s vector autoregression), dynamic general equilibrium models and neural networks. Further, we considered the comparative advantages of these classes of models. In particular, we revealed a new trend in inflation forecasting, which consists of the introduction of synthetic procedures for private forecasts accounting obtained by different models. An important conclusion of the study is the superiority of expert assessments in comparison with all available models. We have shown that in the conditions of a large number of alternative methods of inflation modelling, the choice of the adequate approach in specific conditions (for example, for the Russian economy of the current period) is a non-trivial procedure. Based on this conclusion, the authors substantiate the thesis that large prognostic possibilities are inherent in the mixed strategies of using different methodological approaches, when implementing different modelling tools at different stages of modelling, in particular, the multifactorial econometric model and the artificial neural network.

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Neural network models for inflation forecasting: an appraisal

Cited by 50 publications

References 14 publications

Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information

Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information

A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data

Inflation Forecasting: The Practice of Using Synthetic Procedures

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