nep-for New Economics Papers
on Forecasting
Issue of 2017‒09‒24
two papers chosen by
Rob J Hyndman
Monash University

  1. Comparison of Diffusion Models for Forecasting the Growth of Broadband Markets in Thailand By Sudtasan, Tatcha; Mitomo, Hitoshi

  1. By: Sudtasan, Tatcha; Mitomo, Hitoshi
    Abstract: The aim of this paper is to investigate the most accurate S-curve model, the Logistic, Gompertz, and Bass models, in forecasting the diffusion of telecommunication markets Thailand. The analyses apply the data of mobile telecommunication market and fixed-broadband market separately without the interaction between both services. The originality of this study is at the diffusion path segmentation intervened by technological change that accelerates or decelerates each market. Parameters of each model are estimated by nonlinear model estimation methodology. By applying those parameters, the accuracy of each model can be identified compared to the actual data. Following the evaluation of the goodness-of-fit and forecasting ability, the Gompertz model shows the best performance in forecasting the diffusion of mobile telecommunication and fixed broadband markets. With the more suitable forecasting model to the markets, the ultimate total number of users in the future period could be more accurately predicted.
    Keywords: Broadband diffusion,Empirical comparison,Logistic model,Gompertz model,Bass model
    Date: 2017
  2. By: Baris Soybilgen (Istanbul Bilgi University)
    Abstract: We propose a factor augmented neural network model to obtain short-term predictions of U.S. business cycle regimes. First, dynamic factors are extracted from a large-scale data set consisting of 122 variables. Then, these dynamic factors are fed into neural network models for predicting recession and expansion periods. We show that the neural network model provides good in sample and out of sample fits compared to the popular Markov switching dynamic factor model. We also perform a pseudo real time out of sample forecasting exercise and show that neural network models produce accurate short-term predictions of U.S. business cycle phases.
    Keywords: Dynamic Factor Model; Neural Network; Recession
    JEL: E37 E31
    Date: 2017–08

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