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  • 标题:Bayesian Inference for a Deterministic Cycle with Time-Varying Amplitude: The Case of the Growth Cycle in European Countries
  • 本地全文:下载
  • 作者:Łukasz Lenart
  • 期刊名称:Central European Journal of Economic Modelling and Econometrics
  • 印刷版ISSN:2080-0886
  • 电子版ISSN:2080-119X
  • 出版年度:2018
  • 期号:3
  • 页码:233-262
  • DOI:10.24425/cejeme.2018.125281
  • 语种:English
  • 出版社:Polska Akademia Nauk
  • 摘要:The main goal of this paper is to propose the probabilistic description of cyclical (business) fluctuations. We generalize a fixed deterministic cycle model by incorporating the time-varying amplitude. More specifically, we assume that the mean function of cyclical fluctuations depends on unknown frequencies (related to the lengths of the cyclical fluctuations) in a similar way to the almost periodic mean function in a fixed deterministic cycle, while the assumption concerning constant amplitude is relaxed. We assume that the amplitude associated with a given frequency is time-varying and is a spline function. Finally, using a Bayesian approach and under standard prior assumptions, we obtain the explicit marginal posterior distribution for the vector of frequency parameters. In our empirical analysis, we consider the monthly industrial production in most European countries. Based on the highest marginal data density value, we choose the best model to describe the considered growth cycle. In most cases, data support the model with a time-varying amplitude. In addition, the expectation of the posterior distribution of the deterministic cycle for the considered growth cycles has similar dynamics to cycles extracted by standard bandpass filtration methods.
  • 关键词:deterministic cycle with time-varying amplitude;Bayesian inference;almost periodic function;growth cycle;industrial production
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