Bayesian Methods in Non-linear Time Series
نویسنده
چکیده
This paper reviews the analysis of the threshold autoregressive, smooth threshold autoregressive, and Markov switching autoregressive models from the Bayesian perspective. For each model we start by describing a baseline model and discussing possible extensions and applications. Then we review the choice of prior, inference, tests against the linear hypothesis, and conclude with models selection. A short discussion of recent progress in incorporating regime changes into theoretical macroeconomic models concludes our survey. JEL classification: C11, C22, C52
منابع مشابه
کاربرد آنالیز طیفی بیزی در تحلیل سریهای زمانی نورسنجی
The present paper introduces the Bayesian spectral analysis as a powerful and efficient method for spectral analysis of photometric time series. For this purpose, Bayesian spectral analysis has programmed in Matlab software for XZ Dra photometric time series which is non-uniform with large gaps and the power spectrum of this analysis has compared with the power spectrum which obtained from the ...
متن کاملاستفاده از آنتروپی شانون در پیشپردازش ورودی شبکه بیزین جهت مدلسازی سریهای زمانی
Selecting appropriate inputs for intelligent models is important due to reduce costs and save time and increase accuracy and efficiency of models. The purpose of this study is using Shannon entropy to select the optimum combination of input variables in time series modeling. Monthly time series of precipitation, temperature and radiation in the period of 1982-2010 was used from Tabriz synoptic ...
متن کاملModeling and prediction of time-series of monthly copper prices
One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...
متن کاملBayesian Time Series: Models and Computations for the Analysis of Time Series in the Physical Sciences
This articles discusses developments in Bayesian time series modelling and analysis relevant in studies of time series in the physical and engineering sciences. With illustrations and references, we discuss: Bayesian inference and computation in various state-space models, with examples in analysing quasi-periodic series; isolation and modelling of various components of error in time series; de...
متن کاملDetermination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province
Rainfall is one of the most important components of the water cycle and plays a very important role in the measurement of climate characteristic in any area. Limitations such as lack of sufficient information about the amount of rainfall in time and space scale and complexity of the relationship between meteorological elements related to rainfall, causes the calculation of these parameters usin...
متن کاملDynamic generalized linear models for non-Gaussian time series forecasting
The purpose of this paper is to provide a discussion, with illustrating examples, on Bayesian forecasting for dynamic generalized linear models (DGLMs). Adopting approximate Bayesian analysis, based on conjugate forms and on Bayes linear estimation, we describe the theoretical framework and then we provide detailed examples of response distributions, including binomial, Poisson, negative binomi...
متن کامل