Bayesian Analysis of the Autoregressive-Moving Average Model with Exogenous Inputs Using Gibbs Sampling
نویسنده
چکیده
Abstract: The problem of estimating a set of parameters in the autoregressive moving average model with exogenous inputs (ARMAX) is considered and a numerical Bayesian method proposed. This paper, develops a Bayesian analysis for the ARMAX model by implementing a fast, easy and accurate Gibbs sampling algorithm. The procedure is easy to implement and can be computed also when some priors in the ARMAX are diffuse. The empirical results of the simulated examples and electricity consumption data in the industrial sector in Egypt showed the accuracy of the proposed methodology and has good statistical properties.
منابع مشابه
Structure of Wavelet Covariance Matrices and Bayesian Wavelet Estimation of Autoregressive Moving Average Model with Long Memory Parameter’s
In the process of exploring and recognizing of statistical communities, the analysis of data obtained from these communities is considered essential. One of appropriate methods for data analysis is the structural study of the function fitting by these data. Wavelet transformation is one of the most powerful tool in analysis of these functions and structure of wavelet coefficients are very impor...
متن کاملConstrained Forecasts in Arma Models: a Bayesian Approach
A Bayesian approach is developed to generate constrained and unconstrained forecasts in autoregressive-moving average time series models. Both are calculated by formulating the ARMA(p,q) model in such a way that it is possible to numerically compute the predictive distribution for any number of forecasts as in de Alba (1993). We obtain the posterior distribution of the parameters via Gibbs samp...
متن کاملBayesian Analysis of Spatial Probit Models in Wheat Waste Management Adoption
The purpose of this study was to identify factors influencing the adoption of wheat waste management by wheat farmers. The method used in this study using the spatial Probit models and Bayesian model was used to estimate the model. MATLAB software was used in this study. The data of 220 wheat farmers in Khouzestan Province based on random sampling were collected in winter 2016. To calculate Bay...
متن کاملModified Maximum Likelihood Estimation in First-Order Autoregressive Moving Average Models with some Non-Normal Residuals
When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...
متن کاملComparison of Maximum Likelihood Estimation and Bayesian with Generalized Gibbs Sampling for Ordinal Regression Analysis of Ovarian Hyperstimulation Syndrome
Background and Objectives: Analysis of ordinal data outcomes could lead to bias estimates and large variance in sparse one. The objective of this study is to compare parameter estimates of an ordinal regression model under maximum likelihood and Bayesian framework with generalized Gibbs sampling. The models were used to analyze ovarian hyperstimulation syndrome data. Methods: This study use...
متن کامل