We propose a Bayesian stochastic search approach to selecting restrictions for Vector Autoregressive (VAR) models. For this purpose, we develop a Markov Chain Monte Carlo (MCMC) algorithm that visits high posterior probability restrictions on the elements of both the VAR regression coefficients and the error variance matrix. Numerical simulations show that stochastic search based on this algori...

Vector autoregressive model is a very popular tool in multiple time series analysis. Its parameters are usually estimated by the least squares procedure which is very sensitive to the presence of errors in data, e.g. outliers. If outliers were present, the estimation results would become unreliable. Therefore in the presented paper we will propose a new procedure for estimating multivariate reg...

Model Vector Autoregressive (VAR) merupakan salah satu pemodelan dalam statistika yang dapat digunakan untuk data multivariat time series biasa ditemukan bidang keuangan, manajemen, bisnis dan ekonomi. VAR menganalisis secara simultan mendapatkan kesimpulan tepat menjelaskan perilaku hubungan antar variabel endogeneous maupun endegeneous eksogeneous dinamis. Selain itu model juga mengenai selai...

A Kalman-filter based, vector autoregressive (VAR) model is used to forecast mobile user uplink spatial signatures and improve downlink signal-to-interference ratio in timedivision duplex (TDD) systems. Results are presented from computational electromagnetic (CEM) simulations of a smart antenna system operating at 1.8 GHz in an urban microcellular environment.

Vector autoregressive (VAR) modelling is one of the most popular approaches in multivariate time series analysis. The parameters interpretation is simple, and provide an intuitive identification of relationships and Granger causality among time series. However, the VAR modelling requires stationarity conditions which could not be valid in many practical applications. Locally stationary or time ...

Identified vector autoregressive (VAR) models have become widely used on time series data in recent years, but finite sample inference for such models remains a challenge. In this study, we propose a conjugate prior for Bayesian analysis of normalized VAR models. Under the prior, themarginal posterior of VAR parameters involved in identification can be either derived in closed form or simulated...

in this study, the relationship between inflation and economic growth of iran is conciliated with a perspective on uncertainty of inflation. we use a generalized autoregressive conditional heteroskedasticity (garch) model that make possible this advantage that conditional variance of error term changes along the time, also, vector error correction model (vecm) is stable. for surveying the co-in...

T his paper investigates the existence of possible spillover effects among four main asset markets namely foreign exchange, stock, gold, and housing markets in Iran from 2002:03 to 2015:06. For this purpose, we have exploited Sigma-Point Kalman Filter (SPKF) to extract the bubble component of assets prices in the aforementioned Markets. Then, in order to analyze the price bubbles spi...

The LASSO (Tibshirani, J R Stat Soc Ser B 58(1):267–288, 1996, [30]) and the adaptive LASSO (Zou, J Am Stat Assoc 101:1418–1429, 2006, [37]) are popular in regression analysis for their advantage of simultaneous variable selection and parameter estimation, and also have been applied to autoregressive time series models. We propose the doubly adaptive LASSO (daLASSO), or PLAC-weighted adaptive L...

This paper reexamines the effects of inflation uncertainty on real economic activity by utilizing a flexible, dynamic, multivariate framework that accommodates possible interaction between the conditional means and variances. The empirical model is based on the identified vector autoregressive regression of Bernanke and Gertler (1995), modified to accommodate multivariate generalized autoregres...