This paper is concerned with model determination methods and their use in the prediction of economic time series. The methods are Bayesian but they can be justified by classical arguments as well. The paper continues some recent work on Bayesian asymptotic, develops embedding techniques for vector martingales, and implements the modeling ideas in a multivariate regression framework that includes Bayesian vector autoregression (BVAR's) and reduced rank regressions (RRR's). It is shown how the theory in the paper can be used; (i) to construct optimized BVAR's; (ii) to compare models such as BVAR's, optimized BVAR's and RRR's; (iii) to perform joint order selection of cointegrating rank, lag length and trend degree in a VAR; and (iv) to discar...
This paper surveys recently developed methods for Bayesian inference and their use in economic time ...
A recently proposed Bayesian model selection technique, stochastic model specification search, is ca...
Vector autoregressive (VAR) models for stationary and integrated variables are reviewed. Model spec...
The subject of this paper is modelling, estimation, inference and prediction for economic time serie...
This dissertation describes a technique of economic forecasting with Bayesian vector autoregression ...
This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregres...
Vector autoregressions (VARs) are linear multivariate time-series models able to capture the joint d...
The application of Vector Autoregressive (VAR) models to macroeconomic forecasting problems was sugg...
We study the joint determination of the lag length, the dimension of the cointegrating space and the...
1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant resea...
Economic policy decisions are often informed by empirical analysis based on accurate econometric mod...
Economic forecasts and policy decisions are often informed by empiri- cal analysis based on economet...
Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoret...
In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in gener...
This text presents modern developments in time series analysis and focuses on their application to e...
This paper surveys recently developed methods for Bayesian inference and their use in economic time ...
A recently proposed Bayesian model selection technique, stochastic model specification search, is ca...
Vector autoregressive (VAR) models for stationary and integrated variables are reviewed. Model spec...
The subject of this paper is modelling, estimation, inference and prediction for economic time serie...
This dissertation describes a technique of economic forecasting with Bayesian vector autoregression ...
This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregres...
Vector autoregressions (VARs) are linear multivariate time-series models able to capture the joint d...
The application of Vector Autoregressive (VAR) models to macroeconomic forecasting problems was sugg...
We study the joint determination of the lag length, the dimension of the cointegrating space and the...
1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant resea...
Economic policy decisions are often informed by empirical analysis based on accurate econometric mod...
Economic forecasts and policy decisions are often informed by empiri- cal analysis based on economet...
Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoret...
In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in gener...
This text presents modern developments in time series analysis and focuses on their application to e...
This paper surveys recently developed methods for Bayesian inference and their use in economic time ...
A recently proposed Bayesian model selection technique, stochastic model specification search, is ca...
Vector autoregressive (VAR) models for stationary and integrated variables are reviewed. Model spec...