Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these models to high-dimensional datasets has proved be challenging due intensive computations over-parameterization concerns. We develop an efficient Bayesian sparsification method class of we call hybrid TVP-VARs—VARs time...

Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as timevarying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus, over-parameterization problems may arise. Bayesian methods have become increasingly popular as a way o...

Background. Video-assisted real-time simulation (VARS) offers the possibility of developing competence in acute medicine in a realistic and safe environment. We investigated the effectiveness of the VARS model and compared it with educational methods like Problem-Based Learning (PBL) and Pediatric Advanced Life Support (PALS). Methods. 45 fourth-year medical students were randomized for three e...

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with sm...

s[v] = preo[v] s[w] = prvo[w] ∀w ∈ vars(prvo) s[Ov] = o s[Ow] = frozen ∀w ∈ vars(prvo) s[Cw] = v ∀w ∈ vars(prvo) We show that all operators o′ that interfere with Fire(o) are not applicable in s. Thus Fire(o) is the only applicable operator in Ts. Second, we show that for all these operators o′ ∈ Ts (except for Fire(o)), Ts already contains a necessary enabling set for o′ in s. Let u 6= Fire(o)...

9:55 – 10:40 Rodney Strachan (University of Queensland) Reducing Dimensions in Large Time-varying Parameter VAR Models This paper proposes a new approach to estimating high dimensional time varying parameter vector autoregressive models (TVP-VARs). Such models are rarely used with more than 4-5 variables. However recent work has shown the advantages of modelling VARs with large numbers of varia...

Structural VARs have been extensively used in empirical macroeconomics during the last two decades, particularly in analyses of monetary policy. Existing Bayesian procedures for structural VARs are at best confined to a severly limited handling of cointegration restrictions. This paper extends the Bayesian analysis of structural VARs to cover cointegrated processes with an arbitrary number of c...

This paper develops methods for Bayesian VAR forecasting when the researcher is uncertain about which variables enter the VAR and the dimension of the VAR may be changing over time. It considers the case where there are N variables which might potentially enter a VAR and the researcher is interested in forecasting N∗ of them. Thus, the researcher is faced with 2N−N ∗ potential VARs. If N is lar...