scholarly journals Predictive Likelihood Comparisons with DSGE and DSGE-VAR Models

2013 ◽  
Author(s):  
Anders Warne ◽  
Günter Coenen ◽  
Kai Philipp Christoffel
Keyword(s):  
Var Models ◽  
Predictive Likelihood

Analysis of the Bank of Russia information policy

2017 ◽  
pp. 88-110 ◽  
Author(s):  
S. Drobyshevsky ◽  
P. Trunin ◽  
A. Bozhechkova ◽  
E. Gorunov ◽  
D. Petrova
Keyword(s):  
Interest Rate ◽  
Nonparametric Tests ◽  
Information Policy ◽  
Var Models ◽  
New Approach ◽  
Interest Rate Policy ◽  
News Reports ◽  
Internet Users ◽  
Rate Policy ◽  
Exchange Markets

The article investigates the Bank of Russia information policy using a new approach to measuring information effects on Russian data, including the analysis of the tonality of news reports, as well as internet users’ queries on Google. The efficiency of regulator’s information signals is studied using EGARCH-, VAR- models, as well as nonparametric tests. The authors conclude that the regulator communicates effectively in terms of the predictability of interest rate policy, the degree to which information signals affect the money and foreign exchange markets.

Quality control of risk measures: backtesting VAR models

2007 ◽  
Vol 9 (2) ◽  
pp. 39-54 ◽  
Author(s):  
Victor de la Pena ◽  
Ricardo Rivera ◽  
Jesus Ruiz-Mata
Keyword(s):  
Quality Control ◽  
Risk Measures ◽  
Var Models

An Evaluation Framework for Alternative VaR Models

2003 ◽  
Author(s):  
Christian C.P. Wolff ◽  
Dennis Bams ◽  
Thorsten Lehnert
Keyword(s):  
Evaluation Framework ◽  
Var Models

scholarly journals Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Psychometrika ◽  
2021 ◽  
Author(s):  
Oisín Ryan ◽  
Ellen L. Hamaker
Keyword(s):  
Network Analysis ◽  
Discrete Time ◽  
Continuous Time ◽  
Centrality Measures ◽  
Time Interval ◽  
Direct Effects ◽  
Network Approach ◽  
Var Models ◽  
Auto Regressive ◽  
Conceptual Shift

AbstractNetwork analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

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