scholarly journals Time-Varying Parameter Vector Autoregressions: Specification, Estimation, and an Application

2016 ◽  
Vol 101 (04) ◽  
pp. 323-352 ◽  
Author(s):  
Thomas Lubik ◽  
◽  
Christian Matthes ◽  
Keyword(s):  
Time Varying ◽  
Parameter Vector ◽  
Vector Autoregressions ◽  
Time Varying Parameter

scholarly journals Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions

2020 ◽  
Vol 13 (4) ◽  
pp. 84 ◽  
Author(s):  
Nikolaos Antonakakis ◽  
Ioannis Chatziantoniou ◽  
David Gabauer
Keyword(s):  
Covariance Structure ◽  
Window Size ◽  
Underlying Structure ◽  
Time Varying ◽  
Parameter Vector ◽  
Vector Autoregressions ◽  
Vector Autoregressive ◽  
Time Varying Parameter ◽  
Rolling Window ◽  
Dynamic Measures

In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures.

scholarly journals Endogenous Time Variation in Vector Autoregressions

2021 ◽  
pp. 1-45
Author(s):  
Danilo Leiva-León ◽  
Luis Uzeda
Keyword(s):  
Model Comparison ◽  
Estimation Algorithm ◽  
Time Varying ◽  
Parameter Vector ◽  
Vector Autoregressions ◽  
Structural Shocks ◽  
New Class ◽  
Us Economy ◽  
Time Varying Parameter ◽  
The Us

Abstract We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence the dynamics of the coefficients in these models. An estimation algorithm and a parametrization conducive to model comparison are also provided. We apply our framework to the US economy. Scenario analysis suggests that, once accounting for the influence of structural shocks on the autoregressive coefficients, the effects of monetary policy on economic activity are larger and more persistent than in an otherwise standard TVP-VAR. Our results also indicate that cost-push shocks play a prominent role in understanding historical changes in inflation-gap persistence.

Common time variation of parameters in reduced-form macroeconomic models

2015 ◽  
Vol 0 (0) ◽  
Author(s):  
Dalibor Stevanovic
Keyword(s):  
Time Variation ◽  
Reduced Form ◽  
Time Varying ◽  
Vector Autoregressions ◽  
Macroeconomic Models ◽  
Variation Of Parameters ◽  
Common Time ◽  
Time Varying Parameter ◽  
Low Dimension ◽  
Structural Vector

AbstractStandard time varying parameter (TVP) models usually assume independent stochastic processes. In this paper, I show that the number of underlying sources of parameters’ time variation is likely to be small, and provide empirical evidence for factor structure amongst TVPs of popular macroeconomic models. In order to test for the presence of low dimension sources of time variation in parameters and estimate their magnitudes, I develop the factor time varying parameter (Factor-TVP) framework and apply it to [Primiceri, G.E. (2005), “Time Varying Structural Vector Autoregressions and Monetary Policy,”

scholarly journals Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions

2018 ◽  
Vol 53 (3) ◽  
pp. 1371-1390 ◽  
Author(s):  
Marco Valerio Geraci ◽  
Jean-Yves Gnabo
Keyword(s):  
Dynamic Network ◽  
Spillover Effects ◽  
Time Varying ◽  
Vector Autoregressions ◽  
Crisis Events ◽  
Time Varying Parameter ◽  
Rolling Window ◽  
The 2008 Financial Crisis ◽  
Window Approach

We propose a market-based framework that exploits time-varying parameter vector autoregressions to estimate the dynamic network of financial spillover effects. We apply it to financials in the Standard & Poor’s 500 index and estimate interconnectedness at the sectoral and institutional levels. At the sectoral level, we uncover two main events in terms of interconnectedness: the Long-Term Capital Management crisis and the 2008 financial crisis. After these crisis events, we find a gradual decrease in interconnectedness, not observable using the classical rolling-window approach. At the institutional level, our framework delivers more stable interconnectedness rankings than other comparable market-based measures.

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