scholarly journals Structural Vector Autoregressive Models with more Shocks than Variables Identified via Heteroskedasticity

2020 ◽  
Author(s):  
Helmut Lütkepohl
Keyword(s):  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Structural Vector

scholarly journals Nonlinear Structural Vector Autoregressive Models With Application to Directed Brain Networks

2019 ◽  
Vol 67 (20) ◽  
pp. 5325-5339 ◽  
Author(s):  
Yanning Shen ◽  
Georgios B. Giannakis ◽  
Brian Baingana
Keyword(s):  
Brain Networks ◽  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Nonlinear Structural ◽  
Structural Vector

scholarly journals Testing Identification via Heteroskedasticity in Structural Vector Autoregressive Models

2018 ◽  
Author(s):  
Helmut Lütkepohl ◽  
Mika Meitz ◽  
Aleksei Netsunajev ◽  
Pentti Saikkonen
Keyword(s):  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Structural Vector

scholarly journals Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series

Biometrika ◽  
2019 ◽  
Vol 106 (2) ◽  
pp. 433-452
Author(s):  
A Tank ◽  
E B Fox ◽  
A Shojaie
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Causal Structure ◽  
Sampling Rate ◽  
Autoregressive Models ◽  
Estimation Methods ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Mixed Frequency ◽  
Structural Vector

Summary Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector autoregressive models. We present a unifying framework for parameter identifiability and estimation under subsampling and mixed frequencies when the noise, or shocks, is non-Gaussian. By studying the structural case, we develop identifiability and estimation methods for the causal structure of lagged and instantaneous effects at the desired time scale. We further derive an exact expectation-maximization algorithm for inference in both subsampled and mixed-frequency settings. We validate our approach in simulated scenarios and on a climate and an econometric dataset.

Dealing with Multiple Solutions in Structural Vector Autoregressive Models

2016 ◽  
Vol 51 (2-3) ◽  
pp. 357-373 ◽  
Author(s):  
Adriene M. Beltz ◽  
Peter C. M. Molenaar
Keyword(s):  
Multiple Solutions ◽  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Structural Vector

DOES FEAR LEAD TO RECESSIONS?

2015 ◽  
Vol 20 (5) ◽  
pp. 1247-1263 ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou
Keyword(s):  
Transition Probability ◽  
Autoregressive Models ◽  
Consumer Confidence ◽  
Time Varying ◽  
Structural Shocks ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Economic Downturns ◽  
Economic Recessions ◽  
Structural Vector

This paper investigates the link between consumer pessimism and U.S. economic recessions empirically. First we use structural vector autoregressive models to identify negative structural shocks to consumer confidence, which are used as a proxy for recession fear. We then apply probit models and time-varying-transition-probability Markov-switching autoregressive models to investigate how the lack of consumer confidence affects the probability of recession. We find that recession fear leads to a higher probability of economic downturns. Furthermore, strong evidence exists that an increase in market pessimism may push the economy from an expansion state to a recession state. We also find weaker evidence suggesting that a lack of consumer confidence may trap the economy in the depressed regime longer. We conclude that a lack of confidence can push the economy into recession.

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