Modeling Compositional Time Series with Vector Autoregressive Models

2015 ◽  
Vol 34 (4) ◽  
pp. 303-314 ◽  
Author(s):  
Petra Kynčlová ◽  
Peter Filzmoser ◽  
Karel Hron
Keyword(s):  
Time Series ◽  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive

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.

scholarly journals Nonlinear Models in Macroeconometrics

2018 ◽  
Author(s):  
Timo Teräsvirta
Keyword(s):  
Time Series ◽  
Nonlinear Models ◽  
Nonlinear Time Series ◽  
Single Equation ◽  
Autoregressive Models ◽  
Smooth Transition ◽  
Time Series Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Long Time

Many nonlinear time series models have been around for a long time and have originated outside of time series econometrics. The stochastic models popular univariate, dynamic single-equation, and vector autoregressive are presented and their properties considered. Deterministic nonlinear models are not reviewed. The use of nonlinear vector autoregressive models in macroeconometrics seems to be increasing, and because this may be viewed as a rather recent development, they receive somewhat more attention than their univariate counterparts. Vector threshold autoregressive, smooth transition autoregressive, Markov-switching, and random coefficient autoregressive models are covered along with nonlinear generalizations of vector autoregressive models with cointegrated variables. Two nonlinear panel models, although they cannot be argued to be typically macroeconometric models, have, however, been frequently applied to macroeconomic data as well. The use of all these models in macroeconomics is highlighted with applications in which model selection, an often difficult issue in nonlinear models, has received due attention. Given the large amount of nonlinear time series models, no unique best method of choosing between them seems to be available.

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