Vector autoregressive moving average identification for macroeconomic modeling: A new methodology

Poskitt, D. S.

doi:10.1016/j.jeconom.2016.02.011

Cited by 12 publications

(7 citation statements)

References 35 publications

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“…Vector Autoregression Moving-Average (VARMA) is the generalization of the ARMA model to forecast a multivariate time series. Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX) is the extension of the VARMA model along with the exogenous variables or covariates as the parallel independent input sequences (Poskitt, 2016 ). Seasonal ARIMA or Seasonal Autoregressive Integrated Moving-Average (SARIMA) is an extended version of ARIMA with the ability to capture the seasonality of the time series (Hyndman and Athanasopoulos, 2018 ) and Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX) is a method with exogenous data to improve the prediction result.…”

Section: Time Series Prediction Methods For Mitigating Time Delay mentioning

confidence: 99%

A Brief Survey of Telerobotic Time Delay Mitigation

2020

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There is a substantial number of telerobotics and teleoperation applications ranging from space operations, ground/aerial robotics, drive-by-wire systems to medical interventions. Major obstacles for such applications include latency, channel corruptions, and bandwidth which limit teleoperation efficacy. This survey reviews the time delay problem in teleoperation systems. We briefly review different solutions from early approaches which consist of control-theory-based models and user interface designs and focus on newer approaches developed since 2014. Future solutions to the time delay problem will likely be hybrid solutions which include modeling of user intent, prediction of robot movements, and time delay prediction all potentially using time series prediction methods. Hence, we examine methods that are primarily based on time series prediction. Recent prediction approaches take advantage of advances in nonlinear statistical models as well as machine learning and neural network techniques. We review Recurrent Neural Networks, Long Short-Term Memory, Sequence to Sequence, and Generative Adversarial Network models and examine each of these approaches for addressing time delay. As time delay is still an unsolved problem, we suggest some possible future research directions from information-theory-based modeling, which may lead to promising new approaches to advancing the field.

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Section: Time Series Prediction Methods For Mitigating Time Delay mentioning

confidence: 99%

A Brief Survey of Telerobotic Time Delay Mitigation

2020

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“…Over the past two decades, a number of authors (e.g., Athanasopoulos, Poskitt, & Vahid, 2012;Athanasopoulos & Vahid, 2008;Dufour & Pelletier, 2014;Dufour & Stevanović, 2013;Kascha & Trenkler, 2015;Lütkepohl & Claessen, 1997;Lütkepohl & Poskitt, 1996;Poskitt, 2016) have pointed out this unfortunate phenomenon and various approaches have been proposed aimed at making VARMAs accessible to applied macroeconomists. Nevertheless, VARs continue to dominate in this field.…”

Section: Introductionmentioning

confidence: 99%

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Chan

Eisenstat

2017

J of Applied Econometrics

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Summary Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general vector autoregressive moving averages (VARMAs). A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, particularly in terms of developing useful extensions. We address these computational challenges with a Bayesian approach. Specifically, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with time‐varying vector moving average (VMA) coefficients and stochastic volatility. We illustrate the methodology through a macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.

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“…Over the past two decades, a number of authors (e.g., Lütkepohl and Poskitt, 1996;Lütkepohl and Claessen, 1997;Athanasopoulos and Vahid, 2008;Athanasopoulos, Poskitt, and Vahid, 2012;Dufour and Stevanović, 2013;Dufour and Pelletier, 2014;Kascha and Trenkler, 2014;Poskitt, 2016) have pointed out this unfortunate phenomenon and various approaches have been proposed aimed at making VARMAs accessible to applied macroeconomists. Nevertheless, VARs continue to dominate in this field.…”

Section: Introductionmentioning

confidence: 99%

Efficient Estimation of Bayesian VARMAs with Time-Varying Coefficients

Chan

Eisenstat

2015

SSRN Journal

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Empirical work in macroeconometrics has been mostly restricted to using VARs, even though there are strong theoretical reasons to consider general VARMAs. A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, particularly in terms of developing useful extensions. We address these computational challenges with a Bayesian approach. Specifically, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with timevarying VMA coefficients and stochastic volatility. We illustrate the methodology through a macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.

show abstract

Vector autoregressive moving average identification for macroeconomic modeling: A new methodology

Cited by 12 publications

References 35 publications

A Brief Survey of Telerobotic Time Delay Mitigation

A Brief Survey of Telerobotic Time Delay Mitigation

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Efficient Estimation of Bayesian VARMAs with Time-Varying Coefficients

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