Factor augmented vector autoregressions, panel VARs, and global VARs

Factor augmented vector autoregressions, panel VARs, and global VARs

This chapter provides a thorough introduction to panel, global, and factor augmented vector autoregressive models. These models are typically used to capture interactions across units (i.e., countries) and variable types. Since including a large number of countries and/or variables increases the dim...

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Veröffentlicht in:Macroeconomic forecasting in the era of big data
1. Verfasser: Feldkircher, Martin (VerfasserIn)
Weitere Verfasser: Huber, Florian (VerfasserIn), Pfarrhofer, Michael (VerfasserIn)
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Sprache:eng
Veröffentlicht: 2020
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Titel Jahr Verfasser
Dynamic factor models 2020 Doz, Catherine
Penalized time series regression 2020 Kock, Anders Bredahl
Principal component and static factor analysis 2020 Cao, Jianfei
Bayesian model averaging 2020 Hofmarcher, Paul
Forecast evaluation 2020 Cheng, Mingmian
Robust methods for high-dimensional regression and covariance matrix estimation 2020 Avella-Medina, Marco
Frequency domain 2020 Chan, Felix
Factor augmented vector autoregressions, panel VARs, and global VARs 2020 Feldkircher, Martin
Large Bayesian vector autoregressions 2020 Chan, Joshua
Neural networks 2020 Cook, Thomas R.
Boosting 2020 Chu, Jianghao
Unit roots and cointegration 2020 Smeekes, Stephan
Hierarchical Forecasting 2020 Athanasopoulos, George
Variable selection and feature screening 2020 Liu, Wanjun
Bootstrap aggregating and random forest 2020 Lee, Tae-hwy
Turning points and classification 2020 Piger, Jeremy Max
Sources and types of big data for macroeconomic forecasting 2020 Garboden, Philip M. E.
Volatility forecasting in a data rich environment 2020 Bernardi, Mauro
Subspace methods 2020 Boot, Tom
Frequentist averaging 2020 Chan, Felix
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