Penalisation Methods in Fitting High‐Dimensional Cointegrated Vector Autoregressive Models: A Review
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Penalisation Methods in Fitting High‐Dimensional Cointegrated Vector Autoregressive Models : A Review. / Levakova, Marie; Ditlevsen, Susanne.
In: International Statistical Review, 2024.Research output: Contribution to journal › Review › Research › peer-review
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TY - JOUR
T1 - Penalisation Methods in Fitting High‐Dimensional Cointegrated Vector Autoregressive Models
T2 - A Review
AU - Levakova, Marie
AU - Ditlevsen, Susanne
PY - 2024
Y1 - 2024
N2 - Cointegration has shown useful for modeling non-stationary data with long-run equilibrium relationships among variables, with applications in many fields such as econometrics, climate research and biology. However, the analyses of vector autoregressive models are becoming more difficult as data sets of higher dimensions are becoming available, in particular because the number of parameters is quadratic in the number of variables. This leads to lack of statistical robustness, and regularisation methods are paramount for obtaining valid estimates. In the last decade, many papers have appeared suggesting different penalisation approaches to the inference problem. Here, we make a comprehensive review of different penalisation methods adapted to the specific structure of vector cointegrated models suggested in the literature, with relevant references to software packages. The methods are evaluated and compared according to a range of error measures in a simulation study, considering combinations of low and high dimension of the system and small and large sample sizes.
AB - Cointegration has shown useful for modeling non-stationary data with long-run equilibrium relationships among variables, with applications in many fields such as econometrics, climate research and biology. However, the analyses of vector autoregressive models are becoming more difficult as data sets of higher dimensions are becoming available, in particular because the number of parameters is quadratic in the number of variables. This leads to lack of statistical robustness, and regularisation methods are paramount for obtaining valid estimates. In the last decade, many papers have appeared suggesting different penalisation approaches to the inference problem. Here, we make a comprehensive review of different penalisation methods adapted to the specific structure of vector cointegrated models suggested in the literature, with relevant references to software packages. The methods are evaluated and compared according to a range of error measures in a simulation study, considering combinations of low and high dimension of the system and small and large sample sizes.
U2 - 10.1111/insr.12553
DO - 10.1111/insr.12553
M3 - Review
JO - International Statistical Review
JF - International Statistical Review
SN - 0306-7734
ER -
ID: 367464668