Alain Hecq
Biographic Data
| ID | 8759427 |
|---|---|
| NAME | Alain Hecq |
| GIVEN NAMES | Alain |
| FAMILY NAME | Hecq |
| SIGNATURE | HECQ A |
| AFFILIATIONS | Department of Quantitative Economics Maastricht University P.O.Box 616, 6200 MD Maastricht The Netherlands |
| ORCID | 0000-0002-0548-8215 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Dimension Reduction for High‐Dimensional Vector Autoregressive Models
This article aims to decompose a large dimensional vector autoregressive (VAR) model into two components, the first one being generated by a small‐scale VAR and the second one being a white noise. Hence, a reduced number of common components generates the entire dynamics of the large system through a VAR structure. This modelling, which we label as the dimension‐reducible VAR, extends the common feature approach to high‐dimensional systems, and i…
Mixed causal–noncausal autoregressions with exogenous regressors
Mixed causal–noncausal autoregressive (MAR) models have been proposed to model time series exhibiting nonlinear dynamics. Possible exogenous regressors are typically substituted into the error term to maintain the MAR structure of the dependent variable. We introduce a representation including these covariates called MARX to study their direct impact. The asymptotic distribution of the MARX parameters is derived for a class of non‐Gaussian densit…
No prominent works on this page.
Mixed causal–noncausal autoregressions with exogenous regressors
Mixed causal–noncausal autoregressive (MAR) models have been proposed to model time series exhibiting nonlinear dynamics. Possible exogenous regressors are typically substituted into the error term to maintain the MAR structure of the dependent variable. We introduce a representation including these covariates called MARX to study their direct impact. The asymptotic distribution of the MARX parameters is derived for a class of non‐Gaussian densit…
Dimension Reduction for High‐Dimensional Vector Autoregressive Models
This article aims to decompose a large dimensional vector autoregressive (VAR) model into two components, the first one being generated by a small‐scale VAR and the second one being a white noise. Hence, a reduced number of common components generates the entire dynamics of the large system through a VAR structure. This modelling, which we label as the dimension‐reducible VAR, extends the common feature approach to high‐dimensional systems, and i…
Autoregressive model (2 works) · Computer Science (2 works) · Econometrics (2 works) · Market Dynamics and Volatility (2 works) · Mathematics (2 works) · Monetary Policy and Economic Impact (2 works) · Statistics (2 works) · Artificial Intelligence (1 work) · Commodity (1 work) · Complex Systems and Time Series Analysis (1 work)