Ignace De Vos
Biographic Data
| ID | 8921005 |
|---|---|
| NAME | Ignace De Vos |
| GIVEN NAMES | Ignace |
| FAMILY NAME | De Vos |
| SIGNATURE | DE VOS I |
| AFFILIATIONS | Department of Econometrics and Data Science Vrije Universiteit Amsterdam Amsterdam the Netherlands |
| ORCID | 0000-0002-6217-9160 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Panel Sequential Group Estimation of Interactive Effects Models
This paper proposes a novel procedure to identify latent groups in the slopes of panel data models with interactive effects. The method is straightforward to apply and relies only on closed‐form estimators when evaluating the objective function. This achieves substantial computational gains compared to alternative non‐linear methods and enables estimation even in large datasets. We establish consistency of the procedure and confirm through simula…
Cross-Section Bootstrap for CCE Regressions with General Unknown Factors
The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of u…
Handling Distinct Correlated Effects with CCE
The common correlated effects (CCE) approach by Pesaran is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e., unobserved common factors are approximated with cross‐section averages of the observables, the estimator is highly flexible and lends itself to a wide range of applications. Despite such flexibility, however, the properties of CCE estimators are typically only examined under the restri…
Bias-Corrected Common Correlated Effects Pooled Estimation in Dynamic Panels
This article extends the common correlated effects pooled (CCEP) estimator to homogenous dynamic panels. In this setting, CCEP suffers from a large bias when the time span (T) of the dataset is fixed. We develop a bias-corrected CCEP estimator that is consistent as the number of cross-sectional units (N) tends to infinity, for T fixed or growing large, provided that the specification is augmented with a sufficient number of cross-sectional averag…
No prominent works on this page.
Bias-Corrected Common Correlated Effects Pooled Estimation in Dynamic Panels
This article extends the common correlated effects pooled (CCEP) estimator to homogenous dynamic panels. In this setting, CCEP suffers from a large bias when the time span (T) of the dataset is fixed. We develop a bias-corrected CCEP estimator that is consistent as the number of cross-sectional units (N) tends to infinity, for T fixed or growing large, provided that the specification is augmented with a sufficient number of cross-sectional averag…
Handling Distinct Correlated Effects with CCE
The common correlated effects (CCE) approach by Pesaran is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e., unobserved common factors are approximated with cross‐section averages of the observables, the estimator is highly flexible and lends itself to a wide range of applications. Despite such flexibility, however, the properties of CCE estimators are typically only examined under the restri…
Panel Sequential Group Estimation of Interactive Effects Models
This paper proposes a novel procedure to identify latent groups in the slopes of panel data models with interactive effects. The method is straightforward to apply and relies only on closed‐form estimators when evaluating the objective function. This achieves substantial computational gains compared to alternative non‐linear methods and enables estimation even in large datasets. We establish consistency of the procedure and confirm through simula…
Cross-Section Bootstrap for CCE Regressions with General Unknown Factors
The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of u…
Spatial and Panel Data Analysis (4 works) · Estimator (3 works) · Econometrics (2 works) · Energy, Environment, Economic Growth (2 works) · Fiscal Policy and Economic Growth (2 works) · Inference (2 works) · Mathematics (2 works) · Panel data (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works)