Anders Rahbek
Biographic Data
| ID | 8795326 |
|---|---|
| NAME | Anders Rahbek |
| GIVEN NAMES | Anders |
| FAMILY NAME | Rahbek |
| SIGNATURE | RAHBEK A |
| AFFILIATIONS | University of Copenhagen |
| ORCID | 0000-0002-2549-1913 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1998 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Confidence Intervals for Price Discovery
This paper discusses asymptotic and bootstrap confidence intervals for multivariate permanent‐transitory decompositions of cointegrated vector autoregressive I(1) systems, with a focus on price discovery. Alternative estimators of the permanent components are compared in terms of efficiency also under separable linear restrictions on the parameters, and it is found that the one based on model residuals is most efficient. Bootstrap implementations…
Specification Tests for GARCH Processes with Nuisance Parameters on the Boundary
This article develops tests for the correct specification of the conditional variance function in GARCH models when the true parameter may lie on the boundary of the parameter space. The test statistics considered are of Kolmogorov-Smirnov and Cramér-von Mises type, and are based on empirical processes marked by centered squared residuals. The limiting distributions of the test statistics depend on unknown nuisance parameters in a nontrivial way,…
Bootstrapping Noncausal Autoregressions
In this article, we develop new bootstrap-based inference for noncausal autoregressions with heavy-tailed innovations. This class of models is widely used for modeling bubbles and explosive dynamics in economic and financial time series. In the noncausal, heavy-tail framework, a major drawback of asymptotic inference is that it is not feasible in practice as the relevant limiting distributions depend crucially on the (unknown) decay rate of the t…
Similarity Issues in Cointegration Analysis
Usually cointegration models involve a dynamic, stochastic component as well as deterministic components. This paper identifies relevant cointegration models in terms of interpretability and similarity with respect to parameters of deterministic components. Similarity implies that inference on cointegration rank or common trends can be separated from inference on parameters of deterministic components. The idea is that the functional form and the…
Asymptotic Inference on Cointegrating Rank in Partial Systems
The likelihood ratio test for cointegrating rank is analyzed for partial (or conditional) systems in the vector autoregressive error-correction model. Under the assumption of weak exogeneity for the cointegrating parameters, the asymptotic distributions are given and tables of critical values are provided. A discussion is given of some of the assumptions of the model, why they are needed, and how they are tested
No prominent works on this page.
Asymptotic Inference on Cointegrating Rank in Partial Systems
The likelihood ratio test for cointegrating rank is analyzed for partial (or conditional) systems in the vector autoregressive error-correction model. Under the assumption of weak exogeneity for the cointegrating parameters, the asymptotic distributions are given and tables of critical values are provided. A discussion is given of some of the assumptions of the model, why they are needed, and how they are tested
Similarity Issues in Cointegration Analysis
Usually cointegration models involve a dynamic, stochastic component as well as deterministic components. This paper identifies relevant cointegration models in terms of interpretability and similarity with respect to parameters of deterministic components. Similarity implies that inference on cointegration rank or common trends can be separated from inference on parameters of deterministic components. The idea is that the functional form and the…
Bootstrapping Noncausal Autoregressions
In this article, we develop new bootstrap-based inference for noncausal autoregressions with heavy-tailed innovations. This class of models is widely used for modeling bubbles and explosive dynamics in economic and financial time series. In the noncausal, heavy-tail framework, a major drawback of asymptotic inference is that it is not feasible in practice as the relevant limiting distributions depend crucially on the (unknown) decay rate of the t…
Specification Tests for GARCH Processes with Nuisance Parameters on the Boundary
This article develops tests for the correct specification of the conditional variance function in GARCH models when the true parameter may lie on the boundary of the parameter space. The test statistics considered are of Kolmogorov-Smirnov and Cramér-von Mises type, and are based on empirical processes marked by centered squared residuals. The limiting distributions of the test statistics depend on unknown nuisance parameters in a nontrivial way,…
Confidence Intervals for Price Discovery
This paper discusses asymptotic and bootstrap confidence intervals for multivariate permanent‐transitory decompositions of cointegrated vector autoregressive I(1) systems, with a focus on price discovery. Alternative estimators of the permanent components are compared in terms of efficiency also under separable linear restrictions on the parameters, and it is found that the one based on model residuals is most efficient. Bootstrap implementations…
Mathematics (4 works) · Computer Science (3 works) · Econometrics (3 works) · Statistics (3 works) · Applied Mathematics (2 works) · Cointegration (2 works) · Financial Risk and Volatility Modeling (2 works) · Artificial Intelligence (1 work) · Asymptotic analysis (1 work) · Autoregressive conditional heteroskedasticity (1 work)