Heino Bohn Nielsen
Biographic Data
| ID | 8921008 |
|---|---|
| NAME | Heino Bohn Nielsen |
| GIVEN NAMES | Heino Bohn |
| FAMILY NAME | Nielsen |
| SIGNATURE | NIELSEN H B |
| AFFILIATIONS | University of Copenhagen |
| ORCID | 0000-0001-8516-161X |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| 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…
Mixed Causal–Noncausal Autoregressions
This paper stresses the bimodality of the likelihood function of the Mixed causal–noncausal AutoRegressions (MAR), and it is shown that the bimodality issue becomes more salient as the causal root approaches unity from below. The consequences are important as the roots of the local maxima are typically interchanged, attributing the noncausal component to the causal one and vice‐versa. This severely changes the interpretation of the results, and t…
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…
UK money demand 1873–2001
No prominent works on this page.
UK money demand 1873–2001
Mixed Causal–Noncausal Autoregressions
This paper stresses the bimodality of the likelihood function of the Mixed causal–noncausal AutoRegressions (MAR), and it is shown that the bimodality issue becomes more salient as the causal root approaches unity from below. The consequences are important as the roots of the local maxima are typically interchanged, attributing the noncausal component to the causal one and vice‐versa. This severely changes the interpretation of the results, and t…
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…
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…
Econometrics (3 works) · Mathematics (3 works) · Cointegration (2 works) · Computer Science (2 works) · Economics (2 works) · Monetary Policy and Economic Impact (2 works) · Multivariate statistics (2 works) · Statistics (2 works) · Advanced Causal Inference Techniques (1 work) · Autoregressive model (1 work)