Christophe Planas
Biographic Data
| ID | 8921074 |
|---|---|
| NAME | Christophe Planas |
| GIVEN NAMES | Christophe |
| FAMILY NAME | Planas |
| SIGNATURE | PLANAS C |
| AFFILIATIONS | Joint Research Centre |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2001 |
| LATEST PUBLICATION YEAR | 2008 |
| H-INDEX | 0 |
Bayesian Analysis of the Output Gap
Our objective is to build output gap estimates that benefit from information provided by Phillips curve theory and business cycle studies. For this, we develop a Bayesian analysis of the bivariate Phillips curve model proposed by Kuttner for estimating potential output. Given our priors, we obtain samples from parameters and state variables joint posterior distribution following a Gibbs sampling strategy. We sample the state variables given param…
Overcoming Nonadmissibility in Arima-Model-Based Signal Extraction
We analyze the situation in which the decomposition of a time series into orthogonal balanced components as performed by the AR IMA-model-based (AMB) method is nonadmissible. We show that considering top-heavy models for the components can solve the problem. The top-heavy decomposition is derived and the improvement achieved is illustrated by an application to a class of models often encountered in practice. Two empirical applications allow us to…
No prominent works on this page.
Overcoming Nonadmissibility in Arima-Model-Based Signal Extraction
We analyze the situation in which the decomposition of a time series into orthogonal balanced components as performed by the AR IMA-model-based (AMB) method is nonadmissible. We show that considering top-heavy models for the components can solve the problem. The top-heavy decomposition is derived and the improvement achieved is illustrated by an application to a class of models often encountered in practice. Two empirical applications allow us to…
Bayesian Analysis of the Output Gap
Our objective is to build output gap estimates that benefit from information provided by Phillips curve theory and business cycle studies. For this, we develop a Bayesian analysis of the bivariate Phillips curve model proposed by Kuttner for estimating potential output. Given our priors, we obtain samples from parameters and state variables joint posterior distribution following a Gibbs sampling strategy. We sample the state variables given param…
Econometrics (2 works) · Mathematics (2 works) · Statistics (2 works) · Autoregressive integrated moving average (1 works) · Bayes estimator (1 works) · Bayesian probability (1 works) · Bivariate analysis (1 works) · Blind Source Separation Techniques (1 works) · Computer Science (1 works) · Economics (1 works)