Jean‐Yves Pitarakis
Biographic Data
| ID | 9107501 |
|---|---|
| NAME | Jean‐Yves Pitarakis |
| GIVEN NAMES | Jean‐Yves |
| FAMILY NAME | Pitarakis |
| SIGNATURE | PITARAKIS J Y |
| AFFILIATIONS | University of Southampton |
| ORCID | 0000-0002-6305-7421 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Detecting Sparse Cointegration
We propose a two‐step procedure for detecting sparse cointegration in high‐dimensional single‐equation models. First, we employ the adaptive lasso to identify the subset of integrated covariates driving the long‐run equilibrium relationship. Second, we adopt an information‐theoretic criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding reliance on asymptotic distributions. A key theoretical contrib…
Uncovering Regimes in Out of Sample Forecast Errors from Predictive Regressions
We introduce a set of test statistics for assessing the presence of regimes in out of sample forecast errors produced by recursively estimated linear predictive regressions that can accommodate multiple highly persistent predictors. Our test statistics are designed to be robust to the chosen starting window size and are shown to be both consistent and locally powerful. Their limiting null distributions are also free of nuisance parameters and hen…
Inferring the Predictability Induced by a Persistent Regressor in a Predictive Threshold Model
We develop tests for detecting possibly episodic predictability induced by a persistent predictor. Our framework is that of a predictive regression model with threshold effects and our goal is to develop operational and easily implementable inferences when one does not wish to impose à priori restrictions on the parameters of the model other than the slopes corresponding to the persistent predictor. Differently put our tests for the null hypothes…
Regime-Specific Predictability in Predictive Regressions
Predictive regressions are linear specifications linking a noisy variable such as stock returns to past values of a very persistent regressor with the aim of assessing the presence of predictability. Key complications that arise are the potential presence of endogeneity and the poor adequacy of asymptotic approximations. In this article, we develop tests for uncovering the presence of predictability in such models when the strength or direction o…
No prominent works on this page.
Regime-Specific Predictability in Predictive Regressions
Predictive regressions are linear specifications linking a noisy variable such as stock returns to past values of a very persistent regressor with the aim of assessing the presence of predictability. Key complications that arise are the potential presence of endogeneity and the poor adequacy of asymptotic approximations. In this article, we develop tests for uncovering the presence of predictability in such models when the strength or direction o…
Inferring the Predictability Induced by a Persistent Regressor in a Predictive Threshold Model
We develop tests for detecting possibly episodic predictability induced by a persistent predictor. Our framework is that of a predictive regression model with threshold effects and our goal is to develop operational and easily implementable inferences when one does not wish to impose à priori restrictions on the parameters of the model other than the slopes corresponding to the persistent predictor. Differently put our tests for the null hypothes…
Uncovering Regimes in Out of Sample Forecast Errors from Predictive Regressions
We introduce a set of test statistics for assessing the presence of regimes in out of sample forecast errors produced by recursively estimated linear predictive regressions that can accommodate multiple highly persistent predictors. Our test statistics are designed to be robust to the chosen starting window size and are shown to be both consistent and locally powerful. Their limiting null distributions are also free of nuisance parameters and hen…
Detecting Sparse Cointegration
We propose a two‐step procedure for detecting sparse cointegration in high‐dimensional single‐equation models. First, we employ the adaptive lasso to identify the subset of integrated covariates driving the long‐run equilibrium relationship. Second, we adopt an information‐theoretic criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding reliance on asymptotic distributions. A key theoretical contrib…
Econometrics (4 works) · Mathematics (4 works) · Economics (3 works) · Monetary Policy and Economic Impact (3 works) · Predictability (3 works) · Statistics (3 works) · Complex Systems and Time Series Analysis (2 works) · Computer Science (2 works) · Financial Markets and Investment Strategies (2 works) · Financial Risk and Volatility Modeling (2 works)