Ambient Seismic Noise Image of the Structurally Controlled Heat and Fluid Feeder Pathway at Campi Flegrei Caldera

The TIDES-COST Action (STSM-ES1401-34011) provided a travel grant to framework the research project. The Japan Society for the Promotion of Science - Short-Term Fellowship (JSPS/OF215/022) financed the work, undertaken at Tohoku University and concluded at the University of Aberdeen. We thank Giuseppe Vilardo and Eliana Bellucci Sessa for providing the geomorphological maps, and Simona Petrosino and Paola Cusano for the P- and S-wave pickings used to localise the seismicity. Informal revisions from Guido Ventura, Nick Rawlinson and Chris Kilburn helped us improving the analyses and interpretation, respectively. We acknowledge the help of Naveed Khan in parallelising the codes and two anonymous reviewers who improved the analysis, interpretation, and readibility with their comments. All data to reproduce the maps can be downloaded from the World Data Center PANGAEA data repository, permanent link https://doi.pangaea.de/10.1594/PANGAEA.890238.Peer reviewedPublisher PD

Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure

Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data mod- els with hierarchical error components that allow for strong cross-sectional dependence through unobserved heterogeneous global and local factors. We propose consistent es- timation procedures by extending the common correlated e¤ects (CCE) estimation ap- proach proposed by Pesaran (2006). The standard CCE approach needs to be modied in order to account for the hierarchical factor structure in 3D panels. Further, we pro- vide the associated asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is confirmed by Monte Carlo simulation studies. We also demonstrate the empirical usefulness of the proposed approach through an ap- plication to a 3D panel gravity model of bilateral export flows

Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure

Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-sectional dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide the associated asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is con…rmed by Monte Carlo simulation studies. We also demonstrate the empirical usefulness of the proposed approach through an application to a 3D panel gravity model of bilateral export flows

An LM Test for the Conditional Independence between Regressors and Factor Loadings in Panel Data Models with Interactive Effects

A huge literature on modelling cross-sectional dependence in panels has been developed using interactive effects (IE). One area of contention is the hypothesis concerned with whether the regressors and factor loadings are correlated or not. Under the null hypothesis that they are conditionally independent, we can still apply the consistent and robust two-way fixed effects estimator. As an important specification test we develop an LM test for both static and dynamic panels with IE. Simulation results confirm the satisfactory performance of the LM test in small samples. We demonstrate its usefulness with an application to a total of 22 datasets, including static panels with a small T and dynamic panels with serially correlated factors, providing convincing evidence that the null hypothesis is not rejected in many datasets

Testing for Correlation between the Regressors and Factor Loadings in Heterogeneous Panels with Interactive Effects

A large literature on modelling cross-section dependence in panels has been developed through interactive effects. However, there are areas where research has not really caught on yet. One such area is the one concerned with whether the regressors are correlated with factor loadings or not. This is an important issue because if the regressors are uncorrelated with loadings, we can simply use the consistent two-way fixed effects (FE) estimator without employing any more sophisticated econometric methods such as the principal component (PC) or the common correlated effects estimators. We explore this issue, which has received surprisingly little attention and propose a Hausman-type test to address the matter. Further, we develop two nonparametric variance estimators for the FE and PC estimators as well as their difference, that are robust to the presence of heteroscedasticity, autocorrelation and slope heterogeneity. Under the null hypothesis of no correlation between the regressors and loadings the proposed test follows the chi-squared distribution asymptotically. Monte Carlo simulation results confirm satisfactory size and power performance of the test even in small samples. Finally, we provide extensive empirical evidence in favour of uncorrelated factor loadings. In this situation, the FE estimator would provide a simple and robust estimation strategy which is invariant to nontrivial computational issues associated with the PC estimator

Intentions to Return of Clandestine Migrants: The Perverse Effect of Illegality on Skills”—A Reply to the Note

The purpose of this reply is twofold. First, we discuss the major point raised by Stark and Lukasz (Review of Development Economics 17, no. 1 (2013):156–62), i.e. the fact that a framework which explicitly considers asymmetric information is correct and would imply a reversal of our finding. Although, we acknowledge that the mechanism highlighted by the authors is an alternative explanation to return decisions, we argue that the suggested framework is unsuitable in the specific context analyzed in our paper (as well as most real-world situations). Instead, the assumptions underlying our simple theoretical model are strictly linked to data availability in order to perform a sensible empirical analysis. Second, we present a slightly different version of the model proposed in the original article that overcomes possible inconsistencies on the saving behavior of the migrants. Although all the computations are shown in one of the articles cited in our published paper, we now prefer to show them fully in this issue of the Review. The conclusions of our theoretical model do not change. Hence, we conclude that the empirical evidence of the original article—which is the main contribution of our work—is supported by a robust framework