To the best of our knowledge, most of the few methodological studies which analyze the impact of faked interviews on survey results are based on ?artificial fakes? generated by project students in a ?laboratory environment?. In contrast, panel data provide a unique opportunity to identify data...

To the best of our knowledge, most of the few methodological studies which analyze the impact of faked interviews on survey results are based on 'artificial fakes' generated by project students in a 'laboratory environment'. In contrast, panel data provide a unique opportunity to identify data...

Our starting place is the first order seasonal autoregressive model. Its series are shown to have canonical model-based decompositions whose finite-sample estimates, filters, and error covariances have simple revealing formulas from basic linear regression.We obtain analogous formulas for...

This paper focuses on the valuation and hedging of gas storage facilities, using a spot-based valuation framework coupled with a financial hedging strategy implemented with futures contracts. The contributions of this paper are two-fold. Firstly, we propose a model that unifies the dynamics of...

We explore the implications of digitalization for monetary policy, both in terms of how monetary policy affects the economy and in terms of data analysis and communication with the public.

Classical regression analysis uses partial coefficients to measure the influences of some variables (regressors) on another variable (regressand). However, a descriptive point of view shows that these coefficients are very bad measures of influence. Their interpretation as an average change of...

This paper constructs the probability space underlying the random variable of any time dependent econometric specification. The construction links concrete economic activity, both perceived and recorded, and econometric formulations. Furthermore, it is argued that the probability events...

Explained variance (R^2) is a familiar summary of the fit of a linear regression and has been generalized in various ways to multilevel (hierarchical) models. The multilevel models we consider in this paper are characterized by hierarchical data structures in which individuals are grouped into...

Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral- t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative...