This article concerns parameter estimation for general state space models, following a frequentist likelihood-based approach. Since exact methods for computing and maximizing the likelihood function are usually not feasible, approximate solutions, based on Monte Carlo or numerical methods, have...

A composite likelihood consists of a combination of valid likelihood objects, usually related to small subsets of data. The merit of composite likelihood is to reduce the computational complexity so that it is possible to deal with large datasets and very complex models, even when the use of...

This paper offers a methodology to assess the internal productive efficiency of National Regulatory Authorities (NRAs) based on the performances of regulated markets, measured in terms of the degree of market efficiency (either static or dynamic). The estimation procedure is based on a Data...