We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure employs continuous approximations of filtering densities, and delivers unconditionally optimal global approximations of...

We propose a strategy for assessing structural stability in time-series frameworks when potential change dates are unknown. Existing stability tests are effective in detecting structural change, but procedures for identifying timing are imprecise, especially in assessing the stability of...

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We develop a model of GDP growth under which regime changes are triggered stochastically by an observable tension index, constructed as the geometric sum of deviations of actual GDP growth from a corresponding sustainable rate. Within expansionary regimes, the tension index tends to increase,...

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We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure approximates necessary integrals using continuous approximations of target densities. Construction is achieved via efficient...