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 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...

The volatility of growth in U.S. real GDP declined dramatically in the mid-1980s. Viewed through the lens of linear autoregressive models, this phenomenon appears to be the result of a structural break in the innovation process that drives GDP fluctuations. We present an alternative model that...

We propose a strategy for assessing structural stability in time-series frameworks when potential change dates are unknown. Existing tests for structural stability have proven to be effective in detecting the presence of structural change, but procedures for identifying timing are highly...

We develop a numerical procedure that facilitates efficient filtering 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 importance...