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(AIC) in its theoretical derivation and yet can be applied to any model able to generate simulated or predicted data …, regardless of its methodology. Both the AIC and proposed UIC rely on the Kullback-Leibler (KL) distance between model predictions … and real data as a measure of prediction accuracy. Instead of using the maximum likelihood approach like the AIC, the …
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We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and...
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We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic...
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This paper investigates the time-varying impacts of international macroeconomic uncertainty shocks. We use a global vector autoregressive (GVAR) specification with drifting coefficients and factor stochastic volatility in the errors to model six economies jointly. The measure of uncertainty is...
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