Trends and cycles in economic time series: A Bayesian approach Export

@article{citeulike:1574710, abstract = {Trends and cyclical components in economic time series are modeled in a Bayesian framework. This enables prior notions about the duration of cycles to be used, while the generalized class of stochastic cycles employed allows the possibility of relatively smooth cycles being extracted. The posterior distributions of such underlying cycles can be very informative for policy makers, particularly with regard to the size and direction of the output gap and potential turning points. From the technical point of view a contribution is made in investigating the most appropriate prior distributions for the parameters in the cyclical components and in developing Markov chain Monte Carlo methods for both univariate and multivariate models. Applications to US macroeconomic series are presented.}, author = {Harvey, Andrew C. and Trimbur, Thomas M. and Van Dijk, Herman K.}, citeulike-article-id = {1574710}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jeconom.2006.07.006}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6VC0-4KRY92T-1/2/ba9ceb8ec3fa62991bc4da55212029f6}, doi = {10.1016/j.jeconom.2006.07.006}, journal = {Journal of Econometrics}, keywords = {econometrics}, month = {October}, number = {2}, pages = {618--649}, posted-at = {2007-08-19 05:34:54}, priority = {2}, title = {Trends and cycles in economic time series: A Bayesian approach}, url = {http://dx.doi.org/10.1016/j.jeconom.2006.07.006}, volume = {140}, year = {2007} }

TY - JOUR ID - citeulike:1574710 L3 - citeulike-article-id:1574710 N2 - Trends and cyclical components in economic time series are modeled in a Bayesian framework. This enables prior notions about the duration of cycles to be used, while the generalized class of stochastic cycles employed allows the possibility of relatively smooth cycles being extracted. The posterior distributions of such underlying cycles can be very informative for policy makers, particularly with regard to the size and direction of the output gap and potential turning points. From the technical point of view a contribution is made in investigating the most appropriate prior distributions for the parameters in the cyclical components and in developing Markov chain Monte Carlo methods for both univariate and multivariate models. Applications to US macroeconomic series are presented. IS - 2 JF - Journal of Econometrics EP - 649 TI - Trends and cycles in economic time series: A Bayesian approach VL - 140 SP - 618 KW - econometrics AU - Harvey, Andrew AU - Trimbur, Thomas AU - Van Dijk, Herman PY - 2007/10// UR - http://dx.doi.org/10.1016/j.jeconom.2006.07.006 ER -