کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
997515 1481448 2013 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models
موضوعات مرتبط
علوم انسانی و اجتماعی مدیریت، کسب و کار و حسابداری کسب و کار و مدیریت بین المللی
پیش نمایش صفحه اول مقاله
Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models
چکیده انگلیسی

The object of this paper is to produce non-parametric maximum likelihood estimates of forecast distributions in a general non-Gaussian, non-linear state space setting. The transition densities that define the evolution of the dynamic state process are represented in parametric form, but the conditional distribution of the non-Gaussian variable is estimated non-parametrically. The filtered and prediction distributions are estimated via a computationally efficient algorithm that exploits the functional relationship between the observed variable, the state variable and a measurement error with an invariant distribution. Simulation experiments are used to document the accuracy of the non-parametric method relative to both correctly and incorrectly specified parametric alternatives. In an empirical illustration, the method is used to produce sequential estimates of the forecast distribution of realized volatility on the S&P500 stock index during the recent financial crisis. A resampling technique for measuring sampling variation in the estimated forecast distributions is also demonstrated.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Forecasting - Volume 29, Issue 3, July–September 2013, Pages 411–430
نویسندگان
, , , ,