Parametric covariance models for shock-induced stochastic processes
نویسندگان
چکیده
منابع مشابه
Parametric Covariance Models for Shock - induced Stochastic Processes
A common assumption in the modeling of stochastic processes is that of weak stationarity. Although this is a convenient and sometimes justifiable assumption for many applications, there are other applications for which it is clearly inappropriate. One such application occurs when the process is driven by action at a limited number of sites, or point sources. Interest may lie not only in predict...
متن کاملIdentifiability of Dynamic Stochastic General Equilibrium Models with Covariance Restrictions
This article is concerned with identification problem of parameters of Dynamic Stochastic General Equilibrium Models with emphasis on structural constraints, so that the number of observable variables is equal to the number of exogenous variables. We derived a set of identifiability conditions and suggested a procedure for a thorough analysis of identification at each point in the parameters sp...
متن کاملStochastic Covariance Models∗
A new class of stochastic covariance models based on Wishart distribution is proposed. Three categories of dynamic correlation models are introduced depending on how the timevarying covariance matrix is formulated and whether or not it is a latent variable. A stochastic covariance filter is also developed for filtering and predicting covariances. Extensions of the basic models enable the study ...
متن کاملStationary Points for Parametric Stochastic Frontier Models
The results of Waldman (1982) on the Normal-Half Normal stochastic frontier model are generalized using the theory of the Dirac delta (Dirac, 1930), and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and "wrong skew" results are derived or simulated for c...
متن کاملMonte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using samples, along with density trees generated from such samples. A Monte Carlo version of Baum-Welch (EM) is employed to learn models from data. Regularization during learning is achieved using an exponential shrinking tec...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Planning and Inference
سال: 1999
ISSN: 0378-3758
DOI: 10.1016/s0378-3758(98)00186-4