Adaptive non-linear Bayesian estimation for bounded-noise systems†

1974 ◽  
Vol 19 (1) ◽  
pp. 203-211 ◽  
Author(s):  
TE-SON KUO ◽  
JAMES R. ROWLAND
Keyword(s):  
Bayesian Estimation ◽  
Bounded Noise ◽  
Non Linear

Nearly Exact Bayesian Estimation of Non-linear No-Arbitrage Term-Structure Models*

2021 ◽  
Author(s):  
Marcello Pericoli ◽  
Marco Taboga
Keyword(s):  
Bayesian Estimation ◽  
Term Structure ◽  
Broad Class ◽  
Model Parameters ◽  
State Variables ◽  
Computationally Efficient ◽  
Macroeconomic Factors ◽  
No Arbitrage ◽  
Term Structure Models ◽  
Non Linear

Abstract We propose a general method for the Bayesian estimation of a very broad class of non-linear no-arbitrage term-structure models. The main innovation we introduce is a computationally efficient method, based on deep learning techniques, for approximating no-arbitrage model-implied bond yields to any desired degree of accuracy. Once the pricing function is approximated, the posterior distribution of model parameters and unobservable state variables can be estimated by standard Markov Chain Monte Carlo methods. As an illustrative example, we apply the proposed techniques to the estimation of a shadow-rate model with a time-varying lower bound and unspanned macroeconomic factors.

STOCHASTIC AVERAGING OF STRONGLY NON-LINEAR OSCILLATORS UNDER BOUNDED NOISE EXCITATION

2002 ◽  
Vol 254 (2) ◽  
pp. 245-267 ◽  
Author(s):  
Z.L. HUANG ◽  
W.Q. ZHU ◽  
Y.Q. NI ◽  
J.M. KO
Keyword(s):  
Stochastic Averaging ◽  
Bounded Noise ◽  
Non Linear
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