A particle filter approach for general mixed-frequency state-space models is considered. It employs a backward smoother to filter high-frequency state variables from low-frequency observations. Moreover, it preserves the sequential nature of particle filters, allows for non-Gaussian shocks and...

This paper develops a new Bayesian algorithm to efficiently estimate non-linear/non-Gaussian state space models with abruptly changing parameters. Within the Particle Gibbs framework developed by Andrieu et al. (2010), the proposed algorithm effectively combines two ideas: ancestor sampling and...

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter, this distribution is generated by the state-transition equation. While...

This paper presents a simple and efficient exogenous outlier detection & estimation algorithm introduced in a regularized version of the Kalman Filter (KF). Exogenous outliers that may occur in the observations are considered as an additional stochastic impulse process in the KF observation...

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t − 1 particle values into time t values. In the widely-used bootstrap particle filter this distribution is generated by the state- transition equation. While...

We prove that multifractal functions, characterized by their wavelet representation can be estimated in the white noise model by a Bayesian estimation method. We give rates of convergence for two different models. Further, we study empirical methods for estimating the hyperparameters of the...

The estimation and the analysis of long memory parameters have mainly focused on the analysis of long-range dependence in stock return volatility using traditional time and spectral domain estimators of long memory. The definitive ubiquity and existence of long memory in the volatility of stock...

Taking into consideration the specifics of the Russian economy such as dependency on oil and gas drilling & production, and including the current context of the Western sanctions, COVID-19 pandemic, as well as somewhat idiosyncratic potential output development, the main aim of this paper is to...

The asymptotic theory for the memory parameter estimator constructed from the log-regression with wavelets is incomplete for 1/f processes that are not necessarily Gaussian or linear. Such a theory is necessary due to the importance of non-Gaussian and non-linear long memory models in describing...

Projecting the course of infectious diseases and assessing the likely impact of policies to contain them require reliable estimates of the parameters in dynamic models of disease transmission. However, such estimation is difficult, especially for emerging diseases such as COVID-19, due to...