The present paper examines the microfoundations of how households form subjective expectations about the macroeconomy. In particular, we are interested in the role of perceived news. The paper outlines a theoretical model where households may give unequal importance (or weights) to „good‟ and „bad‟ news. We also consider whether the relationship is state-varying and has any structural changes. ...

Abstract: A flexible controller for optimal control of linear time-varying stochastic systems with multiple time delays is developed. The plants to be controlled are represented using a multi-input multi-output controlled autoregressive moving average model. The delays are described using a diagonal matrix. Input and output filters in the form of linear time-varying moving average operators are...

This work is concerned with nonlinear time series models and, in particular, with nonparametric models for the dynamics of the mean of the time series. We build on the functional-coefficient autoregressive (FAR) model of Chen and Tsay (1993) which is a generalization of the autoregressive (AR) model where the coefficients are varying and are given by functions of the lagged values of the series...

This article proposes a new approach to evaluate volatility regime switching and volatility contagion in financial markets. A time-varying conditional autoregressive range (TVCARR) model is proposed to capture possible regime switching in the range process. A misspecification test for the conditional autoregressive range (CARR) model against the TVCARR model is introduced. The finite-sample pro...

This paper describes a method of extracting time-varying features that is effective for speech signals with high fundamental frequencies. The proposed method adopts a speech production model that consists of a Time-Varying AutoRegressive (TVAR) process for an articulatory filter and a Hidden Markov Model (HMM) for an excitation source. The model represents waveform amplitude variations by timev...

Discrete time-varying autoregressive moving average (ARMA) models are used to describe realistic earthquake ground motion time histories. Both amplitude and frequency nonstationarities are incorporated in the model. An iterative Kalman filtering scheme is introduced to identify the time-varying parameters of an ARMA model from an actual earthquake record. Several model verification tests are pe...

We extend a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. In particular, we focus on autoregressive models with possibly time-varying intercept and slope and decide on whether their parameters are fixed or evolutive. Stochastic model specification is carried out to di...

evolving models have found applications in many real world systems. in this paper, potentials of the evolving linear models (elms) in tracking control design for nonlinear variable structure systems are introduced. at first, an elm is introduced as a dynamic single input, single output (siso) linear model whose parameters as well as dynamic orders of input and output signals can change through ...