Dynamic modulus characterizes the viscoelastic behavior of asphalt materials and is the most important input parameter for design and rehabilitation of flexible pavements using Mechanistic–Empirical Pavement Design Guide (MEPDG). Laboratory determination of dynamic modulus is very expensive and time consuming. To overcome this challenge, several predictive models were developed to determine dyn...

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of...

Based on the Jones (1971) model, we construct two dynamic models of international trade in which the rate of time preference is either constant or time-varying. The main purpose is to study whether and under what conditions the results derived from the Jones model still hold in the dynamic framework. It is shown that the results of dynamic models may be similar or different to those obtained in...

I introduce posterior simulation methods for a dynamic latent factor model featuring both mean and variance factors. The cross-sectional dimension may be large, so the methods are applicable to data-rich environments. I apply the new methods in two empirical applications. The first involves a panel of 10 currencies, with daily log returns observed over a decade; the second, a panel of 134 real ...

This paper concerns the modelling of stochastic processes by means of dynamic factor models. In such models the observed process is decomposed into a structured part called the latent process, and a remainder that is called noise. The observed variables are treated in a symmetric way, so that no distinction between inputs and outputs is required. This motivates the condition that also the prior...

Based on the theory of multiple statistical hypotheses testing, we elaborate likelihood-based simultaneous statistical inference methods in dynamic factor models (DFMs). To this end, we work up and extend the methodology of Geweke and Singleton (1981) by proving a multivariate central limit theorem for empirical Fourier transforms of the observable time series. In an asymptotic regime with obse...

The general purpose of a dynamic factor model (DFM) is to summarize large number time series into few common factors. In this paper we explore several DFMs on 80 granular, non-overlapping commercial property price indexes in the US, quarterly from 2001Q1 2017Q2. We examine nature and structure factors index forecasts that can be produced DFMs. consider specifications one four As major motivatio...