Network modeling of high-dimensional time series in presence of unobserved latent variables is an important problem in macroeconomics and finance. In macroeconomic policy making and forecasting, it is often impossible to observe and incorporate all the relevant series in the analysis. Failure to include these variables often results in spurious connectivity among the observed time series in str...

We discuss dynamic network modeling for multivariate time series, exploiting dynamic variable selection and model structure uncertainty strategies based on the recently introduced concept of “latent thresholding.” This dynamic modeling concept addresses a critical and challenging problem in multivariate time series and dynamic modeling: that of inducing formal probabilistic structures that are ...

In this paper we consider alternative modeling strategies for specification of subset VAR models. We present four strategies and show that under certain conditions a testing procedure based on t-ratios is equivalent to eliminating sequentially lags that lead to the largest improvement in a prespecified model selection criterion. One finding from our Monte Carlo study is that differences between...

Article history: Received 10 May 2013 Received in revised form 2 September 2014 Accepted 2 September 2014 Available online 11 September 2014 The study investigates the relative performance of Value-at-Risk (VaR) models using daily share price index data from six different countries across Asia, Europe and the United States for a period of 10 years from January 01, 2000 toDecember 31, 2009. Them...

Numerical taxonomy characterization of Baccharis genus species was performed using ultraviolet-visible spectrophotometry. The aim was to present a more convenient, more practical, more economic and faster method based on chemometric methods and UV-vis absorbance to give the most information about species identity and discrimination, especially when their classification has been doubtful. Three ...

Functional Principal Component Analysis (FPCA) provides a powerful and natural way to model functional financial data sets (such as collections of time-indexed futures and interest rate yield curves). However, FPCA assumes each sample curve is drawn from an independent and identical distribution. This assumption is axiomatically inconsistent with financial data; rather, samples are often interl...

This chapter presents the mathematical framework to evaluate the sensitivity of a model forecast aspect to the input parameters of a nonlinear four-dimensional variational data assimilation system (4D-Var DAS): observations, prior state (background) estimate, and the error covariance specification. A fundamental relationship is established between the forecast sensitivity with respect to the in...

The authors study the effect of word-of-mouth (WOM) marketing on member growth at an Internet social networking site and compare it with traditional marketing vehicles. Because social network sites record the electronic invitations from existing members, outbound WOM can be precisely tracked. Along with traditional marketing, WOM can then be linked to the number of new members subsequently join...

The Service ORiented Computing EnviRonment (SORCER) targets service abstractions for transdisciplinary complexity with support for heterogeneous service-oriented (SO) computing. SORCER’s models are expressed in a top-down Var-oriented Modeling Language (VML) unified with programs in a bottoms-up Exertion-Oriented Language (EOL). In this paper the introduction to mogramming (modeling and program...

Long memory (long-term dependence) seems to be as widespread in financial time series as in nature. Inspired by the long memory property, Multi-fractal processes have recently been introduced as a new tool for modeling the stylized facts in financial time series. In this paper, we attempt to construct a bivariate multi-fractal model, and implement its estimation via both GMM and likelihood appr...