Abstract The linear multiregression dynamic model (LMDM) is a Bayesian dynamic model which preserves any conditional independence and causal structure across a multivariate time series. The conditional independence structure is used to model the multivariate series by separate (conditional) univariate dynamic linear models, where each series has contemporaneous variables as regressors in its mo...

The linear multiregression dynamic model (LMDM) is a Bayesian dynamic model which preserves any conditional independence and causal structure across a multivariate time series. The conditional independence structure is used to model the multivariate series by separate (conditional) univariate dynamic linear models, where each series has contemporaneous variables as regressors in its model. Calc...

in this study, liquid-liquid extraction process in a kuhni extraction column was modeled and simulated. a non-equilibrium dynamic model was developed for modeling liquid-liquid extraction processes based on a rate-based model. the model equations are inclusive of partial and ordinary differential equations which were discretized in column height direction. the population balance model was used ...

Infrastructure investment is essential for economic development both developed and developing economies. We analyze the short-term return behavior portfolio characteristics of global, regional, selected Asian countries’ infrastructure indexes during pandemic over sample period 3 July 2018 to 1 2021. According multivariate Glosten, Jagannathan, Runkle (GJR) Generalized Autoregressive Conditional...

Consider the following causal explanation: The ball went through the goal because the defender didn’t block it. There are at least two problems with citing omissions as causal explanations. First, how do we choose the relevant candidate omission (e.g. why the defender and not the goalkeeper). Second, how do we determine what would have happened in the relevant counterfactual situation (i.e. may...

The major obstacle to extensive use of cokriging for integration of multiple data types in estimation is the requirement of modeling a positive definite covariance matrix of size K by K including up toK different terms if K coregionalized variables are considered. In practice, this matrix is modeled using a linear model of coregionalization (LMC); each covariance is modeled by a different linea...

This paper models the return series of USD/CNY exchange rate by considering the conditional mean and conditional volatility simultaneously. An index type functional-coefficient model is adopted to model the conditional mean part and a GARCH type model with a policy dummy variable is applied to the conditional volatility model. We show that the government policy indeed has an impact on the excha...

To investigate the interdependence between Indian onion markets in terms of price volatility, present study was conducted four different vital India, viz. Mumbai, Nashik, Delhi and Bengaluru. The long term monthly data, from March, 2003 to September, 2015 collected website agmarknet.nic.in. We have employed VEC-MGARCH model estimate mean volatility spillover simultaneously among also examined n...

Recent studies in international finance have shown that correlation of international equity returns increases during volatile periods. However, correlation should be used with great care. For example, assuming a multivariate normal distribution with constant correlation, conditional correlation during volatile periods (large absolute returns) is higher than conditional correlation during tranqu...