In this paper, a dynamic factor model is proposed to extract the dynamic factors from time series data. In order to deal with the problem of scaling, the cross-correlation matrices (CCM) are first employed to cluster the time series data. Then, the dynamic factors are extracted using the revised independent component analysis (ICA). In addition, a numerical study is used to demonstrate the prop...

By means of so-called virtual or shadow prices, short-run factor demands, short-run marginal costs, etc. can be derived from any long-run cost function. The traditional approach (short-run/restricted/conditional/variable cost functions) is criticized, and it is also shown that technological change, scale e!ects, etc. can be added to any cost function by means of disembodied factor-augmenting e$...

 Outward-oriented economies seem to grow faster than inward-looking ones. Does the literature on convergence have anything to say on this? In the dynamic Heckscher-Ohlin-Samuelson model, with factor-price equalization, there is no convergence of incomes. This is because with identical preferences and return to capital, irrespective of initial levels the growth rates of consumption are the same....

The genesis of human movement involves a complex series of events that depend on the integrated physiology of the neural, muscular, skeletal, metabolic, respiratory and cardiovascular systems. Since each of us has a unique set of physical attributes, such as muscle strength, aerobic capacity, and skeletal structure, our movement patterns serve as another set of “fingerprints”. For instance, we ...

This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden variables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds...

User preferences change over time and capturing such changes is essential for developing accurate recommender systems. Despite its importance, only a few works in collaborative filtering have addressed this issue. In this paper, we consider evolving preferences and we model user dynamics by introducing and learning a transition matrix for each user’s latent vectors between consecutive time wind...

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...