It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that...

The extraction of a common signal from a group of time series is generally obtained using variables recorded with the same frequency or transformed to have the same frequency (monthly, quarterly, etc.). The statistical literature has not paid a great deal of attention to this topic. In this...

Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential <em>a priori</em> partitioning of the data into an...

This is an exploratory study that attempts to identify and provide empirical evidence on the possible determinants of the market capitalisation of the Harare Stock Exchange (HSE) with the view of understanding the development prospects of the HSE and other similar markets. The study used...

This survey reviews diverse academic production on compositional dynamic series analysis. Although time dimension of compositional series has been little investigated, this kind of data structure is widely available and utilized in social sciences research. This way, a review of the...

This paper discusses two alternative two-part models for fractional response variables that are defined as ratios of integers. The first two-part model assumes a Binomial distribution and known group size. It nests the one-part fractional response model proposed by Papke and Wooldridge (1996)...

Although economic processes and systems are in general simple in nature, the underlying dynamics are complicated and seldom understood. Recognizing this, in this paper we use a nonstationary-conditional Markov process model of observed aggregate data to learn about and recover causal influence...

This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that...

The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random...

A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...