Bivariate integer-autoregressive process with an application to mutual fund flows

TL;DR: This paper proposes a Taylor's expansion algorithm for these predictive distributions, which is both exact and fast and demonstrates its advantages with respect to existing methods in terms of the computational gain and/or precision.

TL;DR: In this paper , a first-order bivariate random coefficient integer-valued autoregressive (BRCINAR) model and the inferential procedures of this model, such as the parameter estimation and parameter change test are examined.

TL;DR: In this paper , an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) scheme is proposed to model multivariate time series of counts using the integer-value generalized autorescales (INGAR) scheme.

TL;DR: In this paper , a flexible bivariate conditional Poisson (BCP) INteger-valued Generalized AutoRegressive Conditional Heteroscedastic (INGARCH) model is proposed for correlated count time series data.

TL;DR: In this article, the empirical relationship between technological innovations, market share and stock market value was examined and new developments in the estimation of dynamic count data models were used to control for unobserved firm specific heterogeneity.

TL;DR: In this paper, simple models are presented for use in the modeling and generation of sequences of dependent discrete random variables, which are essentially Markov Chains, but are structurally autoregressions, and so depend on only a few parameters.

TL;DR: The authors show how the possibility of ties that results from atoms in the probability distribution invalidates various familiar relations that lie at the root of copula theory in the continuous case.

TL;DR: In this article, the existence and ergodic properties of the integer-valued autoregressive model with lag p dependence were proved and it was shown that the correlation structure of the INAR model is similar to that of the continuous-valued auto-regression process.