This paper investigates output convergence for the G7 countries using multivariate time series techniques. We consider both the null hypotheses of no convergence and convergence. It is shown that inferences on output convergence depend on which one of the two null hypotheses is considered....

In this paper we specify a multi-factor long-memory process that enables us to estimate the fractional differencing parameters at each frequency separately, and adopt this framework to model quarterly prices in three European countries (France, Italy and the UK). The empirical results suggest...

This paper introduces a new modelling approach that incorporates nonlinear, exponential deterministic terms into a fractional integration model. The proposed model is based on a specific version of Robinson's (1994) tests and is more general that standard time series models, which only allow for...

Despite the fact that many aggregates are nonlinear functions and the aggregation weights of many macroeconomic aggregates are time-varying, much of the literature on forecasting aggregates considers the case of linear aggregates with fixed, time-invariant aggregation weights. In this study a...

This paper investigates the degree of persistence of market fear. Specifically, two different long-memory approaches (R/S analysis with the Hurst exponent method and fractional integration) are used to analyse persistence of the VIX index over the sample period 2004-2016, as well as some...

This paper develops a new open-economy endogenous growth model where technology diffusion allows for a stable and non-degenerate world income distribution. In accordance with the empirical literature, I find that country characteristics such as the social infrastructure, the degree of openness,...

We develop a two-sector, two-country model where trade is driven by technological differences. Each country is populated by large number of heterogeneous workers distinguished by their level of skills. Given that one country has a technological advantage in the skilled intensive good when we...