This paper analyzes the dynamics of prices and wages using a limited information approach to estimation. I estimate a two-equation model for the determination of prices and wages derived from an optimization-based dynamic model in which both goods and labor markets are monopolistically...

In this article, we specify the different approaches followed by the economists and the financial economists in order to use chaos theory. We explain the main difference using this theory with other research domains like the mathematics and the physics. Finally, we present tools necessary for...

This paper focuses on the use of dynamical chaotic systems in Economics and Finance. In these fields, researchers employ different methods from those taken by mathematicians and physicists. We discuss this point. Then, we present statistical tools and problems which are innovative and can be...

We develop a new dynamic general equilibrium model to explain firm entry, exit, and relocation decisions in an urban economy with multiple locations and agglomeration externalities. We characterize the stationary distribution of firms that arises in equilibrium. We estimate the parameters of the...

We investigate some statistical properties of the new k-factor Gegenbauer process with heteroscedastic noises One of the goals of the paper is to give tools which permit to use this model to explain the behaviour of certain data sets in finance and in macroeconomics. Monte Carlo experiments are...

Using Archimedean copulas, we investigate the dependence structure existing between several series of financial assets log-returns that come from different markets. These series are considered as components of a portfolio and they are investigated on a long period including high shocks. To...

There is a continuing need to use recent and consistent multisectoral economic data to support policy analysis and the development of economywide models. Updating and estimating input-output tables and Social Accounting Matrices (SAMs) for a recent year is a difficult and a challenging problem....

Some crucial time series of market data, such as electricity spot prices, exhibit long memory, in the sense of slowly-decaying correlations combined with heteroscedasticity. To e able to model such a behaviour, we consider the k-factor GIGARCH process and we propose two methods to address the...

Some crucial time series of market data, such as electricity spot prices, exhibit long memory, in the sense of slowly-decaying correlations combined with heteroscedasticity. To e able to model such a behaviour, we consider the k-factor GIGARCH process and we propose two methods to address the...

The estimation of large vector autoregressions with stochastic volatility using standard methods is computationally very demanding. In this paper we propose to model conditional volatilities as driven by a single common unobserved factor. This is justified by the observation that the pattern of...