Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep...

In this paper we have assessed an influence of the NYSE Stock Exchange indexes (DJIA and NASDAQ) and European Stock indexes (DAX and FTSE) on the Warsaw Stock Exchange index WIG within a framework of a GARCH model. By applying a procedure of checking predictive quality of econometric models as...

We study 30 vintages of FRB/US, the principal macro model used by the Federal Reserve Board staff for forecasting and policy analysis. To do this, we exploit archives of the model code, coefficients, baseline databases and stochastic shock sets stored after each FOMC meeting from the model’s...

This paper examines the learning dynamics of boundedly rational agents, who are asked to voluntarily contribute to a discrete public good. In an incomplete information setting, we discuss contribution games and subscription games, the latter including a money-back guarantee in case of provision...

This paper illustrates the representation of induced technological change in the multi-regional, multi-sectoral integrated assessment model WIAGEM. The main aim of this paper is to investigate quantitatively economic impacts of climate policy measures due to induced technological changes that...

In this note, a class of nonlinear dynamic models under rational expectations is studied. A particular solution is found using a model reference adaptive technique via an extended Kalman filtering algorithm, for which initial conditions knowledge only is required.