The paper provides an overview of probabilistic forecasting and discusses a theoretical framework for evaluation of probabilistic forecasts which is based on proper scoring rules and moments. An artificial example of predicting second-order autoregression and an example of predicting the RTSI stock index are used as illustrations.
In this paper I examine the Okun-Friedman hypothesis of the link between inflation and inflation uncertainty using historical international data on the monthly CPI. An indicator of inflation uncertainty at the two-years-ahead horizon is derived from a time-series model of inflation with time-varying parameters by means of Monte Carlo simulations. This indicator is compared to other uncertainty measures, with the short forecast horizon and based on simpler GARCH-type models. The analysis convincingly demonstrates that both the longer horizon and changing parameters are important for the regularity. The evidence obtained strongly supports the Okun-Friedman hypothesis both in the time dimension for most countries and across countries.
The paper describes the current state of agent-based modeling of geographical space and spatial economic systems. We explore reasons why this approach to modeling spatial phenomena is of particular interest. Agent-based models (ABMs) allow accounting for agents’ spatial heterogeneity, the existing structure of the space, locality of interactions between agents. A survey of approaches to introducing space into the models and examples of the existing spatial models is presented. There is a great variety of spatial ABMs, but they relate predominantly to the local and city level, rather than to the economy as a whole. Spatial ABMs at the level of large regions and countries are not yet sufficiently developed, but have good prospects in the future. With increasing availability of geodata and technological development in general the number of applications and coverage of spatial ABM will grow
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