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This paper describes the SLOPOL10 model, a quarterly macroeconometric model of the Slovenian economy to be used for forecasting macroeconomic development and simulating alternative policy measures. The model is of the Cowles Commission type and is estimated using the cointegration approach, thus combining the long-run equilibrium and the short-run adjustment mechanism. It contains behavioural equations and identities for the goods market, the labour market, the foreign exchange market, the money market, and the government sector. Estimation of behavioural equations for Slovenian aggregates is based on data starting in 1995. The model combines Keynesian and neoclassical elements. The Keynesian elements determine the short and medium-run solutions in the sense that the model is demand-driven and persistent disequilibria in the goods and labour markets are possible. The supply side incorporates neoclassical features. Static and dynamic ex-post simulations show that the model can reasonably reproduce past development and is therefore suited for prediction and policy evaluation, especially for fiscal policy design and optimal control experiments. DOI: 10.15458/85451.62

We propose a meta-heuristic approach for solving nonlinear dynamic tracking games. In contrast to more "traditional" methods based on linear-quadratic (LQ) techniques, this derivative-free method is very flexible (e.g. to introduce inequality constraints). The meta-heuristic is applied to a three-player dynamic game and tested versus derivative-dependent method in approximating Nash solution in different game specifications. We demonstrate the superiority of the proposed approach in identifying Nash equilibria, where LQ methods are not applicable.

The linear-quadratic (LQ) optimization is a close to standard technique in the optimal control framework. LQ is very well researched and there are many extensions for more sophisticated scenarios like nonlinear models. Usually, the quadratic objective function is taken as a prerequisite for calculating derivative-based solutions of optimal control problems. However, it is not clear whether this framework is so universal as it is considered. In particular, we address the question on whether the objective function specification and the corresponding penalties applied, are well suited in case of a large exogenous shock an economy can experience because of, e.g., the European debt crisis. While one can still efficiently minimize quadratic deviations in state and control variables around policy targets, the economy itself has to go through a period of turbulence with economic indicators, such as unemployment, inflation or public debt, changing considerably over time. In this study we test four alternative designs of the objective function: a least median of squares based approach, absolute deviations, cubic and quartic objective functions. The analysis is performed based on a small-scale model of the Austrian economy and finds that there is a certain trade-off between quickly finding optimal solution using the LQ technique (reaching defined policy targets) and accounting for alternative objectives, such as limiting volatility in the economic performance.

Policy makers constantly face optimal control problems: what controls allow to achieve certain targets in, e.g., GDP growth or inflation? Conventionally this is done by applying certain linear-quadratic optimization algorithms to dynamic econometric models. Several algorithms extend this baseline framework to nonlinear stochastic problems. However, those algorithms are limited in a variety of ways including, most importantly, restriction to local best solutions only and the symmetry of objective function. In Blueschke et al. (2013a) a new flexible optimization method based on Differential Evolution is suggested. It allows to lift these limitations and achieve better approximations of the policy targets, but is designed to deterministic problems only. This study extends the methodology by dealing with stochastic problems in two different ways: applying extreme event analysis and by minimizing the median objective value. Thus, this research is aimed to broaden the range of decision support information used by policy makers in choosing optimal strategy under much more realistic conditions.