Despite strong promotion of green growth by policymakers and international institutions, there is mounting criticism concerning the compatibility of continued economic growth with sustainability goals. Our survey of climate policy researchers reveals widespread scepticism in high-income countries, supporting the notion that as national income rises, environmental goals prevail over economic growth. This finding underscores the importance of considering alternative post-growth perspectives, including agrowth and degrowth strategies, to cultivate a more comprehensive discourse on sustainable development strategies.

Identifying factors which stimulate regional growth and international competitiveness and using them for forecasting are the aims of this book. The author proposes the use of heuristic optimization techniques, Monte Carlo simulation experiments and Lasso-type estimators to avoid bias or misleading findings.

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.

Innovations, be they radical new products or technology improvements are widely recognized as a key factor of economic growth. To identify the factors triggering innovative activities is a main concern for economic theory and empirical analysis. As the number of hypotheses is large, the process of model selection becomes a crucial part of the empirical implementation. The problem is complicated by the fact that unobserved heterogeneity and possible endogeneity of regressors have to be taken into account. A new efficient solution to this problem is suggested, applying optimization heuristics, which exploits the inherent discrete nature of the problem. The model selection is based on information criteria and the Sargan test of overidentifying restrictions. The method is applied to Russian regional data within the framework of a log-linear dynamic panel data model. To illustrate the performance of the method, we also report the results of Monte-Carlo simulations.

This paper proposes a new approach of forecasting “prospective" comparative advantages based on relative prices differences between countries in the context of economic liberalization. An empirical analysis based on the example of Central and East European countries that have already passed the transition period from specialization mainly in natural resource- and labor-intensive goods to "high-tech" goods confirms a significant influence of our “prospective" advantages on comparative advantages dynamics. Using this method we identify a set of industries in Russia that seem to be most promising for formation of comparative advantages in the context of its economic liberalization and joining the WTO agreements. These industries include high and medium technological industries like machinery building, pharmaceutical products, railway transport, electronic and medical equipment.

This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular, inconsistent results are obtained for pairwise strongly correlated predictors. An alternative to the Lasso is constituted by model selection based on information criteria (IC), which remains consistent in the situation mentioned. However, these criteria are hard to optimize due to a discrete search space. To overcome this problem, an optimization heuristic (Genetic Algorithm) is applied. Monte-Carlo simulation results are reported to illustrate the performance of the methods.