A comparative study of the Lasso-type and heuristic model selection methods

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.Model selection, Lasso, adaptive Lasso, elastic net, heuristic methods, genetic algorithms

Forecasting Russian Foreign Trade Comparative Advantages in the Context of a Potential WTO Accession

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.comparative advantage, competitive advantage, economy in transition, Balassa index, Lafay index.

Heuristic model selection for leading indicators in Russia and Germany

Business tendency survey indicators are widely recognized as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full–specified VAR models with subset models obtained using a Genetic Algorithm enabling ’holes’ in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for bothLeading indicators, business cycle forecasts, VAR, model selection, genetic algorithms.

Forecasting Russian Foreign Trade Comparative Advantages in the Context of a Potential WTO Accession

For the private and public sector in any particular country it is crucial to know, which industries may exhibit comparative advantages, that for some reasons are not realized. This can efficiently help all current and potential actors to improve their economic strategy both at the micro- and macroeconomic level. In this paper we propose an approach of forecasting comparative advantages dynamics in foreign trade. The instrument is based on relative price differences and is efficient for countries in the process of economic liberalization. An empirical analysis based on the example of Central and East European countries confirms a good performance in the sense of predictive power of this instrument. On the example of Russia, experiencing a period of economic liberalization and with the prospect to join the WTO agreements, we demonstrate which sectors are most likely to contain comparative advantages in the near future.comparative advantage, economy in transition, Balassa index, Lafay index

Heuristic model selection for leading indicators in Russia and Germany

Business tendency survey indicators are widely recognized as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full–specified VAR models with subset models obtained using a Genetic Algorithm enabling ’holes’ in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both countries revealing marked differences between Russia and Germany.Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms

Heuristic Optimization Methods for Dynamic Panel Data Model Selection. Application on the Russian Innovative Performance

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.Innovation, dynamic panel data, GMM, model selection, threshold accepting, genetic algorithms.

Evolution and recombination of topics in Technological Forecasting and Social Change

Unidad de excelencia María de Maeztu CEX2019-000940-MTechnological Forecasting and Social Change (TFSC) is one of the main outlets in the literature on technological change. To assist its editors and future contributors in understanding the evolution of the journal, we review studies published between 1970 and 2022 identifying 25 main themes ranging from scenario foresight and forecasting methods that dominated the journal agenda in the first decades through innovation diffusion and patent analysis that gained popularity in 2006-2019 to social interaction and financial markets which experienced momentum in the last couple of years. We find that studies concentrated on more recent topics like firm performance, financial markets and environmental regulation have been cited more frequently and were contributed more often by scientists from China compared to the US. Inspired by the fact that studies recombining two or more topics are more impactful in terms of citations, we construct a graph of topics, both for the overall sample of 6240 studies reviewed and three periods of TFSC existence corresponding to different editors-in-chief. Our results illustrate knowledge complementarities explored in the journal so far and may indicate directions for further research

Factor-Biased Technical Change and Specialization Patterns

We analyze the medium- and long-run effects of international integration of capital markets on specialization patterns of countries. For that purpose, we incorporate induced technical change into a Heckscher-Ohlin model with a continuum of final goods. This provides a comprehensive theory that explains the dynamics of comparative advantages based on differences in effective factor endowments. Our model constitutes an appropriate framework for understanding the changes in industrial structure of foreign trade observed, e.g., in the CEE countries over the last two decades. In addition, our approach provides a theoretical foundation for the empirical prospective comparative advantage index (Savin and Winker 2009) with new insights into the future dynamics of comparative advantages. Eventually, the model may serve as a basis to set development priorities in countries being in the period of transition.Factor-biased technical change, continuum of goods, comparative advantage, factor mobility, innovation, knowledge spillovers

Data-driven definitions of gazelle companies that rule out chance : application for Russia and Spain

Altres ajuts: acords transformatius de la UABUnidad de excelencia María de Maeztu CEX2019-000940-MThe phenomenon of fast-growing companies exhibiting sustained growth and creating disproportionally many new jobs, so-called "gazelles", has been widely analyzed in the literature. The criteria defining "gazelles", however, lack a consensus, while it cannot be ruled out that superior performance of these companies is just good luck. We use large firm-level datasets for Russia and Spain and conduct a Monte Carlo experiment with first-order Markov chains to derive a definition of "gazelle" companies and ensure that their existence cannot be explained by chance only. Our results demonstrate that the definitions of "gazelle" companies differ between the two countries warning against using same definition for different countries. We find that the "gazelles" account for about 1-2% of the companies in our datasets and are responsible for approximately 14% of employment growth in Russia and 9% in Spain. These companies are concentrated in economic sectors like retail trade, real estate and construction