The Stochastics of Threshold Accepting: Analysis of an Application to the Uniform Design Problem

Threshold Accepting (TA) is a powerful optimization heuristic from the class of stochastic local search algorithms. It has been applied successfully to different optimization problems in statistics and econometrics, including the uniform design problem. Using the latter application as example, the stochastic properties of a TA implementation are analyzed. We provide a formal framework for the analysis of optimization heuristics like TA, which can be used to estimate lower bounds and to derive convergence results. It is also helpful for tuning real applications. Based on this framework, empirical results are presented for the uniform design problem. In particular, for two problem instances, the rate of convergence of the algorithm is estimated to be of the order of a power of -0.3 to -0.7 of the number of iterations. --Heuristic optimization,Threshold Accepting,Stochastic analysis of heuristics

Robust Portfolio Optimization with a Hybrid Heuristic Algorithm

Estimation errors in both the expected returns and the covariance matrix hamper the constructing of reliable portfolios within the Markowitz framework. Robust techniques that incorporate the uncertainty about the unknown parameters are suggested in the literature. We propose a modification as well as an extension of such a technique and compare both with another robust approach. In order to eliminate oversimplifications of Markowitz’ portfolio theory, we generalize the optimization framework to better emulate a more realistic investment environment. Because the adjusted optimization problem is no longer solvable with standard algorithms, we employ a hybrid heuristic to tackle this problem. Our empirical analysis is conducted with a moving time window for returns of the German stock index DAX100. The results of all three robust approaches yield more stable portfolio compositions than those of the original Markowitz framework. Moreover, the out-of-sample risk of the robust approaches is lower and less volatile while their returns are not necessarily smaller.Hybrid heuristic algorithm, Markowitz, Robust optimization, Uncertainty sets.

The Romanian Economy in Transition: Developments and Future Prospects

We address the macroeconomic developments experienced by Romania in a decade of transition towards a market driven society. We focus on a descriptive analysis with the intent of offering a clear and broad picture of the main aspects distinguishing the Romanian economy. The paper also discusses perspectives for future growth and puts forward the need to develop within the prospects of European Union integration a strategy on a more sustainable basis.transition economies, Romania, macroeconomic trends

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.

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.

The convergence of optimization based estimators : theory and application to a GARCH-model

The convergence of estimators, e.g. maximum likelihood estimators, for increasing sample size is well understood in many cases. However, even when the rate of convergence of the estimator is known, practical application is hampered by the fact, that the estimator cannot always be obtained at tenable computational cost. This paper combines the analysis of convergence of the estimator itself with the analysis of the convergence of stochastic optimization algorithms, e.g. threshold accepting, to the theoretical estimator. We discuss the joint convergence of estimator and algorithm in a formal framework. An application to a GARCH-model demonstrates the approach in practice by estimating actual rates of convergence through a large scale simulation study. Despite of the additional stochastic component introduced by the use of an optimization heuristic, the overall quality of the estimates turns out to be superior compared to conventional approaches. --GARCH,Threshold Accepting,Optimization Heuristics,Convergence

The Economics of Crime: Investigating the Drugs-Crime Channel

The rising trends both in drug addiction and crime rates are of major public concern in Germany. Surprisingly, the economic theory of crime seems to ignore the drugs-crime nexus, whereas the criminological literature considers illicit drug use a main reason of criminal activities. This paper provides an econometric assessment of the drugs-crime channel within a Becker-Ehrlich model of crime supply. We analyse three different channels from drug abuse to crime: system-related, economic-related and pharmacological effects. Estimation with panel data from the German states allows us to take into account further factors that might influence both drug abuse and crime. The results indicate that drug offences have a significant impact, in particular on property crimes. We attribute this to a strong economic-related channel of drug abuse on crime.

Hedonic regression for digital cameras in Germany

Standard measures of consumer price inflation are based on a bundle of representative goods. It is well known that this approach might overstate inflation for new products and products with fast increasing quality. For this reason, hedonic adjustment methods have been proposed and introduced in official statistics for some products like personal computers. In this contribution, we consider the application of a hedonic regression to digital cameras, which have been introduced in the product bundle of the German consumer price index in 2003 – so far without hedonic quality adjustment. We present first results on hedonic price measurement for digital cameras in Germany for the time period 1999 to 2004. The results are based on data sampled from public interest journals and advertisements. --Hedonic regression,hedonic price index,quality adjustment

Indirect Estimation of the Parameters of Agent Based Models of Financial Markets

Agent based models take into account limited rational behaviour of individuals acting on financial markets. Explicit simulation of this behaviour and the resulting interac-tion of individuals provide a description of aggregate financial market time series. Al-though the outcomes of such simulations often exhibit similarities with real financial market time series, methods for explicit validation are required. This paper proposes validation using simulation based indirect estimation. It uses typical characteristic moments of financial market data to assess the similarity of simulation outcomes. Fur-thermore, the parameters of the agent based models can be estimated by maximizing this similarity. The paper presents details of this estimation approach and first results for the US–$/DM exchange rate.Agent Based Models; Indirect Estimation; Validation

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.