A combinatorical problem is said to be of high computational complexity, if it can be shown that every efficient algorithm needs a high amount of resources as measured in Computing time or storage capacity. This paper will (1) introduce some basic concepts of mathematical complexity theory; (2)...

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,...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

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,...

A combinatorical problem is said to be of high computational complexity, if it can be shown that every efficient algorithm needs a high amount of resources as measured in Computing time or storage capacity. This paper will (1) introduce some basic concepts of mathematical complexity theory; (2)...

Aggregation may be harmful but cannot always be avoided in the analysis of complex econometric models. It should be carried out intelligently by choosing ein aggregative model optimally for modes of aggregation speeified in advance, i.e. minimizing the bias introduced by aggregation and...

A widely used method in the analysis of complex econometric models is to replace the "true model" by an aggregative one in which the variables are grouped and replaced by sums or weighted averages of the variables in each group. The analysis of the problem of choosing an aggregative model...