Optimal control of nonlinear dynamic econometric models: An algorithm and an application

Blueschke-Nikolaeva, V.; Blueschke, Dmitri; Neck, Reinhard

doi:10.1016/j.csda.2010.10.030

Cited by 20 publications

(20 citation statements)

References 17 publications

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“…4 To identify an optimal policy mix, we apply an exercise from optimal control literature (see, e.g., Blueschke-Nikolaeva et al, 2012), where a set of controls is optimised to achieve policy targets as close as possible. Since the search space of possible solutions is infinite (due to the continuous nature of the problem) and not necessarily 'well-behaved' (with non-linearities and multiple local optima), a Differential Evolution algorithm is used.…”

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confidence: 99%

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Optimal policy identification: Insights from the German electricity market

Herrmann

Savin

2017

Technological Forecasting and Social Change

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The diffusion of renewable electricity generating technologies is widely considered as crucial for establishing a sustainable energy system in the future. However, the required transition is unlikely to be achieved by market forces alone. For this reason, many countries implement various policy instruments to support this process, also by re-distributing related costs among all electricity consumers. This paper presents a novel history-friendly agent-based study aiming to explore the efficiency of different mixes of policy instruments by means of a Differential Evolution algorithm. Special emphasis of the model is devoted to the possibility of small scale renewable electricity generation, but also to the storage of this electricity using small scale facilities being actively developed over the last decade. Both combined pose an important instrument for electricity consumers to achieve partial or full autarky from the electricity grid, particularly after accounting for decreasing costs and increasing efficiency of both due to continuous innovation. Among other things, we find that the historical policy mix of Germany introduced too strong and inflexible demand-side instruments (like feed-in tariff) too early, thereby creating strong path-dependency for future policy makers and reducing their ability to react to technological but also economic shocks without further increases of the budget.

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confidence: 99%

“…To identify an optimal policy mix we apply an exercise from the optimal control literature (see, e.g., Blueschke-Nikolaeva et al, 2012), where a set of controls is optimised to achieve the states as close as possible to policy targets. An important difference is that since we fix the overall budget of policy interventions.…”

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confidence: 99%

Optimal policy identification: Insights from the German electricity market

Herrmann

Savin

2017

Technological Forecasting and Social Change

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“…In our experiments we use the last version of the OPTCON algorithm, which is called OPTCON2. We skip the presentation of the OPTCON algorithm, which can be found in more detail in Matulka and Neck (1992) and Blueschke-Nikolaeva et al (2012), and discuss only those issues relevant for this paper.…”

Section: Optconmentioning

confidence: 99%

“…Chow (1975), Chow (1981) and Kendrick (1981)). One of the algorithms which deals with these types of problems is OPTCON described in Matulka and Neck (1992) and Blueschke-Nikolaeva et al (2012). However, the OPTCON algorithm still relies on the LQ optimization technique and, therefore, has some limitations typical for this framework.…”

Section: Introductionmentioning

confidence: 99%

Solving Nonlinear Stochastic Optimal Control Problems Using Evolutionary Heuristic Optimization

Savin

Blueschke

2013

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Solving nonlinear stochastic optimal control problems using evolutionary heuristic optimization * I. Savin † ‡ and D. Blueschke § Abstract 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. Terms of use: Documents in

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“…We start with the results for the SLOVNL model, a small nonlinear econometric model of the Slovenian economy, which consists of 8 equations and includes 8 state variables, 4 exogenous non-controlled variables, 3 control variables and 16 unknown (estimated) parameters (see Appendix 6.1 and Blueschke-Nikolaeva et al (2011) 9 Thus, we find evidence that DE can even beat standard optimum control strategies for complex econometric models compensating by this the higher computational cost required to obtain a solution. …”

Section: Comparison Of Optcon and Dementioning

confidence: 99%

New Insights into Optimal Control of Nonlinear Dynamic Econometric Models: Application of a Heuristic Approach

2012

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractOptimal control of dynamic econometric models has a wide variety of applications including economic policy relevant issues. There are several algorithms extending the basic case of a linear-quadratic optimization and taking nonlinearity and stochastics into account, but being still limited in a variety of ways, e.g., symmetry of the objective function and identical data frequencies of control variables. To overcome these problems, an alternative approach based on heuristics is suggested. To this end, we apply a 'classical' algorithm (OPTCON) and a heuristic approach (Differential Evolution) to three different econometric models and compare their performance. In this paper we consider scenarios of symmetric and asymmetric quadratic objective functions. Results provide a strong support for the heuristic approach encouraging its further application to optimum control problems.

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Optimal control of nonlinear dynamic econometric models: An algorithm and an application

Cited by 20 publications

References 17 publications

Optimal policy identification: Insights from the German electricity market

Optimal policy identification: Insights from the German electricity market

Solving Nonlinear Stochastic Optimal Control Problems Using Evolutionary Heuristic Optimization

New Insights into Optimal Control of Nonlinear Dynamic Econometric Models: Application of a Heuristic Approach

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