Suchergebnisse

Recently population employment and unemployment issues have become especially urgent. Developing Lithuanian work market is at the junction of two processes. On the one hand there occurred the processes conditioned by economy transfer from the planned system to the market system which led to the structural changes of separate economy branches, diffusion of technical and technological advance, change of production scale and range, competition in domestic and foreign markets, etc. On the other hand the integration into the international economic system has come to the fire. These developments have caused quality and quantity parameter changes in complex work resources, the latter determining the causes of irregular population. New ways of the solution of these problems have been presented by the integration into the European Union promoting the possibility to use the experience of other countries in modelling the development adequate to the country's social and economic growth. Consequently it is purposeful to improve models of population employment estimation, which would let to identify factors determining changing differences in the work market.

1. Introduction -- 1.1 References -- 2. Some Remarks on Econometric Model Building -- 2.1 References -- 3. Prediction-Oriented Estimation Methods for the Canonical Disequilibrium Model -- 3.1 References -- 4. Investment in Residential Construction -- 4.1 Introductory Remarks -- 4.2 Demand for Investment in Residential Construction -- 4.3 Supply of Investment in Residential Construction -- 4.4 Data and Empirical Results -- 4.5 Description of Variables -- 4.6 References -- 5. The Money Stock -- 5.1 Introductory Remarks -- 5.2 Demand for Money -- 5.3 Supply of Money -- 5.4 Disequilibrium Estimation -- 5.5 Description of Variable -- 5.6 References -- 6. The Aggregate Labor Market -- 6.1 Introductory Remarks -- 6.2 Supply of Labor -- 6.3 Demand for Labor -- 6.4 Disequilibrium Estimation -- 6.5 Description of Variables -- 6.6 References.

Introduction to DSGE modeling and Bayesian inference. DSGE modeling -- Turning a DSGE model into a Bayesian model -- A crash course in Bayesian inference -- Estimation of linearized DSGE models. Metropolis-Hastings algorithms for DSGE models -- Sequential Monte Carlo methods -- Three applications -- Estimation of nonlinear DSGE models. From linear to nonlinear DSGE models -- Particle filters -- Combining particle filters with MH samplers -- Combining particle filters with SMC samplers -- Appendix. Model descriptions -- Data sources.

17 pages, 9 figures, 4 tables. ; Background: Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power. Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability. Results: We successfully evaluate this novel approach considering four difficult case studies regarding the calibration of well-known biological oscillators (Goodwin, FitzHugh–Nagumo, Repressilator and a metabolic oscillator). In contrast, we show how local gradient-based approaches, even if used in multi-start fashion, are unable to avoid the above-mentioned pitfalls. Conclusions: Our approach results in more efficient estimations (thanks to the bounding strategy) which are able to escape convergence to local optima (thanks to the global optimisation approach). Further, the use of regularisation allows us to avoid overfitting, resulting in more generalisable calibrated models (i.e. models with greater predictive power). ; This research has received funding from the European Union's Horizon 2020 research and innovation program. ; Peer reviewed

[Background] Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power. Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability. ; [Results] We successfully evaluate this novel approach considering four difficult case studies regarding the calibration of well-known biological oscillators (Goodwin, FitzHugh–Nagumo, Repressilator and a metabolic oscillator). In contrast, we show how local gradient-based approaches, even if used in multi-start fashion, are unable to avoid the above-mentioned pitfalls. ; [Conclusions] Our approach results in more efficient estimations (thanks to the bounding strategy) which are able to escape convergence to local optima (thanks to the global optimisation approach). Further, the use of regularisation allows us to avoid overfitting, resulting in more generalisable calibrated models (i.e. models with greater predictive power). ; This research has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 675585 (MSCA ITN "SyMBioSys") and from the Spanish MINECO/FEDER project SYNBIOCONTROL (DPI2017-82896-C2-2-R). ; Peer reviewed