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- W2479894413 abstract "Parameter estimation (inverse) problems are ubiquitous in many fields, including spatial econometrics. Global optimization can provide good parameter estimates for many such problems for which traditional, analytic estimation methods fail, or that are otherwise intractable. Stochastic global methods inspired by natural processes have recently gained popularity for difficult optimization problems characterized by imprecise measurements or local optima. In this chapter, one such approach, particle swarm optimization (PSO), is used to estimate parameters of the time series cross-sectional spatiotemporal autoregressive model, a particulary difficult and computationally intensive problem arising in spatial econometrics. Preliminary results are promising, and suggest that stochastic global approaches, and global optimization in general, can successfully address some of these intractable problems." @default.
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- W2479894413 date "2015-01-01" @default.
- W2479894413 modified "2023-10-14" @default.
- W2479894413 title "Approximations to Intractable Spatial Econometric Models and Their Solutions Through Global Optimization" @default.
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- W2479894413 doi "https://doi.org/10.1007/978-3-319-12307-3_69" @default.
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