By Amos Golan (auth.), Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta, Komsan Suriya (eds.)

ISBN-10: 3642354424

ISBN-13: 9783642354427

ISBN-10: 3642354432

ISBN-13: 9783642354434

Unlike doubtful dynamical platforms in actual sciences the place versions for prediction are a little bit given to us by means of actual legislation, doubtful dynamical platforms in economics desire statistical versions. during this context, modeling and optimization floor as easy materials for fruitful purposes. This quantity concentrates at the present method of copulas and greatest entropy optimization.

This quantity includes major study shows on the 6th foreign convention of the Thailand Econometrics Society held on the college of Economics, Chiang Mai collage, Thailand, in the course of January 10-11, 2013. It includes keynote addresses, theoretical and utilized contributions. those contributions to Econometrics are a bit of situated round the subject matter of Copulas and greatest Entropy Econometrics. the tactic of copulas is utilized to various fiscal difficulties the place multivariate version development and correlation research are wanted. As for the paintings of selecting copulas in useful difficulties, the primary of extreme entropy surfaces as a possible approach to achieve this. The cutting-edge of utmost Entropy Econometrics is gifted within the first keynote handle, whereas the second one keynote deal with focusses on checking out stationarity in monetary time sequence data.

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The KPSS test statistic is then given by k T 1 KPSS = ∑ (ω T )2 k=1 2 ∑ (yt − yT ) , t=1 and, under the null hypothesis of level stationarity, 1 d KPSS =⇒ 0 κ (α )2 d α , d where =⇒ indicates convergence in distribution and κ (α ) is the standard Brownian bridge. That is, κ (α ) := W (α ) − α W (1), where W (α ) is the Wiener process. The critical values can be found in KPSS (1992). In a recent paper, de Jong et al. (2007) proposed a robust version of the KPSS test based on the following empirical process: IT (r) := 1 √ σ T Tr ∑ sign yt − mT , t=1 T , σ 2 is a nonparametric consistent estimawhere mT is the sample median of {yt }t=1 tor of the long-run variance, 1 σ 2 = lim e √ T →∞ T T 2 ∑ sign yt − mT t=1 and sign x is the sign of x: 1 if x > 0, −1 if x < 0 and 0 if x = 0.

Testing Covariance Stationarity. Econometric Reviews 26(6), 643–667 (2007) Part II Fundamental Theory Statistical Inference from Ill-known Data Using Belief Functions Thierry Denœux Abstract. As a general formalism for uncertain reasoning, the theory of belief functions extends the logical and probabilistic approaches to uncertainty: a belief function (or a completely monotone Choquet capacity) can be seen both as a non additive measure and as a generalized set. , imprecise and/or partially reliable data.

Xn with respect to m (see [25, page 149]), we can write: n pl(x) = ∏ pli (xi ), ∀x = (x1 , . . , xn ) ∈ ΩX , (21) i=1 where pli is the contour function corresponding to the mass function mi obtained by marginalizing m on ΩXi . Statistical Inference from Ill-known Data Using Belief Functions 45 We can remark here that the two assumptions above are totally unrelated as they are of different natures: stochastic independence of the random variables Xi is an objective property of the random data generating process, whereas cognitive independence pertains to our state of knowledge about the unknown realization x of X.

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Uncertainty Analysis in Econometrics with Applications: Proceedings of the Sixth International Conference of the Thailand Econometric Society TES'2013 by Amos Golan (auth.), Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta, Komsan Suriya (eds.)


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