Atjaunināt sīkdatņu piekrišanu

E-grāmata: Asymmetric Kernel Smoothing: Theory and Applications in Economics and Finance

  • Formāts: PDF+DRM
  • Sērija : SpringerBriefs in Statistics
  • Izdošanas datums: 08-Jun-2018
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811054662
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 52,34 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Asymmetric Kernel Smoothing: Theory and Applications in Economics and FinancePalielināt
  • Formāts: PDF+DRM
  • Sērija : SpringerBriefs in Statistics
  • Izdošanas datums: 08-Jun-2018
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811054662
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This is the first book to provide an accessible and comprehensive introduction to a newly developed smoothing technique using asymmetric kernel functions. Further, it discusses the statistical properties of estimators and test statistics using asymmetric kernels. The topics addressed include the bias-variance tradeoff, smoothing parameter choices, achieving rate improvements with bias reduction techniques, and estimation with weakly dependent data. Further, the large- and finite-sample properties of estimators and test statistics smoothed by asymmetric kernels are compared with those smoothed by symmetric kernels. Lastly, the book addresses the applications of asymmetric kernel estimation and testing to various forms of nonnegative economic and financial data.

Until recently, the most popularly chosen nonparametric methods used symmetric kernel functions to estimate probability density functions of symmetric distributions with unbounded support. Yet many types of economic and financial data are nonnegative and violate the presumed conditions of conventional methods. Examples include incomes, wages, short-term interest rates, and insurance claims. Such observations are often concentrated near the boundary and have long tails with sparse data. Smoothing with asymmetric kernel functions has increasingly gained attention, because the approach successfully addresses the issues arising from distributions that have natural boundaries at the origin and heavy positive skewness. Offering an overview of recently developed kernel methods, complemented by intuitive explanations and mathematical proofs, this book is highly recommended to all readers seeking an in-depth and up-to-date guide to nonparametric estimation methods employing asymmetric kernel smoothing.

1 Asymmetric Kernels: An Introduction
1(16)
1.1 How Did Asymmetric Kernels Emerge?
1(3)
1.1.1 Boundary Bias in Kernel Density Estimation
1(1)
1.1.2 Matching the Support of the Kernel with That of the Density
2(1)
1.1.3 Emergence of Asymmetric Kernels
2(1)
1.1.4 Asymmetric Kernel Density Estimation as General Weight Function Estimation
3(1)
1.2 What Are Asymmetric Kernels?
4(1)
1.2.1 Two Key Properties of Asymmetric Kernels
4(1)
1.2.2 List of Asymmetric Kernels
5(1)
1.3 Which Asymmetric Kernels Are Investigated?
5(12)
1.3.1 Scope of Asymmetric Kernels to Be Studied
5(2)
1.3.2 Functional Forms of the Kernels
7(4)
1.3.3 Shapes of the Kernels
11(2)
References
13(4)
2 Univariate Density Estimation
17(24)
2.1 Bias and Variance
17(6)
2.1.1 Regularity Conditions
17(1)
2.1.2 Bias Approximation
18(3)
2.1.3 Variance Approximation
21(2)
2.2 Local Properties
23(3)
2.3 Global Properties
26(2)
2.4 Other Convergence Results
28(1)
2.5 Properties of Density Estimators at the Boundary
29(1)
2.5.1 Bias and Variance of Density Estimators at the Boundary
29(1)
2.5.2 Consistency of Density Estimators for Unbounded Densities at the Origin
29(1)
2.6 Further Topics
30(3)
2.6.1 Density Estimation Using Weakly Dependent Observations
30(1)
2.6.2 Normalization
31(1)
2.6.3 Extension to Multivariate Density Estimation
32(1)
2.7 Smoothing Parameter Selection
33(4)
2.7.1 Plug-In Methods
33(2)
2.7.2 Cross-Validation Methods
35(2)
2.8 List of Useful Formulae
37(4)
References
38(3)
3 Bias Correction in Density Estimation
41(18)
3.1 An Overview
41(2)
3.1.1 Nonparametric Bias Correction
41(1)
3.1.2 Semiparametric Bias Correction
42(1)
3.2 Nonparametric Bias Correction
43(8)
3.2.1 Additive Bias Correction
43(2)
3.2.2 Multiplicative Bias Correction
45(6)
3.3 Semiparametric Bias Correction
51(6)
3.3.1 Local Multiplicative Bias Correction
51(2)
3.3.2 Local Transformation Bias Correction
53(2)
3.3.3 Rate Improvement via Combining with JLN-MBC
55(2)
3.4 Smoothing Parameter Selection
57(2)
References
57(2)
4 Regression Estimation
59(14)
4.1 Preliminary
59(1)
4.1.1 The Estimators
59(1)
4.2 Convergence Properties of the Regression Estimators
60(4)
4.2.1 Regularity Conditions
60(1)
4.2.2 Asymptotic Normality of the Estimators
61(2)
4.2.3 Other Convergence Results
63(1)
4.2.4 Regression Estimation Using Weakly Dependent Observations
64(1)
4.3 Estimation of Scalar Diffusion Models of Short-Term Interest Rates
64(6)
4.3.1 Background
64(1)
4.3.2 Estimation of Scalar Diffusion Models via Asymmetric Kernel Smoothing
65(4)
4.3.3 Additional Remarks
69(1)
4.4 Smoothing Parameter Selection
70(3)
References
71(2)
5 Specification Testing
73(30)
5.1 Test of a Parametric Form in Autoregressive Conditional Duration Models
73(5)
5.1.1 Background
73(1)
5.1.2 Specification Testing for the Distribution of the Standardized Innovation
74(3)
5.1.3 Additional Remarks
77(1)
5.2 Test of Symmetry in Densities
78(5)
5.2.1 Background
78(1)
5.2.2 Asymmetric Kernel-Based Testing for Symmetry in Densities
78(4)
5.2.3 Additional Remarks
82(1)
5.3 Test of Discontinuity in Densities
83(7)
5.3.1 Background
83(1)
5.3.2 Joint Estimation and Testing on Discontinuity in Densities at Multiple Cutoffs
84(4)
5.3.3 Estimation of the Entire Density in the Presence of Multiple Discontinuity Points
88(2)
5.4 Smoothing Parameter Selection
90(2)
5.5 Technical Proofs
92(11)
5.5.1 Proof of Theorem 5.4
92(6)
5.5.2 Proof of Theorem 5.5
98(2)
References
100(3)
6 Asymmetric Kernels in Action
103(6)
6.1 Estimation of Income Distributions
103(1)
6.2 Estimation and Testing of Discontinuity in Densities
104(5)
6.2.1 Finite-Sample Properties of Test Statistics for Discontinuity at Multiple Cutoffs
105(1)
6.2.2 Empirical Illustration
106(1)
References
107(2)
Index 109
Masayuki Hirukawa, Faculty of Economics, Ryukoku University
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97898110546622e.html
Keywords: