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020 _a9781461480600
_9978-1-4614-8060-0
024 7 _a10.1007/978-1-4614-8060-0
_2doi
035 _ato000540938
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aHB139-141
072 7 _aKCH
_2bicssc
072 7 _aBUS021000
_2bisacsh
082 0 4 _a330.015195
_223
245 1 0 _aRecent Advances in Estimating Nonlinear Models
_helectronic resource
_bWith Applications in Economics and Finance /
_cedited by Jun Ma, Mark Wohar.
260 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXVI, 299 p. 39 illus., 24 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aChapter 1 Stock Return and Inflation: An Analysis Based on the State-Space Framework -- Chapter 2 Diffusion Index Model Specification and Estimation: Using Mixed Frequency Datasets -- Chapter 3 Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks -- Chapter 4 On the Use of the Flexible Fourier Form in Unit Roots Tests, Endogenous Breaks, and Parameter Instability -- Chapter 5 Testing for a Markov-Switching Mean in Serially-Correlated Data -- Chapter 6 Nonlinear Time Series Models and Model Selection -- Chapter 7 Nonstationarities and Markov Switching Models -- Chapter 8 Has Wealth Effect Changed Over Time? Evidence from Four Industrial Countries -- Chapter 9 A Simple Specification Procedure for the Transition Function in Persistent Nonlinear Times Series Models -- Chapter 10 Small Area Estimation with Correctly Specified Linking Models -- Chapter 11 Forecasting Stock Returns: Does Switching between Models Help? -- Chapter 12 The Global Joint Distribution of Income and Health.
520 _aThis edited volume provides a timely overview of nonlinear estimation techniques, offering new methods and insights into nonlinear time series analysis. The focus is on such topics as state-space model and the identification issue, use of Markov Switching Models and Smooth Transition Models to analyze economic series, and how best to distinguish between competing nonlinear models. Most economic theory suggests that the economic relationships among economic variables in the real world are fairly complex and nonlinear. Nonlinear models are necessary to capture these important channels through which economic variables can influence each other and various policies can affect economic activities. This volume features cutting-edge research from leading academics in economics, finance, and business management. The principles and techniques used here will appeal to econometricians, finance professors teaching quantitative finance, researchers, and graduate students interested in learning how to apply advances in nonlinear time series modeling to solve complex problems in economics and finance.
650 0 _aEconomics.
_9135154
650 0 _aEconomics
_xStatistics.
_9304057
650 0 _aEconometrics.
_9566276
650 0 _aFinance.
_9142509
650 1 4 _aEconomics/Management Science.
_9247365
650 2 4 _aEconometrics.
_9566276
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
_9304058
650 2 4 _aFinancial Economics.
_9304566
700 1 _aMa, Jun.
_eeditor.
_9303697
700 1 _aWohar, Mark.
_eeditor.
_9446504
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-8060-0
912 _aZDB-2-SBE
999 _c399084