000			11341nam a2200433 i 4500
008			100525s2010 enka b 001 0 eng
010			_a2010935053
020			_a9780199587148
020			_a0199587140
020			_a0199587159
020			_a9780199587155
035			_a(OCoLC)636906670
040			_aUKM _erda _cUKM _dYDXCP _dCDX _dNLGGC _dTEF _dBWX _dTXI _dMUU
049			_aBAUN_MERKEZ
050	0	4	_aQA280 _b.T47 2010
082	0	4	_222
100	1		_aTeräsvirta, Timo
245	1	0	_aModelling nonlinear economic time series / _cby Timo Teräsvirta, Dag Tjøstheim, and Clive W.J. Granger
264		1	_aOxford ; _aNew York : _bOxford University Press, _c2010
300			_axxviii, 557 pages : _billustrations, _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aAdvanced texts in econometrics
504			_aIncludes bibliographical references (pages 470-536) and indexes
505	0	0	_tTable Of Contents: _tList of Figures _tList of Tables _tAcronyms and abbreviations _t1 Concepts, models, and definitions _t1.1 Defining nonlinearity _t1.2 Where does nonlinearity come from? _t1.3 Stationarity and nonstationarity _t1.4 Invertibility _t1.5 Trends _t1.6 Seasonality _t1.7 Conditional distributions _t1.8 Wold's representation and Volterra expansion _t1.9 Additive models _t1.10 Spectral analysis _t1.11 Chaos _t2 Nonlinear models in economic theory _t2.1 Disequilibrium models _t2.2 Labour market models _t2.2.1 Theory _t2.2.2 Practice _t2.3 Exchange rates in a target zone _t2.3.1 Theory _t2.3.2 Practice _t2.4 Production theory _t3 Parametric nonlinear models _t3.1 General considerations _t3.2 Switching regression models _t3.2.1 Standard switching regression model _t3.2.2 Vector threshold autoregressive model _t3.3 Markov-switching regression models _t3.4 Smooth transition regression models _t3.4.1 Standard smooth transition regression model _t3.4.2 Additive, multiple, and time-varying STR models _t3.4.3 Vector smooth transition autoregressive model _t3.5 Polynomial models _t3.6 Artificial neural network models _t3.7 Min-max models _t3.8 Nonlinear moving average models _t3.9 Bilinear models _t3.10 Time-varying parameters and state space models _t3.11 Random coefficient and volatility models _t4 The nonparametric approach _t4.1 Introduction _t4.2 Autocovariance and spectrum _t4.3 Density, conditional mean, and conditional variance _t4.3.1 Non-Gaussian marginals _t4.3.2 Conditional quantities _t4.4 Dependence measures for nonlinear processes _t4.4.1 Local measures of dependence _t4.4.2 Global measures of dependence _t4.4.3 Measures based on density and distribution functions _t4.4.4 The copula _t5 Testing linearity against parametric alternatives _t5.1 Introduction _t5.2 Consistent misspecification tests _t5.3 Lagrange multiplier or score test _t5.3.1 Standard case _t5.3.2 Test in stages and a heteroskedasticity-robust version _t5.3.3 Robustifying against conditional heteroskedasticity _t5.4 Locally equivalent alternatives _t5.5 Nonlinear model only identified under the alternative _t5.5.1 Identification problem _t5.5.2 General solution _t5.5.3 Lagrange multiplier-type tests _t5.5.4 Monte Carlo tests _t5.5.5 Giving values to the nuisance parameters _t5.6 Testing linearity against unspecified alternatives _t5.6.1 Regression Specification Error Test _t5.6.2 Tests based on expansions _t5.7 Comparing parametric linearity tests using asymptotic relative efficiency _t5.7.1 Definition _t5.7.2 An example _t5.8 Which test to use? _t6 Testing parameter constancy _t6.1 General considerations _t6.2 Generalizing the Chow test _t6.2.1 Testing against a single break _t6.2.2 Testing against multiple breaks _t6.3 Lagrange multiplier type tests _t6.3.1 Testing a stationary single-equation model _t6.3.2 Testing a stationary vector autoregressive model _t6.3.3 Testing a nonstationary vector autoregressive model _t6.4 Tests based on recursive estimation of parameters _t6.4.1 Cumulative sum tests _t6.4.2 Moving sum tests _t6.4.3 Fluctuation tests _t6.4.4 Tests against stochastic parameters _t6.4.5 Testing the constancy of cointegrating relationships _t7 Nonparametric specification tests _t7.1 Introduction _t7.2 Nonparametric linearity tests _t7.2.1 Nonparametric tests: the spectral domain _t7.2.2 Testing linearity in the conditional mean and conditional variance _t7.2.3 Estimation _t7.2.4 Asymptotic theory _t7.2.5 Finite-sample properties and use of the asymptotics _t7.2.6 A bootstrap approach to testing _t7.3 Testing for specific functional forms _t7.3.1 Tests based on residuals _t7.3.2 Conditional mean and conditional variance testing _t7.3.3 Continuous time _t7.4 Selecting lags _t7.5 Testing for additivity and interaction _t7.5.1 Testing in additive models _t7.5.2 A simulated example _t7.6 Tests for partial linearity and semiparametric modelling _t7.7 Tests of independence _t7.7.1 Traditional tests _t7.7.2 Rank correlation _t7.7.3 Frequency based tests _t7.7.4 BDS test _t7.7.5 Distribution based tests of independence _t7.7.6 Generalized spectrum and tests of independence _t7.7.7 Density based tests of independence _t7.7.8 Some examples of independence testing _t8 Models of conditional heteroskedasticity _t8.1 Autoregressive conditional heteroskedasticity _t8.1.1 The ARCH model _t8.2 The Generalized ARCH model _t8.2.1 Why Generalized ARCH? _t8.2.2 Families of univariate GARCH models _t8.2.3 Nonlinear GARCH _t8.2.4 Time-varying GARCH _t8.2.5 Moment structure of first-order GARCH models _t8.2.6 Moment structure of higher-order GARCH models _t8.2.7 Integrated and fractionally Integrated GARCH _t8.2.8 Stylized facts and the GARCH model _t8.2.9 Building univariate GARCH models _t8.3 Family of Exponential GARCH models _t8.3.1 Moment structure of EGARCH model _t8.3.2 Stylized facts and the EGARCH model _t8.3.3 Building EGARCH models _t8.4 The Autoregressive Stochastic Volatility model _t8.4.1 Definition _t8.4.2 Moment structure of ARSV models _t8.4.3 Stylized facts and the stochastic volatility model _t8.4.4 Estimation of ARSV models _t8.4.5 Comparing the ARSV model with GARCH _t8.5 GARCH-in-Mean model _t8.6 Realized volatility _t8.7 Multivariate GARCH models _t8.7.1 General multivariate GARCH model _t8.7.2 Link to random coefficient models _t8.7.3 Constant Conditional Correlation GARCH _t8.7.4 Testing the constant correlation assumption and the Dynamic Conditional Correlation model _t8.7.5 Other extensions to the CCC-GARCH model _t8.7.6 The BEKK-GARCH model _t8.7.7 Factor GARCH models _t9 Time-varying parameters and state space models _t9.1 Introduction _t9.2 Linear state space models _t9.3 Time-varying parameter models _t9.4 Nonlinear state space models _t9.4.1 Extended Kalman filter _t9.4.2 Kitagawa's grid approximation _t9.4.3 Monte Carlo methods _t9.4.4 Particle filters _t9.4.5 Approximating with a Gaussian density _t9.5 Hidden Markov chains and regimes _t9.5.1 Hidden Markov chains _t9.5.2 Mixture models _t9.6 Estimating parameters _t9.6.1 Stationarity _t9.6.2 Identification _t9.6.3 Estimation in linear models _t9.6.4 The nonlinear case _t9.6.5 Estimation in hidden Markov and mixture models _t10 Nonparametric models _t10.1 Additive models _t10.1.1 Estimation in purely additive models _t10.1.2 Marginal integration _t10.1.3 Backfitting and smoothed backfitting _t10.1.4 Additive models with interactions _t10.1.5 A simulated example _t10.1.6 Nonparametric and additive estimation of the conditional variance function _t10.2 Some related models _t10.2.1 Functional coefficient autoregressive models _t10.2.2 Transformation of dependent variables and the ACE algorithm _t10.2.3 Regression trees, splines, and MARS _t10.2.4 Quantile regression _t10.3 Semiparametric models _t10.3.1 Index models _t10.3.2 Projection pursuit regression _t10.3.3 Partially linear models _t10.4 Robust and adaptive estimation _t11 Nonlinear and nonstationary models _t11.1 Long memory models _t11.2 Linear unit root models _t11.3 Vector autoregressive processes and linear cointegration _t11.4 Nonlinear I(1) processes _t11.5 Nonlinear error correction models _t11.6 Parametric nonlinear regression _t11.7 Nonparametric estimation in a nonlinear cointegration type framework _t11.8 Stochastic unit root models _t12 Algorithms for estimating parametric nonlinear models _t12.1 Optimization without derivatives _t12.1.1 Grid and line searches _t12.1.2 Conjugate directions _t12.1.3 Simulated annealing _t12.1.4 Evolutionary algorithms _t12.2 Methods requiring derivatives _t12.2.1 Gradient methods _t12.2.2 Variable metric methods _t12.3 Other methods _t12.3.1 EM algorithm _t12.3.2 Sequential estimation for neural networks _t13 Basic nonparametric estimates _t13.1 Density estimation _t13.1.1 Kernel estimation _t13.1.2 Bias and variance reduction _t13.1.3 Choice of bandwidth _t13.1.4 Variable bandwidth and nearest neighbour estimation _t13.1.5 Multivariate density estimation _t13.2 Nonparametric regression estimation _t13.2.1 Kernel regression estimation _t13.2.2 Local polynomial estimation _t13.2.3 Bias, convolution, and higher-order kernels _t13.2.4 Nearest neighbour estimation _t13.2.5 Splines and MARS _t13.2.6 Series
505	0	0	_t expansion _t13.2.7 Choice of bandwidth for nonparametric regression _t14 Forecasting from nonlinear models _t14.1 Introduction _t14.2 Conditional mean forecasts from parametric models _t14.2.1 Analytical point forecasts _t14.2.2 Numerical techniques in forecasting _t14.3 Forecasting with nonparametric models _t14.4 Forecast accuracy _t14.5 The usefulness of forecasts from nonlinear models _t14.6 Forecasting volatility _t14.7 Overview of forecasting from nonlinear models _t15 Nonlinear impulse responses _t15.1 Generalized impulse response function _t15.2 Graphical representation _t16 Building nonlinear models _t16.1 General considerations _t16.2 Nonparametric and semiparametric models _t16.3 Building smooth transition regression models _t16.3.1 The three stages of the modelling procedure _t16.3.2 Specification _t16.3.3 Estimation of parameters _t16.3.4 Evaluation _t16.3.5 Graphical tools for characterizing the dynamic behaviour of the STAR model _t16.3.6 Examples _t16.4 Building switching regression models _t16.4.1 Specification _t16.4.2 Estimation and evaluation _t16.4.3 Examples _t16.5 Building artificial neural network models _t16.5.1 Specification _t16.5.2 Estimation _t16.5.3 Evaluation _t16.5.4 Alternative modelling approaches _t16.5.5 Examples _t16.6 Two forecast comparisons _t16.6.1 Forecasting Wolf's annual sunspot numbers _t16.6.2 Forecasting the monthly US unemployment rate _t17 Other topics _t17.1 Aggregation _t17.2 Seasonality _t17.2.1 Time-varying seasonality _t17.2.2 Temporal aggregation and time-varying seasonality _t17.2.3 Nonlinear filters in seasonal adjustment _t17.3 Outliers and nonlinearity _t17.3.1 What is an outlier? _t17.3.2 Model-based definitions _tBibliography _tAuthor Index _tGeneral Index
520	8		_aA comprehensive assessment of many recent developments in the modelling of time series, this text introduces various nonlinear models and discusses their practical use, encouraging the reader to apply nonlinear models to their practical modelling problems
650		0	_aTime-series analysis
650		0	_aEconometric models
650		0	_aNonlinear theories
700	1		_aTjøstheim, Dag
700	1		_aGranger, C. W. J. _q(Clive William John), _d1934-2009
710	2		_9111967 _aOxford University Press.
830		0	_9108833 _aAdvanced texts in econometrics.
900			_a34869
900			_bsatın
942			_2lcc _cKT
999			_c32123 _d32123