TY  - BOOK
AU  - Teräsvirta,Timo
AU  - Tjøstheim,Dag
AU  - Granger,C.W.J.
ED  - Oxford University Press.
TI  - Modelling nonlinear economic time series
T2  - Advanced texts in econometrics
SN  - 9780199587148
AV  - QA280 .T47 2010
PY  - 2010///
CY  - Oxford, New York
PB  - Oxford University Press
KW  - Time-series analysis
KW  - Econometric models
KW  - Nonlinear theories
N1  - Includes bibliographical references (pages 470-536) and indexes; Table Of Contents; List of Figures; List of Tables; Acronyms and abbreviations; 1 Concepts, models, and definitions; 1.1 Defining nonlinearity; 1.2 Where does nonlinearity come from?; 1.3 Stationarity and nonstationarity; 1.4 Invertibility; 1.5 Trends; 1.6 Seasonality; 1.7 Conditional distributions; 1.8 Wold's representation and Volterra expansion; 1.9 Additive models; 1.10 Spectral analysis; 1.11 Chaos; 2 Nonlinear models in economic theory; 2.1 Disequilibrium models; 2.2 Labour market models; 2.2.1 Theory; 2.2.2 Practice; 2.3 Exchange rates in a target zone; 2.3.1 Theory; 2.3.2 Practice; 2.4 Production theory; 3 Parametric nonlinear models; 3.1 General considerations; 3.2 Switching regression models; 3.2.1 Standard switching regression model; 3.2.2 Vector threshold autoregressive model; 3.3 Markov-switching regression models; 3.4 Smooth transition regression models; 3.4.1 Standard smooth transition regression model; 3.4.2 Additive, multiple, and time-varying STR models; 3.4.3 Vector smooth transition autoregressive model; 3.5 Polynomial models; 3.6 Artificial neural network models; 3.7 Min-max models; 3.8 Nonlinear moving average models; 3.9 Bilinear models; 3.10 Time-varying parameters and state space models; 3.11 Random coefficient and volatility models; 4 The nonparametric approach; 4.1 Introduction; 4.2 Autocovariance and spectrum; 4.3 Density, conditional mean, and conditional variance; 4.3.1 Non-Gaussian marginals; 4.3.2 Conditional quantities; 4.4 Dependence measures for nonlinear processes; 4.4.1 Local measures of dependence; 4.4.2 Global measures of dependence; 4.4.3 Measures based on density and distribution functions; 4.4.4 The copula; 5 Testing linearity against parametric alternatives; 5.1 Introduction; 5.2 Consistent misspecification tests; 5.3 Lagrange multiplier or score test; 5.3.1 Standard case; 5.3.2 Test in stages and a heteroskedasticity-robust version; 5.3.3 Robustifying against conditional heteroskedasticity; 5.4 Locally equivalent alternatives; 5.5 Nonlinear model only identified under the alternative; 5.5.1 Identification problem; 5.5.2 General solution; 5.5.3 Lagrange multiplier-type tests; 5.5.4 Monte Carlo tests; 5.5.5 Giving values to the nuisance parameters; 5.6 Testing linearity against unspecified alternatives; 5.6.1 Regression Specification Error Test; 5.6.2 Tests based on expansions; 5.7 Comparing parametric linearity tests using asymptotic relative efficiency; 5.7.1 Definition; 5.7.2 An example; 5.8 Which test to use?; 6 Testing parameter constancy; 6.1 General considerations; 6.2 Generalizing the Chow test; 6.2.1 Testing against a single break; 6.2.2 Testing against multiple breaks; 6.3 Lagrange multiplier type tests; 6.3.1 Testing a stationary single-equation model; 6.3.2 Testing a stationary vector autoregressive model; 6.3.3 Testing a nonstationary vector autoregressive model; 6.4 Tests based on recursive estimation of parameters; 6.4.1 Cumulative sum tests; 6.4.2 Moving sum tests; 6.4.3 Fluctuation tests; 6.4.4 Tests against stochastic parameters; 6.4.5 Testing the constancy of cointegrating relationships; 7 Nonparametric specification tests; 7.1 Introduction; 7.2 Nonparametric linearity tests; 7.2.1 Nonparametric tests: the spectral domain; 7.2.2 Testing linearity in the conditional mean and conditional variance; 7.2.3 Estimation; 7.2.4 Asymptotic theory; 7.2.5 Finite-sample properties and use of the asymptotics; 7.2.6 A bootstrap approach to testing; 7.3 Testing for specific functional forms; 7.3.1 Tests based on residuals; 7.3.2 Conditional mean and conditional variance testing; 7.3.3 Continuous time; 7.4 Selecting lags; 7.5 Testing for additivity and interaction; 7.5.1 Testing in additive models; 7.5.2 A simulated example; 7.6 Tests for partial linearity and semiparametric modelling; 7.7 Tests of independence; 7.7.1 Traditional tests; 7.7.2 Rank correlation; 7.7.3 Frequency based tests; 7.7.4 BDS test; 7.7.5 Distribution based tests of independence; 7.7.6 Generalized spectrum and tests of independence; 7.7.7 Density based tests of independence; 7.7.8 Some examples of independence testing; 8 Models of conditional heteroskedasticity; 8.1 Autoregressive conditional heteroskedasticity; 8.1.1 The ARCH model; 8.2 The Generalized ARCH model; 8.2.1 Why Generalized ARCH?; 8.2.2 Families of univariate GARCH models; 8.2.3 Nonlinear GARCH; 8.2.4 Time-varying GARCH; 8.2.5 Moment structure of first-order GARCH models; 8.2.6 Moment structure of higher-order GARCH models; 8.2.7 Integrated and fractionally Integrated GARCH; 8.2.8 Stylized facts and the GARCH model; 8.2.9 Building univariate GARCH models; 8.3 Family of Exponential GARCH models; 8.3.1 Moment structure of EGARCH model; 8.3.2 Stylized facts and the EGARCH model; 8.3.3 Building EGARCH models; 8.4 The Autoregressive Stochastic Volatility model; 8.4.1 Definition; 8.4.2 Moment structure of ARSV models; 8.4.3 Stylized facts and the stochastic volatility model; 8.4.4 Estimation of ARSV models; 8.4.5 Comparing the ARSV model with GARCH; 8.5 GARCH-in-Mean model; 8.6 Realized volatility; 8.7 Multivariate GARCH models; 8.7.1 General multivariate GARCH model; 8.7.2 Link to random coefficient models; 8.7.3 Constant Conditional Correlation GARCH; 8.7.4 Testing the constant correlation assumption and the Dynamic Conditional Correlation model; 8.7.5 Other extensions to the CCC-GARCH model; 8.7.6 The BEKK-GARCH model; 8.7.7 Factor GARCH models; 9 Time-varying parameters and state space models; 9.1 Introduction; 9.2 Linear state space models; 9.3 Time-varying parameter models; 9.4 Nonlinear state space models; 9.4.1 Extended Kalman filter; 9.4.2 Kitagawa's grid approximation; 9.4.3 Monte Carlo methods; 9.4.4 Particle filters; 9.4.5 Approximating with a Gaussian density; 9.5 Hidden Markov chains and regimes; 9.5.1 Hidden Markov chains; 9.5.2 Mixture models; 9.6 Estimating parameters; 9.6.1 Stationarity; 9.6.2 Identification; 9.6.3 Estimation in linear models; 9.6.4 The nonlinear case; 9.6.5 Estimation in hidden Markov and mixture models; 10 Nonparametric models; 10.1 Additive models; 10.1.1 Estimation in purely additive models; 10.1.2 Marginal integration; 10.1.3 Backfitting and smoothed backfitting; 10.1.4 Additive models with interactions; 10.1.5 A simulated example; 10.1.6 Nonparametric and additive estimation of the conditional variance function; 10.2 Some related models; 10.2.1 Functional coefficient autoregressive models; 10.2.2 Transformation of dependent variables and the ACE algorithm; 10.2.3 Regression trees, splines, and MARS; 10.2.4 Quantile regression; 10.3 Semiparametric models; 10.3.1 Index models; 10.3.2 Projection pursuit regression; 10.3.3 Partially linear models; 10.4 Robust and adaptive estimation; 11 Nonlinear and nonstationary models; 11.1 Long memory models; 11.2 Linear unit root models; 11.3 Vector autoregressive processes and linear cointegration; 11.4 Nonlinear I(1) processes; 11.5 Nonlinear error correction models; 11.6 Parametric nonlinear regression; 11.7 Nonparametric estimation in a nonlinear cointegration type framework; 11.8 Stochastic unit root models; 12 Algorithms for estimating parametric nonlinear models; 12.1 Optimization without derivatives; 12.1.1 Grid and line searches; 12.1.2 Conjugate directions; 12.1.3 Simulated annealing; 12.1.4 Evolutionary algorithms; 12.2 Methods requiring derivatives; 12.2.1 Gradient methods; 12.2.2 Variable metric methods; 12.3 Other methods; 12.3.1 EM algorithm; 12.3.2 Sequential estimation for neural networks; 13 Basic nonparametric estimates; 13.1 Density estimation; 13.1.1 Kernel estimation; 13.1.2 Bias and variance reduction; 13.1.3 Choice of bandwidth; 13.1.4 Variable bandwidth and nearest neighbour estimation; 13.1.5 Multivariate density estimation; 13.2 Nonparametric regression estimation; 13.2.1 Kernel regression estimation; 13.2.2 Local polynomial estimation; 13.2.3 Bias, convolution, and higher-order kernels; 13.2.4 Nearest neighbour estimation; 13.2.5 Splines and MARS; 13.2.6 Series;  expansion; 13.2.7 Choice of bandwidth for nonparametric regression; 14 Forecasting from nonlinear models; 14.1 Introduction; 14.2 Conditional mean forecasts from parametric models; 14.2.1 Analytical point forecasts; 14.2.2 Numerical techniques in forecasting; 14.3 Forecasting with nonparametric models; 14.4 Forecast accuracy; 14.5 The usefulness of forecasts from nonlinear models; 14.6 Forecasting volatility; 14.7 Overview of forecasting from nonlinear models; 15 Nonlinear impulse responses; 15.1 Generalized impulse response function; 15.2 Graphical representation; 16 Building nonlinear models; 16.1 General considerations; 16.2 Nonparametric and semiparametric models; 16.3 Building smooth transition regression models; 16.3.1 The three stages of the modelling procedure; 16.3.2 Specification; 16.3.3 Estimation of parameters; 16.3.4 Evaluation; 16.3.5 Graphical tools for characterizing the dynamic behaviour of the STAR model; 16.3.6 Examples; 16.4 Building switching regression models; 16.4.1 Specification; 16.4.2 Estimation and evaluation; 16.4.3 Examples; 16.5 Building artificial neural network models; 16.5.1 Specification; 16.5.2 Estimation; 16.5.3 Evaluation; 16.5.4 Alternative modelling approaches; 16.5.5 Examples; 16.6 Two forecast comparisons; 16.6.1 Forecasting Wolf's annual sunspot numbers; 16.6.2 Forecasting the monthly US unemployment rate; 17 Other topics; 17.1 Aggregation; 17.2 Seasonality; 17.2.1 Time-varying seasonality; 17.2.2 Temporal aggregation and time-varying seasonality; 17.2.3 Nonlinear filters in seasonal adjustment; 17.3 Outliers and nonlinearity; 17.3.1 What is an outlier?; 17.3.2 Model-based definitions; Bibliography; Author Index; General Index
N2  - A 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
ER  -