دانلود کتاب Forecasting economic time series using locally stationary processes : a new approach with applications

فهرست مطالب :

Content: 1 Introduction; 2 From stationarity to local stationarity; 2.1 Stationary stochastic processes; 2.1.1 A short introduction to stationarity; 2.1.2 Spectral representation of stationary processes; 2.1.3 Stationary ARMA processes; 2.1.4 Asymptotical properties of the sample partial autocorrelations of a stationary AR(p) process; 2.2 Locally stationary processes; 2.2.1 Evolutionary spectrum; 2.2.2 Definition of local stationarity; 2.2.3 Local covariance estimation; 2.2.4 Local partial autocorrelation; 2.2.5 TVAR; 3 Estimation. 3.1 Maximum likelihood estimation with the Kullback-Leibler information divergence3.2 Sieve estimation; 4 Forecasting; 4.1 Prediction in the case of stationarity; 4.2 Approaches to forecast time series using TVAR processes; 4.3 Iterative stages in the selection of a model; 4.4 Simulations; 5 Application; 5.1 Motivation; 5.2 Futures data; 5.2.1 Course of action; 5.2.2 Practical evaluation of TVAR processes on futures series; 5.3 Dow Jones index data; 6 Conclusion; 6.1 Contributions; 6.2 Possible directions for future research; References; Notations and abbreviations; List of tables. List of figuresA Appendix; B GAUSS source code; B.1 Fitting time-varying autoregressive models to non-stationaryprocesses; B.2 Procedures for computing the coefficient functions.