TAILIEUCHUNG - Handbook of Econometrics Vols1-5 _ Chapter 17

Chapter 17 TIME SERIES AND SPECTRAL METHODS IN ECONOMETRICS A discrete time series is here defined as a vector x, of observations made at regularly spaced time points t = 1,2,. . . , n. These series arise in many fields, including oceanography, meterology, medicine, geophysics, as well as in economics | Chapter 17 TIME SERIES AND SPECTRAL METHODS IN ECONOMETRICS C. W. J. GRANGER and MARK W. WATSON Contents 1. Introduction 980 2. Methodology of time series analysis 980 3. Theory of forecasting 993 4. Multiple time series and econometric models 1002 5. Differencing and integrated models 1006 6. Seasonal adjustment 1009 7. Applications 1016 8. Conclusion 1019 References 1019 Handbook of Econometrics Volume II Edited by Z. Griliches and . Intriligator Elsevier Science Publishers BV 1984 980 C. W. J. Granger and M. W. Watson 1. Introduction A discrete time series is here defined as a vector xt of observations made at regularly spaced time points t l 2 These series arise in many fields including oceanography meterology medicine geophysics as well as in economics finance and management. There have been many methods of analysis proposed for such data and the methods are usually applicable to series from any field. For many years economists and particularly econometricans behaved as though either they did not realize that much of their data was in the form of time series or they did not view this fact as being important. Thus there existed two alternative strategies or approaches to the analysis of economic data excluding cross-sectional data from this discussion which can be called the time series and the classical econometric approaches. The time series approach was based on experience from many fields but that of the econometrician was viewed as applicable only to economic data which displayed a great deal of simultaneous or contemporaneous interrelationships. Some influences from the time series domain penetrated that of the classical econometrician such as how to deal with trends and seasonal components Durbin-Watson statistics and first-order serial correlation but there was little influence in the other direction. In the last ten years this state of affairs has changed dramatically with time series ideas becoming more mainstream and the procedures developed by .

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