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040			_aNISER LIBRARY _bENGLISH _cNISER LIBRARY
041			_aENGLISH
082			_a517.9 _bHAM-T
100			_aHamilton, James D.
245			_aTime series analysis
260			_aKolkata : _bLevant Books, _c2012.
300			_axiv,799p. _bPbk.
520			_aPreface Much of economics is concerned with modeling dynamics. There has been an explosion of research in this area in the last decade, as "time series econometrics" Souooe jeudua snouuouks ag on auoo Aeosed sey of dynamie systems, while others summarize the earlier literature on statistical inference for time series data. There seemed a use for a text that could integrate the theoretical and empirical issues as well as incorporate the many advances of the last decade, such as the analysis of vector autoregressions, estimation by gen- eralized method of moments, and statistical inference for nonstationary data. This is the goal of Time Series Analysis. SAJvue ouosa ay u sauVape an jo aleaoo pood aptoad sxa eanas A principal anticipated use of the book would be as a textbook for a graduate econometrics course in time series analysis. The book aims for maximum flexibility through what might be described as an integrated modular structure. As an example of this, the first three sections of Chapter 13 on the Kalman filter could be covered right after Chapter 4, if desired. Aliernatively, Chapter 13 could be skipped al- together without loss of comprehension. Despite this flexibility, state-space ideas are fully integrated into the text beginning with Chapter 1, where a state-space representation is used (without any jargon or formalism) to introduce the key results concerning difference equations. Thus, when the reader encounters the formal development of the state-space framework and the Kalman filter in Chapter 13. the notation and key ideas should already be quite familiar. Spectral analysis (Chapter 6) is another topic that could be covered at a point of the reader's choosing or skipped altogether. In this case, the integrated modular structure is achieved by the early introduction and use of autocovariance-generating functions and filters. Wherever possible, results are described in terms of these rather than the spectrum. Although the book is designed with an econometrics course in time series methods in mind, the book should be useful for several other purposes. It is completely self-contained, starting from basic principles accessible to first-year graduate students and including an extensive math review appendix. Thus the book would be quite suitable for a first-year graduate course in macroeconomics or dynamic methods that has no econometric content. Such a course might use Chap- ters I and 2, Sections 3.1 through 3.5, and Sections 4.1 and 4.2. Yet another intended use for the book would be in a conventional econo- metrics course without an explicit time series focus. The popular econometrics texts do not have much discussion of such topics as numerical methods; asymptotic results for serially dependent, heterogeneously distributed observations; estimation of models with distributed lags; autocorrelation- and heteroskedasticity-consistent
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