Описание: This book explores widely used seasonaladjustment methods and recent developments in real time trend-cycle estimation.It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATSand STAMP - the main seasonal adjustment methods used by statistical agencies. Several real-world cases illustrate eachmethod and real data examples can be followed throughout the text. Thetrend-cycle estimation is presented using nonparametric techniques based onmoving averages, linear filters and reproducing kernel Hilbert spaces, takingrecent advances into account. The book provides a systematical treatment ofresults that to date have been scattered throughout the literature.Seasonal adjustment and real timetrend-cycle prediction play an essential part at all levels of activity inmodern economies. They are used by governments to counteract cyclicalrecessions, by central banks to control inflation, by decision makers forbetter modeling and planning and by hospitals, manufacturers, builders,transportation, and consumers in general to decide on appropriate action.This book appeals to practitioners ingovernment institutions, finance and business, macroeconomists, and other professionalswho use economic data as well as academic researchers in time series analysis,seasonal adjustment methods, filtering and signal extraction. It is also usefulfor graduate and final-year undergraduate courses in econometrics and timeseries with a good understanding of linear regression and matrix algebra, aswell as ARIMA modelling.
Описание: 1 The Importance of Irreversibility and Learning - Familiar 11 Bxamples Revisited 1. 3 Conclusions 30 2 The Role of Irreversibility and Learning in Sequential Decision Problems - Basic Concepts 33 2. 2 Economic Examples 37 2. 2 The Two-Period Model with Uncertainty 46 2. 2 An Example: Money Demand as Demand for Flexibility 61 2.
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