000			03298nam a22002177a 4500
005			20251023111030.0
008			251023b |||||||| |||| 00| 0 eng d
020			_a9783031888373
082			_a330.015195 _bNES
100			_aNeusser, Klaus _924959
245			_aTime series econometrics
250			_a2nd
260			_aSwitzerland _bSpringer _c2025
300			_axvii, 429p.
365			_aEUR _b99.99
490			_aSpringer Text in Business and Economics
500			_aFront Matter Pages i-xxiii Download chapter PDF Univariate Time Series Analysis Front Matter Pages 1-1 Download chapter PDF Introduction and Basic Theoretical Concepts Klaus Neusser Pages 3-22 Autoregressive Moving-Average Processes Klaus Neusser Pages 23-42 Forecasting Stationary Processes Klaus Neusser Pages 43-65 Estimation of the Expected Value and the Autocorrelation Function of a Stationary Stochastic Processes Klaus Neusser Pages 67-86 Modeling Stationary ARMA Processes Klaus Neusser Pages 87-106 Spectral Analysis and Linear Filters Klaus Neusser Pages 107-132 Integrated Processes Klaus Neusser Pages 133-169 Models of Volatility Klaus Neusser Pages 171-195 Multivariate Time Series Analysis Front Matter Pages 197-197 Download chapter PDF Synopsis on Empirical Macroeconomic Research Klaus Neusser Pages 199-201 Definitions and Stationarity Klaus Neusser Pages 203-208 Estimation of Mean and Covariance Function Klaus Neusser Pages 209-217 Vector Autoregressive Moving-Average Processes Klaus Neusser Pages 219-229 Estimation of Vector Autoregressive Models Klaus Neusser Pages 231-247
520			_aThis text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and its relation to the basic properties of covariance funtions, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting as well as regressions models and presenting standard statistical tests. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text is devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. The exposition finally connects to recent developments in the field. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students. (https://link.springer.com/book/10.1007/978-3-031-88838-0)
650			_aEconometrics
942			_cBK _2ddc
999			_c10373 _d10373