Fuller Wa 1976 Introduction To Statistical Time Series
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Introduction to Statistical Time Series
The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models, nonlinearestimation, multivariate models, state space representations, andempirical model identification. New sections have also been addedon the Wold decomposition, partial autocorrelation, long memoryprocesses, and the Kalman filter. Major topics include: * Moving average and autoregressive processes * Introduction to Fourier analysis * Spectral theory and filtering * Large sample theory * Estimation of the mean and autocorrelations * Estimation of the spectrum * Parameter estimation * Regression, trend, and seasonality * Unit root and explosive time series To accommodate a wide variety of readers, review material,especially on elementary results in Fourier analysis, large samplestatistics, and difference equations, has been included.
Macroeconometrics and Time Series Analysis
Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
Forecasting Models for the German Office Market
Author: Alexander Bönner
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-04-22
The applicability and performance of ARIMA, GARCH and multivariate regression models are analyzed and city as well as forecasting horizon-specific patterns are determined and interpreted by Alexander Bönner. Univariate rent forecasting models generally outperform multivariate rent forecasting regression models in the short run. In the long run, multivariate regression models dominate.