New Developments in Time Series Econometrics

Softcover Nachdruck of the original 1. Auflage 1994
ISBN: 978-3-642-48744-6
Verlag: Springer

Buch, Englisch, 250 Seiten, Format (B × H): 170 mm x 244 mm, Gewicht: 455 g

Reihe: Studies in Empirical Economics

This book contains eleven articles which provide empirical applications as well as theoretical extensions of some of the most exciting recent developments in time-series econometrics. The papers are grouped around three broad themes: (I) the modeling of multivariate times series; (II) the analysis of structural change; (III) seasonality and fractional integration. Since these themes are closely inter-related, several other topics covered are also worth stressing: vector autoregressive (VAR) models, cointegration and error-correction models, nonparametric methods in time series, and fractionally integrated models. Researchers and students interested in macroeconomic and empirical finance will find in this collection a remarkably representative sample of recent work in this area.

Zielgruppe

Research

Autoren/Hrsg.

Dufour, Jean-Marie

Raj, Baldev

Inhaltsverzeichnis

New Developments in Time Series Econometrics: An Overview.- Modelling of Multivariate Economic Time Series.- Usefulness of Linear Transformations in Multivariate Time-Series Analysis.- VAR Modelling and Haavelmo’s Probability Approach to Macroeconomic Modelling.- Inference in Expectations Models of the Term Structure: A Non-parametric Approach.- Adjustment Costs and Time-To-Build in Factor Demand in the U.S Manufacturing Industry.- Structural Change Analysis.- Parameter Constancy in Cointegrating Regressions.- The HUMP-Shaped Behavior of Macroeconomic Fluctuations.- The Sources of the U.S. Money Demand Instability.- Seasonality, Cointegration and Fractional Integration.- On the (Mis)Specification of Seasonality and its Consequences: An Empirical Investigation with US Data.- Seasonal Cointegration, Common Seasonals, and Forecasting Seasonal Series.- A Note on Johansen’s Cointegration Procedure when Trends are Present.- Small Sample Bias in Conditional Sum-of-Squares Estimators of Fractionally Integrated ARMA Models.