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Time Series and Dynamic Models ; Séries Temporelles et Modèles Dynamiques

By: Contributor(s): Language: English Language: French Series: Themes in Modern Econometrics | Phillips, Peter C.B. (Ed.) | Pagan, Adrian (Ed.) | Gourieroux, Christian (Ed.) | et alPublication details: Cambridge, New York, Oakleigh : Cambridge University Press 1997Edition: 1. EdDescription: xv, 668 ppISBN:
  • 0-521-42308-2
Subject(s): DDC classification:
  • 330 Economics
Online resources:
Contents:
from the Table of Contents: Preface; Introduction; Traditional Methods: Linear Regression for Seasonal Adjustment; Moving Averages for Seasonal Adjustment; Exponential Smoothing Methods; Probabilistic and Statistical Properties of Stationary Processes: Some Results on the Univariate Processes; The Box and Jenkins Method for Forecasting; Multivariate Time Series; Time-series Representations; Estimation and Testing (Stationary Case); Time-series Econometrics. Stationary and Nonstationary Models: Causality, Exogeneity, and Shocks; Trend Components; Expectations; Specification Analysis; Statistical Properties of Nonstationary Processes; State-space Models: State-space Models and the Kalman Filter; Applications of the State-space Model;
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Loanable Institute for Advanced Studies (IHS) Book 18875-A Available IHS102745407

from the Table of Contents: Preface; Introduction; Traditional Methods: Linear Regression for Seasonal Adjustment; Moving Averages for Seasonal Adjustment; Exponential Smoothing Methods; Probabilistic and Statistical Properties of Stationary Processes: Some Results on the Univariate Processes; The Box and Jenkins Method for Forecasting; Multivariate Time Series; Time-series Representations; Estimation and Testing (Stationary Case); Time-series Econometrics. Stationary and Nonstationary Models: Causality, Exogeneity, and Shocks; Trend Components; Expectations; Specification Analysis; Statistical Properties of Nonstationary Processes; State-space Models: State-space Models and the Kalman Filter; Applications of the State-space Model;

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