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Vector autoregressive models [PDF]

, 2011
This comprehensive Handbook presents the current state of art in the theory and methodology of macroeconomic data analysis. It is intended as a reference for graduate students and researchers interested in exploring new methodologies, but can also be employed as a graduate text.
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Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model

International Journal of Forecasting, 2017
We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of few linear combinations of all the variables in the system ...
G. Cubadda, B. Guardabascio
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An autoregressive credibility IBNR model

Blätter der DGVFM, 1995
An application of credibility theory to IBNR reserves is described for portfolios in which payments in consecutive development periods are determined by payments in preceding development periods, in addition to newly originating payments. For that purpose an autoregressive model is introduced and estimators for the occurring parameters are given ...
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Tutorial on Multivariate Autoregressive Modelling

Journal of Clinical Monitoring and Computing, 2006
In the present paper, the theoretical background of multivariate autoregressive modelling (MAR) is explained. The motivation for MAR modelling is the need to study the linear relationships between signals. In biomedical engineering, MAR modelling is used especially in the analysis of cardiovascular dynamics and electroencephalographic signals, because ...
Reijo Takalo, Heimo Ihalainen, Heli Hytti   +2 more
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Modelling of cointegration in the vector autoregressive model

Economic Modelling, 2000
Abstract A survey is given of some results obtained for the cointegrated VAR. The Granger representation theorem is discussed and the notions of cointegration and common trends are defined. The statistical model for cointegrated I (1) variables is defined, and it is shown how hypotheses on the cointegrating relations can be estimated under suitable ...
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Using an autoregressive model for DMC

2012 6th European Conference on Antennas and Propagation (EUCAP), 2012
In this work an iterative two-step procedure is proposed for estimating the diffuse multipath component (DMC) in noise and specular multipath components from channel measurement data. During the first step of the scheme the specular part of the impulse response from the measured data is estimated. Then, the reconstructed channel impulse response due to
Jost, Thomas, Wang, Wei, Shutin, Dmitriy, Antreich, Felix   +3 more
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Scalable Pre-training of Large Autoregressive Image Models

International Conference on Machine Learning
This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties.
Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai, Miguel Angel Bautista, Alexander Toshev, Vaishaal Shankar, J. Susskind, Armand Joulin   +7 more
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Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation

, 1982
Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper.
R. Engle
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Distribution of the Estimators for Autoregressive Time Series with a Unit Root

, 1979
Let n observations Y 1, Y 2, ···, Y n be generated by the model Y t = pY t−1 + e t , where Y 0 is a fixed constant and {e t } t-1 n is a sequence of independent normal random variables with mean 0 and variance σ2.
D. Dickey, W. Fuller
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A Note on Autoregressive Modeling

Econometric Theory, 1994
This paper addresses the problem of estimating vector autoregressive models. An approach to handling nonstationary (integrated) time series is briefly discussed, but the main emphasis is upon the estimation of autoregressive approximations to stationary processes. Three alternative estimators are considered–the Yule-Walker, least-squares, and Burg-type
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