Autoregressive Process

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Autoregressive Process

Any process or model that uses past data to predict future data. Technical analysis, for example, is an autoregressive process. See also: Forecasting.
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Processes possessing this property are called compound autoregressive processes (CaR) (see Darolles, Gourieroux, and Jasiak, 2006); they are the discrete time counterparts of the (continuous time) affine processes, which is widely used in finance and insurance (see, e.g., Duffie, Filipovic, and Schachermayer, 2003; Schrager, 2006).
DYNAMIC FRAILTY COUNT PROCESS IN INSURANCE: A UNIFIED FRAMEWORK FOR ESTIMATION, PRICING, AND FORECASTING
By using (4) we generated 100 multivariate autoregressive processes with known causality structures.
Sparse Causality Network Retrieval from Short Time Series
By taking into account time-lagged soil water content, time-lagged soil temperature, autoregressive processes and seasonality, the model provides more-detailed information on the nature of the relationship between [N.sub.2]O and the environmental drivers and the effects of temporal resolution on [N.sub.2]O emissions, obtained from fitting the model with weekly or monthly data.
Nitrous oxide emissions from subtropical horticultural soils: a time series analysis
Among the topics are gradient-based algorithms with applications to signal-recovery problems, graphical models of autoregressive processes, convex analysis for non-negative blind source separation with applications in imaging, robust broadband adaptive beamforming using convex optimization, and cooperative distributed multi- agent optimization.
Convex optimization in signal processing and communications
To examine the nature of various nonlinear estimates, we generated a large number of series from second-order autoregressive processes. The best nonlinear model was compared with the best linear model using in-sample and out-of-sample criteria.
Out-of-sample forecasts and nonlinear model selection with an example of the term structure of interest rates
Likelihood ratio statistics for autoregressive processes. Econometrica 1981;49:1057-72.
Meteorologic influences on Plasmodium falciparum malaria in the highland tea estates of Kericho, Western Kenya
9B and 9C are not a pair of first-order autoregressive processes due to the dependence of [[Phi].sub.2](t) and [[Phi].sub.3](t) on other variables.
Complex dynamics in stochastic tritrophic models
A second approach is to build dynamics into the unobserved factors themselves by modeling them as autoregressive processes.
Sources of New York employment fluctuations
Johansen's tests for cointegration (1988, 1991, Johansen and Juselius (1990)) are a logical multivariate extension of Dickey-Fuller unit root tests for autoregressive processes. The Johansen tests use the canonical correlation of residuals from a reparameterized model to estimate the space of cointegration vectors and test the dimensions of the space.
Monetary policy indicators after deregulation
Estimation of the above relationship requires specification of the maximum number of lags, [Rho] and [Gamma] (or [Gamma]' and [Rho]' in reverse causality), for the autoregressive processes. As noted by Hsiao (1981), artificial selection of the length of the lags may bias the estimates and induce inefficiency in the inferential procedure.
The dynamics and competitiveness of European-Asian trade flows

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