Search results for "Autoregressive model"
showing 10 items of 120 documents
Spatial autocorrelation and the selection of simultaneous autoregressive models
2007
Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect estimates of model coefficients and inference from statistical models. Here, we test the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR err , lagged = SAR lag and mixed = SAR mix ) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial autocorrelation structures. Methods We evaluate the performance of SAR models by examining spatial patterns in model residuals (with correlograms and residual maps), by comparing model para…
Granger causality analysis of sleep brain-heart interactions
2014
We studied the networks of Granger causality (GC) between the time series of cardiac vagal autonomic activity and brain wave activities, measured respectively as the normalized high frequency (HF) component of heart rate variability and EEG power in the δ, θ, α, Ï, β bands, computed in 10 healthy subjects during sleep. GC analysis was performed by vector autoregressive modeling, and significance of each link in the network was assessed using F-statistics. The whole-night analysis revealed the existence of a fully connected network of brain-heart and brain-brain interactions, with the à EEG power acting as a hub which conveys the largest number of GC links between the heart and brain n…
Tests for time reversibility: a complementarity analysis
2003
Abstract Since time reversibility (TR) is a necessary condition for an independent and identically distributed (iid) sequence, several tests for TR have been suggested to be applied as tests for model misspecification. In this paper, we analyze possible complementarities among two well known TR tests (Ramsey and Rothman's test, and Chen et al.'s test) in two situations: (1) the fitted model is a linear ARMA model when the true data generating process is a nonlinear-in-mean model (either threshold autoregressive or bilinear), and (2) the fitted model is a symmetric GARCH model but the true process belongs to the asymmetric GARCH family (either EGARCH or GJR). The results suggest that there a…
Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks
2016
The continuously growing framework of information dynamics encompasses a set of tools, rooted in information theory and statistical physics, which allow to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of complex networks. Building on the most recent developments in this field, this work designs a complete approach to dissect the information carried by the target of a network of multiple interacting systems into the new information produced by the system, the information stored in the system, and the information transferred to it from the other systems; information storage and transfer are then further decomposed into amou…
A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks
2020
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on…
Spatio-Temporal Analysis of Suicide-Related Emergency Calls
2017
Considerable effort has been devoted to incorporate temporal trends in disease mapping. In this line, this work describes the importance of including the effect of the seasonality in a particular setting related with suicides. In particular, the number of suicide-related emergency calls is modeled by means of an autoregressive approach to spatio-temporal disease mapping that allows for incorporating the possible interaction between both temporal and spatial effects. Results show the importance of including seasonality effect, as there are differences between the number of suicide-related emergency calls between the four seasons of each year.
Simulating Term Structure of Interest Rates with Arbitrary Marginals
2007
Decision models under uncertainty need to be feeded with scenarios of the interest rate curve. Such scenarios have to comply, as close as possible, with the empirical distribution of each rate. Simulation models of the term structure usually assume that the conjugate distribution of the interest rates is lognormal. Dynamic models, like vector auto-regression, implicitly postulate that the logarithm of the interest rates is normally distributed. Statistical analyses have, however, shown that stationary transformations (yield changes) of the interest rates are substantially leptokurtic, thus posing serious doubts on the reliability of the available models. We propose in this paper a vector au…
Simulating term structure of interest rates with arbitrary marginals
2011
Decision models under uncertainty rely their analysis on scenarios of the economic factors. A key economic factor is the term structure of interest rates (yields). Simulation models of the yield curve usually assume that the conjugate distribution of the interest rates is lognormal. Dynamic models, like vector auto-regression, implicitly postulate that the logarithm of the interest rates is normally distributed. Statistical analyses have, however, shown that stationary transformations (yield changes) of the interest rates are substantially leptokurtic, thus posing serious doubts on the reliability of the available models. We propose in this paper a VARTA model (Biller and Nelson, 2003) to s…
Searching for Threshold Effects in the Evolution of Budget Deficits: An Application to the Spanish Case
2004
Abstract In this paper, we use recent developments on threshold autoregressive (TAR) models that allow us to derive endogenously threshold effects in the evolution of the Spanish budget deficit. Specifically, a mean-reverting dynamic behaviour of the budget deficit should be expected once such threshold is reached.
FISCAL READJUSTMENTS IN THE UNITED STATES: A NONLINEAR TIME-SERIES ANALYSIS
2009
We analyze the fiscal adjustment process in the United States using a multivariate threshold vector error regression model. The shift from single-equation to multivariate setting adds value both in terms of our economic understanding of the fiscal adjustment process and the forecasting performance of nonlinear models. We find evidence that fiscal authorities intervene to reduce real per capita deficit only when it reaches a certain threshold and that fiscal adjustment takes place primarily by cutting government expenditure. The results of out-of-sample density forecast and probability forecasts suggest that a shift from a univariate autoregressive model to a multivariate model improves fore…