11,812 research outputs found
Risk-sharing networks and farsighted stability
Evidence suggests that in developing countries, agents rely on mutual insurance agreements to deal with income or expenditure shocks. This paper analyzes which risk-sharing networks can be sustained in the long run when individuals are far- sighted, in the sense that they are able to forecast how other agents would react to their choice of insurance partners. In particular, we study whether the farsightedness of the agents leads to a reduction of the tension between stability and efficiency that arises when individuals are myopic. We find that for extreme values of the cost of establishing a mutual insurance agreement, myopic and farsighted agents form the same risk-sharing networks. For intermediate costs, farsighted agents form efficient networks while myopic agents don't.risk-sharing, networks, farsighted agents, stability, efficiency
Multiscale Bayesian State Space Model for Granger Causality Analysis of Brain Signal
Modelling time-varying and frequency-specific relationships between two brain
signals is becoming an essential methodological tool to answer heoretical
questions in experimental neuroscience. In this article, we propose to estimate
a frequency Granger causality statistic that may vary in time in order to
evaluate the functional connections between two brain regions during a task. We
use for that purpose an adaptive Kalman filter type of estimator of a linear
Gaussian vector autoregressive model with coefficients evolving over time. The
estimation procedure is achieved through variational Bayesian approximation and
is extended for multiple trials. This Bayesian State Space (BSS) model provides
a dynamical Granger-causality statistic that is quite natural. We propose to
extend the BSS model to include the \`{a} trous Haar decomposition. This
wavelet-based forecasting method is based on a multiscale resolution
decomposition of the signal using the redundant \`{a} trous wavelet transform
and allows us to capture short- and long-range dependencies between signals.
Equally importantly it allows us to derive the desired dynamical and
frequency-specific Granger-causality statistic. The application of these models
to intracranial local field potential data recorded during a psychological
experimental task shows the complex frequency based cross-talk between amygdala
and medial orbito-frontal cortex.
Keywords: \`{a} trous Haar wavelets; Multiple trials; Neuroscience data;
Nonstationarity; Time-frequency; Variational methods
The published version of this article is
Cekic, S., Grandjean, D., Renaud, O. (2018). Multiscale Bayesian state-space
model for Granger causality analysis of brain signal. Journal of Applied
Statistics. https://doi.org/10.1080/02664763.2018.145581
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