1,508 research outputs found
A State Dependent Regime Switching Model of Dynamic Correlations
Replaced with revised version of paper 07/29/09.dynamic correlations, regime switching, state dependent probabilities, thresholds, spillovers, Research Methods/ Statistical Methods,
Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
An autoregressive process with Markov regime is an autoregressive process for
which the regression function at each time point is given by a nonobservable
Markov chain. In this paper we consider the asymptotic properties of the
maximum likelihood estimator in a possibly nonstationary process of this kind
for which the hidden state space is compact but not necessarily finite.
Consistency and asymptotic normality are shown to follow from uniform
exponential forgetting of the initial distribution for the hidden Markov chain
conditional on the observations.Comment: Published at http://dx.doi.org/10.1214/009053604000000021 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data
Deducing the structure of neural circuits is one of the central problems of
modern neuroscience. Recently-introduced calcium fluorescent imaging methods
permit experimentalists to observe network activity in large populations of
neurons, but these techniques provide only indirect observations of neural
spike trains, with limited time resolution and signal quality. In this work we
present a Bayesian approach for inferring neural circuitry given this type of
imaging data. We model the network activity in terms of a collection of coupled
hidden Markov chains, with each chain corresponding to a single neuron in the
network and the coupling between the chains reflecting the network's
connectivity matrix. We derive a Monte Carlo Expectation--Maximization
algorithm for fitting the model parameters; to obtain the sufficient statistics
in a computationally-efficient manner, we introduce a specialized
blockwise-Gibbs algorithm for sampling from the joint activity of all observed
neurons given the observed fluorescence data. We perform large-scale
simulations of randomly connected neuronal networks with biophysically
realistic parameters and find that the proposed methods can accurately infer
the connectivity in these networks given reasonable experimental and
computational constraints. In addition, the estimation accuracy may be improved
significantly by incorporating prior knowledge about the sparseness of
connectivity in the network, via standard L penalization methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Mixed membership stochastic blockmodels
Observations consisting of measurements on relationships for pairs of objects
arise in many settings, such as protein interaction and gene regulatory
networks, collections of author-recipient email, and social networks. Analyzing
such data with probabilisic models can be delicate because the simple
exchangeability assumptions underlying many boilerplate models no longer hold.
In this paper, we describe a latent variable model of such data called the
mixed membership stochastic blockmodel. This model extends blockmodels for
relational data to ones which capture mixed membership latent relational
structure, thus providing an object-specific low-dimensional representation. We
develop a general variational inference algorithm for fast approximate
posterior inference. We explore applications to social and protein interaction
networks.Comment: 46 pages, 14 figures, 3 table
Negative volatility spillovers in the unrestricted ECCC-GARCH model
Copyright @ 2010 Cambridge University Press.This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the parameters of the model are nonnegative, which is a sufficient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative feedback, we show that the positive definiteness of the conditional covariance matrix can be guaranteed even if some of the parameters are negative. Thus, we extend the results of Nelson and Cao (1992) and Tsai and Chan (2008) to a multivariate setting. For the bivariate case of order one, we look into the consequences of adopting these less severe restrictions and find that the flexibility of the process is substantially increased. Our results are helpful for the model-builder, who can consider the unrestricted formulation as a tool for testing various economic theories
Inference on causal and structural parameters using many moment inequalities
This paper considers the problem of testing many moment inequalities where
the number of moment inequalities, denoted by , is possibly much larger than
the sample size . There is a variety of economic applications where solving
this problem allows to carry out inference on causal and structural parameters,
a notable example is the market structure model of Ciliberto and Tamer (2009)
where with being the number of firms that could possibly enter
the market. We consider the test statistic given by the maximum of
Studentized (or -type) inequality-specific statistics, and analyze various
ways to compute critical values for the test statistic. Specifically, we
consider critical values based upon (i) the union bound combined with a
moderate deviation inequality for self-normalized sums, (ii) the multiplier and
empirical bootstraps, and (iii) two-step and three-step variants of (i) and
(ii) by incorporating the selection of uninformative inequalities that are far
from being binding and a novel selection of weakly informative inequalities
that are potentially binding but do not provide first order information. We
prove validity of these methods, showing that under mild conditions, they lead
to tests with the error in size decreasing polynomially in while allowing
for being much larger than , indeed can be of order
for some . Importantly, all these results hold without any restriction
on the correlation structure between Studentized statistics, and also hold
uniformly with respect to suitably large classes of underlying distributions.
Moreover, in the online supplement, we show validity of a test based on the
block multiplier bootstrap in the case of dependent data under some general
mixing conditions.Comment: This paper was previously circulated under the title "Testing many
moment inequalities
The velocity distribution of nearby stars from Hipparcos data I. The significance of the moving groups
We present a three-dimensional reconstruction of the velocity distribution of
nearby stars (<~ 100 pc) using a maximum likelihood density estimation
technique applied to the two-dimensional tangential velocities of stars. The
underlying distribution is modeled as a mixture of Gaussian components. The
algorithm reconstructs the error-deconvolved distribution function, even when
the individual stars have unique error and missing-data properties. We apply
this technique to the tangential velocity measurements from a kinematically
unbiased sample of 11,865 main sequence stars observed by the Hipparcos
satellite. We explore various methods for validating the complexity of the
resulting velocity distribution function, including criteria based on Bayesian
model selection and how accurately our reconstruction predicts the radial
velocities of a sample of stars from the Geneva-Copenhagen survey (GCS). Using
this very conservative external validation test based on the GCS, we find that
there is little evidence for structure in the distribution function beyond the
moving groups established prior to the Hipparcos mission. This is in sharp
contrast with internal tests performed here and in previous analyses, which
point consistently to maximal structure in the velocity distribution. We
quantify the information content of the radial velocity measurements and find
that the mean amount of new information gained from a radial velocity
measurement of a single star is significant. This argues for complementary
radial velocity surveys to upcoming astrometric surveys
MACROECONOMIC SHOCKS, HUMAN CAPITAL AND PRODUCTIVE EFFICIENCY: EVIDENCE FROM WEST AFRICAN FARMERS
Little empirical work has quantified the transitory effects of macroeconomic shocks on farm-level production behavior. We develop a simple analytical model to explain how macroeconomic shocks might temporarily divert managerial attention, thereby affecting farm-level productivity, but perhaps to different degrees and for different durations across production units. We then successfully test hypotheses from that model using panel data bracketing massive currency devaluation in the west African nation of Cote d'Ivoire. We find a transitory increase in mean plot-level technical inefficiency among Ivorien rice producers and considerable variation in the magnitude and persistence of this effect, attributable largely to ex ante complexity of operations, and the educational attainment and off-farm employment status of the plot manager.Labor and Human Capital, O1, Q12, Q18,
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