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- BIOMED CENTRAL LTD; LONDON
- Biblioteca Digital da Produção Intelectual da USP
- European University Institute
- Instituto Universitário Europeu
- Taylor & Francis
- Hindawi Publishing Corporation
- Inderscience Publishers
- Institute of Electrical and Electronics Engineers (IEEE Inc)
- Universidade Cornell
- Instituto Superior de Economia e Gestão
- Universidad de Guadalajara
- Mais Publicadores...

## Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Fonte: BIOMED CENTRAL LTD; LONDON
Publicador: BIOMED CENTRAL LTD; LONDON

Tipo: Artigo de Revista Científica

ENG

Relevância na Pesquisa

56%

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables...

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## Modeling gene expression regulatory networks with the sparse vector autoregressive model

Fonte: Biblioteca Digital da Produção Intelectual da USP
Publicador: Biblioteca Digital da Produção Intelectual da USP

Tipo: Artigo de Revista Científica

ENG

Relevância na Pesquisa

46.18%

Abstract
Background
To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems.
Results
We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations...

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## Econometric Analysis with Vector Autoregressive Models

Fonte: European University Institute
Publicador: European University Institute

Tipo: Trabalho em Andamento
Formato: 545232 bytes; application/pdf; digital

EN

Relevância na Pesquisa

56.17%

Vector autoregressive (VAR) models for stationary and integrated variables are
reviewed. Model specification and parameter estimation are discussed and various uses of
these models for forecasting and economic analysis are considered. For integrated and cointegrated variables it is argued that vector error correction models offer a particularly convenient parameterization both for model specification and for using the models for economic
analysis.

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## A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models

Fonte: European University Institute
Publicador: European University Institute

Tipo: Trabalho em Andamento
Formato: 899626 bytes; application/pdf; digital

EN

Relevância na Pesquisa

66.23%

Classical Gaussian maximum likelihood estimation of mixed vector autoregressive moving-average models is plagued with various numerical problems and has been considered difficult by many applied researchers. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. Therefore, several other, simpler estimation methods have been proposed in the literature. In this paper these methods are compared by means of a Monte Carlo study. Different evaluation criteria are used to judge the relative performances of the algorithms.

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## Testing Exact Rational Expectations in Cointegrated Vector Autoregressive Models

Fonte: Instituto Universitário Europeu
Publicador: Instituto Universitário Europeu

Tipo: Artigo de Revista Científica

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56%

This paper considers the testing of restrictions implied by rational expectations hypotheses in a cointegrated vector autoregressive model for I(1) variables. If the rational expectations involve one-step-ahead observations only and the coefficients are known, an explicit parameterization of the restrictions is found, and the maximum-likelihood estimator is derived by regression and reduced rank regression. An application is given to a present value model.

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## Vector Autoregressive Models

Fonte: Instituto Universitário Europeu
Publicador: Instituto Universitário Europeu

Tipo: Trabalho em Andamento
Formato: application/pdf; digital

EN

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56.24%

Multivariate simultaneous equations models were used extensively for macroeconometric analysis when Sims (1980) advocated vector autoregressive (VAR) models as alternatives. At that time longer and more frequently observed macroeconomic time series called for models which described the dynamic structure of the variables. VAR models lend themselves for this purpose. They typically treat all variables as a priori endogenous. Thereby they account for Sims’ critique that the exogeneity assumptions for some of the variables in simultaneous equations models are ad hoc and often not backed by fully developed theories. Restrictions, including exogeneity of some of the variables, may be imposed on VAR models based on statistical procedures. VAR models are natural tools for forecasting. Their setup is such that current values of a set of variables are partly explained by past values of the variables involved. They can also be used for economic analysis, however, because they describe the joint generation mechanism of the variables involved. Structural VAR analysis attempts to investigate structural economic hypotheses with the help of VAR models. Impulse response analysis, forecast error variance decompositions, historical decompositions and the analysis of forecast scenarios are the tools which have been proposed for disentangling the relations between the variables in a VAR model. Traditionally VAR models are designed for stationary variables without time trends. Trending behavior can be captured by including deterministic polynomial terms. In the 1980s the discovery of the importance of stochastic trends in economic variables and the development of the concept of cointegration by Granger (1981)...

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## Markov-Switching Vector Autoregressive Models: Monte Carlo experiment, impulse response analysis, and Granger-Causal analysis

Fonte: Instituto Universitário Europeu
Publicador: Instituto Universitário Europeu

Tipo: Tese de Doutorado
Formato: application/pdf; digital

EN

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66.26%

This dissertation has for prime theme the exploration of nonlinear econometric models featuring a hidden Markov chain. Occasional and discrete shifts in regimes generate convenient nonlinear dynamics to econometric models, allowing for structural changes similar to the exogenous economic events occurring in reality. The first paper sets up a Monte Carlo experiment to explore the finite-sample properties of the estimates of vector autoregressive models subject to switches in regime governed by a hidden Markov chain. The main finding of this article is that the accuracy with which regimes are determined by the Expectation Maximixation algorithm shows improvement when the dimension of the simulated series increases. However this gain comes at the cost of higher sample size requirements for models with more variables. The second paper advocates the use of Bayesian impulse responses for a Markovswitching Vector Autoregressive model. These responses are sensitive to the Markovswitching properties of the model and, based on densities, allow statistical inference to be conducted. Upon the premise of structural changes occurring on oil markets, the empirical results of Kilan (2009) are reinvestigated. The effects of the structural shocks are characterized over four estimated regimes. Over time...

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## Common dynamics of nonenergy commodity prices and their relation to uncertainty

Fonte: Taylor & Francis
Publicador: Taylor & Francis

Tipo: info:eu-repo/semantics/article; Artículo; info:eu-repo/semantics/publishedVersion
Formato: application/pdf; 13 p.

SPA

Relevância na Pesquisa

56.12%

The purpose of this article is to improve the empirical evidence on commodity
prices in various dimensions. First, we attempt to identify the extent of comovements
in 44 monthly nonenergy commodity price series in order to ascertain
whether the increase in comovement is a recent term phenomenon. Second, we
attempt to determine the role of uncertainty in determining comovements among
nonenergy prices in the short run. We diagnose the overall comovement using a
dynamic factor model estimated by principal components. A factor-augmented
vector autoregressive approach is used to assess the relationship of fundamentals,
financial and uncertainty variables with the comovement in commodity prices.
We find a greater synchronization among raw materials since December 2003.
Since that date, uncertainty has played an important role in determining short-run
fluctuations in nonenergy raw material prices.; The purpose of this article is to improve the empirical evidence on commodity
prices in various dimensions. First, we attempt to identify the extent of comovements
in 44 monthly nonenergy commodity price series in order to ascertain
whether the increase in comovement is a recent term phenomenon. Second, we
attempt to determine the role of uncertainty in determining comovements among
nonenergy prices in the short run. We diagnose the overall comovement using a
dynamic factor model estimated by principal components. A factor-augmented
vector autoregressive approach is used to assess the relationship of fundamentals...

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## Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting

Fonte: Hindawi Publishing Corporation
Publicador: Hindawi Publishing Corporation

Tipo: Artigo de Revista Científica

EN

Relevância na Pesquisa

56%

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.

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## An innovative kernel-based recursive time-series learning algorithm with applications to improvements of beehive management practices

Fonte: Inderscience Publishers
Publicador: Inderscience Publishers

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

56.06%

#Keywords: algorithm#apiculture#foraging behavior#modeling#time series analysis#Apis mellifera#Apoidea Beehive management#Innovation#Learning algorithm#Subset vector autoregressive modelling

In this paper, we propose an innovative kernel-based learning algorithm to sequentially estimate subset vector autoregressive models (including full-order models). To demonstrate the effectiveness of the proposed recursive algorithm, we apply this algorit

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## Vector Autoregressive Model-Order Selection From Finite Samples Using Kullback's Symmetric Divergence

Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc)
Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

56.28%

#Keywords: Computer simulation#Mathematical models#Numerical methods#Vectors#Autoregressive (AR) models#Kullback information criterion (KIC)#Kullback-Leibler information#Model selection#Symmetric divergence#Information theory Autoregressive (AR) models#KICc

In this paper, a new small-sample model selection criterion for vector autoregressive (VAR) models is developed. The proposed criterion is named Kullback information criterion (KICvc), where the notation vc stands for vector correction, and it can be cons

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## Learning vector autoregressive models with focalised Granger-causality graphs

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 07/07/2015

Relevância na Pesquisa

66.11%

While the importance of Granger-causal (G-causal) relationships for learning
vector autoregressive models (VARs) is widely acknowledged, the
state-of-the-art VAR methods do not address the problem of discovering the
underlying G-causality structure in a principled manner. VAR models can be
restricted if such restrictions are supported by a strong domain theory (e.g.
economics), but without such strong domain-driven constraints the existing VAR
methods typically learn fully connected models where each series is G-caused by
all the others. We develop new VAR methods that address the problem of
discovering structure in the G-causal relationships explicitly. Our methods
learn sparse G-causality graphs with small sets of \emph{focal} series that
govern the dynamical relationships within the time-series system. While
maintaining competitive forecasting accuracy, the sparsity in the G-causality
graphs that our methods achieve is far from reach of any of the
state-of-the-art VAR methods.

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## Adaptive estimation of vector autoregressive models with time-varying variance: application to testing linear causality in mean

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

56.09%

Linear Vector AutoRegressive (VAR) models where the innovations could be
unconditionally heteroscedastic and serially dependent are considered. The
volatility structure is deterministic and quite general, including breaks or
trending variances as special cases. In this framework we propose Ordinary
Least Squares (OLS), Generalized Least Squares (GLS) and Adaptive Least Squares
(ALS) procedures. The GLS estimator requires the knowledge of the time-varying
variance structure while in the ALS approach the unknown variance is estimated
by kernel smoothing with the outer product of the OLS residuals vectors.
Different bandwidths for the different cells of the time-varying variance
matrix are also allowed. We derive the asymptotic distribution of the proposed
estimators for the VAR model coefficients and compare their properties. In
particular we show that the ALS estimator is asymptotically equivalent to the
infeasible GLS estimator. This asymptotic equivalence is obtained uniformly
with respect to the bandwidth(s) in a given range and hence justifies
data-driven bandwidth rules. Using these results we build Wald tests for the
linear Granger causality in mean which are adapted to VAR processes driven by
errors with a non stationary volatility. It is also shown that the commonly
used standard Wald test for the linear Granger causality in mean is potentially
unreliable in our framework. Monte Carlo experiments illustrate the use of the
different estimation approaches for the analysis of VAR models with stable
innovations.

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## Forecasting with time-varying vector autoregressive models

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

56.17%

The purpose of this paper is to propose a time-varying vector autoregressive
model (TV-VAR) for forecasting multivariate time series. The model is casted
into a state-space form that allows flexible description and analysis. The
volatility covariance matrix of the time series is modelled via inverted
Wishart and singular multivariate beta distributions allowing a fully conjugate
Bayesian inference. Model performance and model comparison is done via the
likelihood function, sequential Bayes factors, the mean of squared standardized
forecast errors, the mean of absolute forecast errors (known also as mean
absolute deviation), and the mean forecast error. Bayes factors are also used
in order to choose the autoregressive order of the model. Multi-step
forecasting is discussed in detail and a flexible formula is proposed to
approximate the forecast function. Two examples, consisting of bivariate data
of IBM shares and of foreign exchange (FX) rates for 8 currencies, illustrate
the methods. For the IBM data we discuss model performance and multi-step
forecasting in some detail. For the FX data we discuss sequential portfolio
allocation; for both data sets our empirical findings suggest that the TV-VAR
models outperform the widely used VAR models.; Comment: 17 pages...

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## Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 29/06/2013

Relevância na Pesquisa

56%

We study sparse principal component analysis for high dimensional vector
autoregressive time series under a doubly asymptotic framework, which allows
the dimension $d$ to scale with the series length $T$. We treat the transition
matrix of time series as a nuisance parameter and directly apply sparse
principal component analysis on multivariate time series as if the data are
independent. We provide explicit non-asymptotic rates of convergence for
leading eigenvector estimation and extend this result to principal subspace
estimation. Our analysis illustrates that the spectral norm of the transition
matrix plays an essential role in determining the final rates. We also
characterize sufficient conditions under which sparse principal component
analysis attains the optimal parametric rate. Our theoretical results are
backed up by thorough numerical studies.; Comment: 28 pages

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## Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 11/11/2015

Relevância na Pesquisa

56%

#Statistics - Methodology#Mathematics - Statistics Theory#Physics - Data Analysis, Statistics and Probability#Statistics - Computation#Statistics - Machine Learning

Granger causality has been used for the investigation of the inter-dependence
structure of the underlying systems of multi-variate time series. In
particular, the direct causal effects are commonly estimated by the conditional
Granger causality index (CGCI). In the presence of many observed variables and
relatively short time series, CGCI may fail because it is based on vector
autoregressive models (VAR) involving a large number of coefficients to be
estimated. In this work, the VAR is restricted by a scheme that modifies the
recently developed method of backward-in-time selection (BTS) of the lagged
variables and the CGCI is combined with BTS. Further, the proposed approach is
compared favorably to other restricted VAR representations, such as the
top-down strategy, the bottom-up strategy, and the least absolute shrinkage and
selection operator (LASSO), in terms of sensitivity and specificity of CGCI.
This is shown by using simulations of linear and nonlinear, low and
high-dimensional systems and different time series lengths. For nonlinear
systems, CGCI from the restricted VAR representations are compared with
analogous nonlinear causality indices. Further, CGCI in conjunction with BTS
and other restricted VAR representations is applied to multi-channel scalp
electroencephalogram (EEG) recordings of epileptic patients containing
epileptiform discharges. CGCI on the restricted VAR...

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## Comparing causality measures of fMRI data using PCA, CCA and vector autoregressive modelling

Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc)
Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)

Tipo: Conference paper

Relevância na Pesquisa

46.2%

Extracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using two measures; one derived based on univariate autoregressive and autoregressive exogenous (AR/ARX) and other derived based on multivariate vector autoregressive and vector autoregressive exogenous (VAR/VARX) models. The significance and effectiveness of these measures is illustrated on both simulated and real fMRI data sets. It has been revealed that VAR modelling of the regions of interest is robust in inferring true causality compared to principal component analysis (PCA) and canonical correlation analysis (CCA) based causality methods.

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## Comparing causality measures of fMRI data using PCA, CCA and vector autoregressive modelling.

Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc)
Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

46.2%

Extracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using two measures; one derived based on univariate autoregressive and autoregressive exogenous (AR/ARX) and other derived based on multivariate vector autoregressive and vector autoregressive exogenous (VAR/VARX) models. The significance and effectiveness of these measures is illustrated on both simulated and real fMRI data sets. It has been revealed that VAR modelling of the regions of interest is robust in inferring true causality compared to principal component analysis (PCA) and canonical correlation analysis (CCA) based causality methods.

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## Análise da modelação dos preços do mercado de habitação na área de Lisboa entre 1972 e 2011

Fonte: Instituto Superior de Economia e Gestão
Publicador: Instituto Superior de Economia e Gestão

Tipo: Dissertação de Mestrado

Publicado em //2012
POR

Relevância na Pesquisa

66.06%

#Índice de Preços da Habitação#Variáveis Macroeconómicas#Causalidade à Granger#Modelos Vetoriais Autorregressivos (VAR)#Price Index of Housing#Macroeconomic Variables#Granger Causality#Vector Autoregressive Models

Mestrado em Gestão e Avaliação Imobiliária; O propósito deste estudo é investigarmos empiricamente os determinantes que influenciaram a formação do preço da habitação em Portugal. A evolução dos preços da habitação em Portugal reveste-se de grande importância para os profissionais do sector. Conhecer, estudar e analisar a evolução deste mercado ao longo dos últimos anos permite aos profissionais tomar decisões fundamentadas em análises profundas e cuidadas sobre quais foram os determinantes que influenciaram a procura e a oferta que por sua vez determinaram os preços. Pretendemos conhecer o comportamento do mercado imobiliário e qual a sua relação de causalidade com as variáveis macroeconómicas que influenciam o desenvolvimento económico do país. Usamos modelos Vetoriais Autorregressivos (VAR) para identificar os principais fatores macroeconómicos que influenciaram a formação dos preços ao longo dos últimos vinte e seis anos. Para a análise utilizamos dados trimestrais, referentes ao período de 1985 a 2011 e observámos as variáveis: Índice de Preços da Habitação (IPH), Produto Interno Bruto, Rendimento Disponível dos particulares, Taxa de desemprego e Taxa de Juro Implícitas no crédito hipotecário. Os resultados empíricos obtidos evidenciaram que existe uma relação de causalidade entre os preços da habitação e o PIB e a Taxa de Juro aplicada ao crédito hipotecário. O teste de causalidade à Granger revelou não existir relação de causalidade entre o Índice de Preços da Habitação e as variáveis Rendimento disponível dos particulares e Taxa de desemprego.; The purpose of this study is to empirically investigate the determinants that influenced the formation of the housing price in Portugal. The evolution of housing prices in Portugal is of great importance to industry professionals. Knowing...

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## Generalized impulse response analysis: General or Extreme?

Fonte: Universidad de Guadalajara
Publicador: Universidad de Guadalajara

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/12/2013
EN

Relevância na Pesquisa

56.06%

#Generalized Impulse Response Function#Orthogonalized Impulse Response Function#Vector Autoregressive Models

This note discusses a pitfall of using the generalized impulse response function (GIRF) in vector autoregressive (VAR) models (Pesaran and Shin, 1998). The GIRF is general because it is invariant to the ordering of the variables in the VAR. The GIRF, in fact, is extreme because it yields a set of response functions that are based on extreme identifying assumptions that contradict each other, unless the covariance matrix is diagonal. With a help of empirical examples, the present note demonstrates that the GIRF may yield quite misleading economic inferences.

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