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Modeling gene expression regulatory networks with the sparse vector autoregressive model

Fujita, André ; Sato, João R; Garay-Malpartida, Humberto M; Yamaguchi, Rui ; Miyano, Satoru ; Sogayar, Mari C; Ferreira, Carlos E
Fonte: Biblioteca Digital da Produção Intelectual da USP Publicador: Biblioteca Digital da Produção Intelectual da USP
Tipo: Artigo de Revista Científica
Português
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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...

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Fujita, André; Sato, João R; Garay-Malpartida, Humberto M; Yamaguchi, Rui; Miyano, Satoru; Sogayar, Mari C; Ferreira, Carlos E
Fonte: Biblioteca Digital da Produção Intelectual da USP Publicador: Biblioteca Digital da Produção Intelectual da USP
Tipo: Artigo de Revista Científica
Português
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46.46%
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...

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Fujita, André; Sato, João R; Garay-Malpartida, Humberto M; Yamaguchi, Rui; Miyano, Satoru; Sogayar, Mari C; Ferreira, Carlos E
Fonte: Biblioteca Digital da Produção Intelectual da USP Publicador: Biblioteca Digital da Produção Intelectual da USP
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.46%
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...

Impacto da política monetária nas principais variáveis macroeconómicas em Portugal

Tavares, Patrícia Afonso
Fonte: Instituto Universitário de Lisboa Publicador: Instituto Universitário de Lisboa
Tipo: Dissertação de Mestrado
Publicado em //2011 Português
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Mestrado em Finanças; The purpose of this paper is to shed light on how monetary policy decisions taken by the European Central Bank (ECB), the most important decision-maker of economic policy in the Euro Zone, affect the main macroeconomic variables in Portugal. A set of 5 variables was considered to be representative of the economic reality of the country and, because of this, are able to affect, directly or indirectly, all economic agents. The variables are: short-term interest rates, equity prices, consumer price index, real gross domestic product and residential property prices. Such monetary policy decisions are referred, especially, to changes in ECB reference rate (refi rate), its most important instrument when trying to cope with its main objective – guarantee financial stability. To do such work, is important to check whether the Portuguese financial system is an open system. To do that, its main features were analyzed, because this has a great influence on the effectiveness of the monetary transmission mechanism described above. Through a Vector Autoregressive (VAR) Model, we estimated the interdependence between the different variables considered. After that, and by introducing a shock in short-term interest rate...

Estimación de la elasticidad precio de la demanda un ejercicio para el consumo de agua residencial en Bogotá

Trout Lastra, Camilo Alfonso; Villegas Restrepo, Daniel
Fonte: Pontifícia Universidade Javeriana Publicador: Pontifícia Universidade Javeriana
Tipo: bachelorThesis; Trabajo de grado Formato: PDF
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Este documento tiene como objetivo analizar la estimación de la elasticidad de corto y largo plazo de la demanda residencial de agua para la ciudad de Bogotá (Colombia) en el periodo 2004M1:2011M12. El análisis es basado con el método de variables rezagadas sobre si misma, VAR (Vector Auto-Regresivo) y un test de impulso respuesta para identificar choques entre variables a medida que se extienden los horizontes de pronóstico. El propósito de este trabajo es comprobar la inelasticidad de la demanda de agua residencial y ver cómo cambian los hábitos de consumo ante choques de las variables explicativas.; This paper aims to analyze the elasticity in a short and long-term water demand for the city´s residential area from 2004M1:2011M12. This analysis is based on the VAR (Vector Autoregressive) method and a test to identify changes between variables throughout the forecast horizon. The purpose of this work is to prove the inelasticity of demand for residential water consumption and observe the changes in the consumer habits according to the variations the explanatory variables.

The role of portfolio shocks in a structural vector autoregressive model of the Australian economy*

Fry, Renée; Hocking, James; Martin, Vance L
Fonte: Wiley Publicador: Wiley
Tipo: Artigo de Revista Científica Formato: 17 pages
Português
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Domestic and foreign equity shocks on the Australian economy are analysed within a five-variate structural vector autoregressive model, with identification achieved through long-run restrictions based on the natural rate hypothesis, monetary neutrality, long-run portfolio balance and purchasing power parity. The results show that real equity values were undervalued by 19 per cent by June 2005, with the gap narrowing thereafter. Foreign crises are important factors explaining this deterioration. The real wealth effects of equity market shocks impact significantly upon financial and goods market prices, whereas output tends to be immune. The model is also able to address puzzles that exist in the vector autoregression literature.; Fry acknowledges funding from ARC Discovery Project Grant DP0343418, and Martin acknowledges funding from ARC Discovery Project Grant DP0208669.

Econometric Analysis with Vector Autoregressive Models

LUETKEPOHL, Helmut
Fonte: European University Institute Publicador: European University Institute
Tipo: Trabalho em Andamento Formato: 545232 bytes; application/pdf; digital
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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.

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

KASCHA, Christian
Fonte: European University Institute Publicador: European University Institute
Tipo: Trabalho em Andamento Formato: 899626 bytes; application/pdf; digital
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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.

A Statistical Comparison of Alternative Identification Schemes for Monetary Policy Shocks

LANNE, Markku; LUETKEPOHL, Helmut
Fonte: European University Institute Publicador: European University Institute
Tipo: Trabalho em Andamento Formato: application/pdf; digital
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Abstract. Different identification schemes for monetary policy shocks have been proposed in the literature. They typically specify just-identifying re- strictions in a standard structural vector autoregressive (SVAR) framework. Thus, in this framework the different schemes cannot be checked against the data with statistical tests. We consider different approaches how to use the data properties to augment the standard SVAR setup for identifying the shocks. Thereby it becomes possible to test models which are just identified in a standard setting. For monthly US data it is found that a model where monetary shocks are induced via the federal funds rate is the only one which cannot be rejected when the data properties are used for identification.

Structural Vector Autoregressions with Markov Switching

LANNE, Markku; LUETKEPOHL, Helmut; MACIEJOWSKA, Katarzyna
Fonte: European University Institute Publicador: European University Institute
Tipo: Trabalho em Andamento Formato: application/pdf; digital
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Abstract. It is argued that in structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. The model setup is formulated and discussed and it is shown how it can be used to test restrictions which are just-identifying in a standard structural vector autoregressive analysis. The approach is illustrated by two SVAR examples which have been reported in the literature and which have features which can be accommodated by the MS structure.

Structural Vector Autoregressions with Markov Switching: Combining conventional with statistical identification of shocks

HERWARTZ, Helmut; LUETKEPOHL, Helmut
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Trabalho em Andamento Formato: application/pdf; digital
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In structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. Unfortunately, these shocks may not have a meaningful structural economic interpretation. It is discussed how statistical and conventional identifying information can be combined. The discussion is based on a VAR model for the US containing oil prices, output, consumer prices and a short-term interest rate. The sys- tem has been used for studying the causes of the early millennium economic slowdown based on traditional identification with zero and long-run restric- tions and using sign restrictions. We find that previously drawn conclusions are questionable in our framework.

Vector Autoregressive Models

LUETKEPOHL, Helmut
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Trabalho em Andamento Formato: application/pdf; digital
Português
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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)...

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

DROUMAGUET, Matthieu
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Tese de Doutorado Formato: application/pdf; digital
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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...

Testing for the Cointegrating Rank of a Vector Autoregressive Process with Uncertain Deterministic Trend Term

DEMETRESCU, Matei; LUETKEPOHL, Helmut; SAIKKONEN, Pentti
Fonte: Wiley-Blackwell Publishing, Inc Publicador: Wiley-Blackwell Publishing, Inc
Tipo: Artigo de Revista Científica
Português
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P>When applying Johansen's procedure for determining the cointegrating rank to systems of variables with linear deterministic trends, there are two possible tests to choose from. One test allows for a trend in the cointegration relations and the other one restricts the trend to being orthogonal to the cointegration relations. The first test is known to have reduced power relative to the second one if there is in fact no trend in the cointegration relations, whereas the second one is based on a misspecified model if the linear trend is not orthogonal to the cointegration relations. Hence, the treatment of the linear trend term is crucial for the outcome of the rank determination procedure. We compare three alternative procedures, which are applicable if there is uncertainty regarding the proper trend specification. In the first one a specific cointegrating rank is rejected if one of the two tests rejects, in the second one the trend term is decided upon by a pretest and in the third procedure only tests which allow for an unrestricted trend term are used. We provide theoretical asymptotic and small sample simulation results, which show that the first strategy is preferable in applied work.

Common dynamics of nonenergy commodity prices and their relation to uncertainty

Sierra Suárez, Lya Paola; Poncela, Pilar; Senra, Eva
Fonte: Taylor & Francis Publicador: Taylor & Francis
Tipo: info:eu-repo/semantics/article; Artículo; info:eu-repo/semantics/publishedVersion Formato: application/pdf; 13 p.
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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...

Ciclo econômico, emprego e desigualdade; Texto para Discussão (TD) 1981: Ciclo econômico, emprego e desigualdade; Economic cycle, employment and inequality

Cavalcanti, Marco A. F. H.; Moreira, Ajax R. B.
Fonte: Instituto de Pesquisa Econômica Aplicada (Ipea) Publicador: Instituto de Pesquisa Econômica Aplicada (Ipea)
Tipo: Texto para Discussão (TD)
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Este trabalho analisa, para o caso brasileiro, os condicionantes transitórios dos níveis de emprego e distribuição de renda do país, utilizando os microdados da Pesquisa Mensal de Emprego (PME) produzida pelo Instituto Brasileiro de Geografia e Estatística (IBGE) para avaliar os efeitos de choques macroeconômicos sobre o emprego e a desigualdade de renda de diferentes segmentos sociais, com foco na possível heterogeneidade destes efeitos. Consideram-se oito segmentos sociais, diferenciados segundo três atributos: gênero, experiência e educação. Para relacionar os indicadores sociais de emprego/desigualdade às variáveis macroeconômicas, adotam-se dois tipos de abordagem econométrica: modelos autorregressivos vetoriais padrão – Vector Autoregressive Model (VAR) – e modelos autorregressivos vetorais de fatores – Factor-Augmented Vector Autoregressive (Favar) –, nos quais os choques macroeconômicos são identificados utilizando restrições de sinal inspiradas em modelos dinâmicos estocásticos de equilíbrio geral – Dynamic Stochastic General Equilibrium (DSGE). A comparação entre os resultados das metodologias VAR e Favar permite avaliar em que medida a consideração da possível heterogeneidade dos efeitos pode alterar resultados qualitativos da relação entre os choques macroeconômicos e os indicadores sociais. Os principais resultados do trabalho são que o efeito dos choques identificados: i) é estatisticamente significativo apenas para os modelos Favar...

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

Seghouane, Abd-Krim
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Português
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56.53%
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

Forecasting with time-varying vector autoregressive models

Triantafyllopoulos, K.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
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46.39%
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...

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

Shah, Adnan; Khalid, Muhammad; Seghouane, Abd-Krim
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Português
Relevância na Pesquisa
46.58%
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.

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

Shah, Adnan; Khalid, Muhammad; Seghouane, Abd-Krim
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.58%
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.