Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

A conditional likelihood ratio test for structural models

This paper develops a general method for constructing similar tests based on the conditional distribution of nonpivotal statistics in a simultaneous equations model with normal errors and known reducedform covariance matrix. The test based on the likelihood ratio statistic is particularly simple and has good power properties. When identification is strong, the power curve of this conditional likelihood ratio test is essentially equal to the power envelope for similar tests. Monte Carlo simulations also suggest that this test dominates the Anderson- Rubin test and the score test. Dropping the restrictive assumption of disturbances normally distributed with known covariance matrix, approximate conditional tests are found that behave well in small samples even when identification is weak.

Comparative analysis between a CAD model design and physical models obtained by manufacturing additive technologies using optical scan

This article presents a detailed study of the application of different additive manufacturing technologies (sintering process, three-dimensional printing, extrusion and stereolithographic process), in the design process of a complex geometry model and its moving parts. The fabrication sequence was evaluated in terms of pre-processing conditions (model generation and model STL SLI), generation strategy and physical model post-processing operations. Dimensional verification of the obtained models was undertook by projecting structured light (optical scan), a relatively new technology of main importance for metrology and reverse engineering. Studies were done in certain manufacturing time and production costs, which allowed the definition of an more comprehensive evaluation matrix of additive technologies.

Short communication: Principal components and factor analytic models for test-day milk yield in Brazilian Holstein cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The Yang-Lee edge singularity in spin models on connected and non-connected rings

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Generalized partition function zeros of 1D spin models and their critical behavior at edge singularities

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Critical behavior at edge singularities in one-dimensional spin models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Experimental poisoning by Senecio brasiliensis in calves: quantitative and semi-quantitative study on changes in the hepatic extracellular matrix and sinusoidal cells

A matriz extracelular (MEC) desempenha um papel importante em lesões hepáticas crônicas e tem sido estudada em modelos de intoxicação experimental. em bovinos, no entanto, não há estudos específicos sobre a MEC hepática normal ou com lesões crônicas. Por isso, foi desenvolvido um modelo de intoxicação experimental hepático usando Senecio brasilliensis, uma planta que contém alcalóides pirrolizidínicos e causa lesão hepática dependente da dose. Cinco bezerros receberam por via oral, 0.38g/kg de folhas secas por 24 dias. Biópsias hepáticas foram obtidas a cada 15 dias durante 60 dias. Sinais clínicos de complicações digestivas surgiram da terceira semana do experimento. Um bezerro morreu aos 45 dias e os outros quatro foram avaliados até os 60 dias. As biópsias hepáticas foram processadas para microscopia óptica, imuno-histoquímica e microscopia eletrônica de transmissão. No trigésimo dia, as lesões hepáticas eram progessivas caracterizadas por vacuolização hepatocelular, necrose, apoptose, megalocitose, e fibrose centrolobular, pericelular e portal. Foram realizadas avaliações quantitativas e semi-quantitativas de componentes da MEC hepática antes e após o aparecimento das lesões. Foi realizada morfometria do colágeno total e do sistema de fibras elásticas. Colágeno total e colágenos tipos I e III aumentaram progressivamente em todos os locais do fígado. Mudanças na localização, quantidade e disposição do sistema de fibras elásticas foram também observadas. Houve um aumento significativo de células de Kupffer aos 30 dias e de células sinusoidais totais aos 45 e 60 dias. As lesões hepáticas neste experimento foram progressivas mesmo após a remoção da planta. Lesões de fibrose severa foram localizadas principalmente nos espaços porta, seguido por fibrose veno-oclusiva e pericelular. Os colágenos tipo I e tipo III foram observados no fígado normal e no fígado dos bezerros afetados, com predomínio do tipo I. Nos bezerros afetados o aumento do colágeno total e do sistema de fibras elásticas foi paralelo ao aumento no número das células sinusoidais.

Integrable aspects of the scaling q-state Potts models I: bound states and bootstrap closure

We discuss the q-state Potts models for q less than or equal to 4, in the scaling regimes close to their critical or tricritical points. Starting from the kink S-matrix elements proposed by Chim and Zamolodchikov, the bootstrap is closed for the scaling regions of all critical points, and for the tricritical points when 4 > q greater than or equal to 2. We also note a curious appearance of the extended last line of Freudenthal's magic square in connection with the Potts models. (C) 2003 Elsevier B.V. B.V. All rights reserved.

Integrable aspects of the scaling q-state Potts models II: finite-size effects

We continue our discussion of the q-state Potts models for q less than or equal to 4, in the scaling regimes close to their critical and tricritical points. In a previous paper, the spectrum and full S-matrix of the models on an infinite line were elucidated; here, we consider finite-size behaviour. TBA equations are proposed for all cases related to phi(21) and phi(12) perturbations of unitary minimal models. These are subjected to a variety of checks in the ultraviolet and infrared limits, and compared with results from a recently-proposed non-linear integral equation. A non-linear integral equation is also used to study the flows from tricritical to critical models, over the full range of q. Our results should also be of relevance to the study of the off-critical dilute A models in regimes 1 and 2. (C) 2003 Elsevier B.V. B.V. All rights reserved.

Bayesian L-optimal exact design of experiments for biological kinetic models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)