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A Note on Foreign Direct Investment (FDI) and Industrial Competitiveness in Brazil

Author

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  • Alexandra Mello Schmidt
  • Dani Gamerman
  • Ajax R. B. Moreira

Abstract

Bayesian dynamic linear models (DLM) are useful in time series modelling because of the flexibility that they present in obtaining a good forecast. They are based on a decomposition of the relevant factors which explain the behavior of the series through a series of state parameters. Nevertheless the DLM as developed in West & Harrison (1997) depend on additional quantities, such as the variance of the system disturbances, which, in practice, are unknown. These are refered as hyperparameters of the model. In this paper, DLM with auto-regressive components are used to describe time series showing cyclic behavior. The marginal posterior distribution for state parameters can be obtained by weighing the conditional distribution of state parameters by the marginal distribution of hyperparameters. In most cases the joint distribution of the hyperparameters can be obtained analytically but the marginal distributions of the components can not, thus requiring numerical integration. We propose to obtain samples of the hyperparameters by a variant of the Sampling Importance Resampling (SIR) method. A few applications are made with simulated and real datasets. Os modelos lineares dinâmicos (MLD) West and Harrison (1997) constituem instrumentos úteis para a previsão de curto prazo de séries de tempo porque são flexíveis e também podem decompor a trajetória da série em fatores relevantes que têm interpretação e dinâmica característica. Tais modelos, entretanto, dependem de quantidades desconhecidas, constantes ao longo da amostra, denominadas hiperparâmetros. Neste artigo, um (MLD) com componentes auto-regressivas é utilizado para descrever séries que têm componentes cíclicas. A distribuição marginal para os parâmetros de estado pode ser obtida ponderando as distribuições condicionais desses parâmetros pela distribuição marginal dos hiperparâmetros. Na maioria dos casos, a distribuição conjunta dos hiperparâmetros pode ser obtida analiticamente, mas não a distribuição marginal dos componentes, o que requer integração numérica. Propomos obter amostras de hiperparâmetros utilizando uma variante do método de amostragem e reamostragem por importância (SIR). Apresentamos uma aplicação com dados simulados e duas com dados reais.

Suggested Citation

  • Alexandra Mello Schmidt & Dani Gamerman & Ajax R. B. Moreira, 2015. "A Note on Foreign Direct Investment (FDI) and Industrial Competitiveness in Brazil," Discussion Papers 0078, Instituto de Pesquisa Econômica Aplicada - IPEA.
  • Handle: RePEc:ipe:ipetds:0078
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