ResumoO objetivo deste trabalho é avaliar o comportamento dos principais parâmetros da economia brasileira através da estimação de um modelo DSGE (Dynamic Stochastic General Equilibrium) de economia aberta usando métodos bayesianos e permitindo mudanças de regime markovianas de determinados parâmetros. Utilizando o modelo DSGE desenvolvido por Justiniano e Preston (2010) e o método de solução do modelo Markov Switching DSGE (MS-DSGE) proposto por Farmer et al. (2008), este trabalho encontrou superioridade nos ajustes dos dados dos modelos que incorporaram mudanças markovianas, rejeitando a hipótese de parâmetros constantes em modelos DSGE para a economia brasileira. Palavras-chave: Modelo DSGE. Markov Switching. MS-DSGE. AbstractThe goal of this paper is to evaluate the behavior of the main parameters of the Brazilian economy through the estimation of an open-economy dynamic stochastic general equilibrium (DSGE) model using Bayesian methods and allowing for Markov switching of certain parameters. Using the DSGE model developed by Justiniano & Preston (2010) and the solution method of the Markov switching DSGE (MS-DSGE) model proposed by Farmer et al. (2008), this paper found a superior fit in the data of Markov switching models, rejecting the hypothesis of constant parameters in DSGE models for the Brazilian economy.

O objetivo desse artigo é propor uma metodologia para mensurar o tamanho das atividades características do turismo. A proposta parte do princípio da mensuração pelo lado da oferta utilizando as mesmas técnicas empregadas para mensuração das atividades no âmbito das Contas Nacionais e Regionais. Utilizando dados do Brasil e de suas Unidades da Federação, entre os anos de 2010 a 2015, os resultados encontrados apontaram que quanto a contribuição das atividades características do turismo no valor adicionado nacional, os estados litorâneos tendem a se destacar, as regiões Sul e Sudeste são hegemônicas em termos nominais e foi possível observar um decrescimento real no turismo em 2015.

The Brazilian Labour Force Survey publishes monthly national indicators based on 3-month rolling data. This paper presents state-space models to produce state-level single-month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS-COV-2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state-space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation.

The Brazilian Labour Force Survey (BLFS) is a quarterly rotating panel survey with 80% sample overlap between two successive quarters. Monthly unemployment rate estimates are regularly produced based on a three-month average of direct estimates. Due to the unforeseen situation of COVID19 pandemic and its effects in the economy and labour market, there was a need to investigate model-based estimation procedures to obtain unemployment rate single-month estimates. We present structural time series models developed to produce model-based single month estimates at national level as well as small area (state-level) estimates at a higher frequency than those currently being published. Using the state-space framework, the models account for the autocorrelation due to sample overlap and the increased dynamics in the labour force series in 2020. In addition, bivariate models that combine claimant count and survey data are investigated. The models not only yield estimates with better precision than direct estimates, since the latter were affected by a rise in non-response, but they can deliver reliable state-level official statistics at a monthly frequency that are presently required. The new improved model-based estimates were proposed as experimental statistics for the Brazilian national statistical office (IBGE).

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