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The goal of this paper is to test the Husted model and to inspect the long-run sustainability of Brazilian current account in a very specific period of time (1996-2005) by the use of monthly data. We have tested the inter-temporal budget constraints (IBC) condition via unit root test with structural break and co-integration through Gregory-Hansen test in a 117 long nominal, GDP normalized and CPI normalized series for the Brazilian economy. The results indicated that the pure IBC condition does not hold for the Brazilian economy. However, there is co-integration among the series used in this work and the balance of accounts is sustainable. O objetivo deste trabalho é testar o modelo de Husted e inspecionar a sustentabilidade de longo prazo das contas correntes brasileiras em um período muito específico de tempo (1996-2005) por meio do uso de dados mensais. Foi realizado teste da condição de restrição orçamentária inter-temporal (IBC) via teste de raiz unitária com quebra estrutural e co-integração pelo método de Gregory-Hansen em séries (117 pontos) nominais, normalizadas pelo PIB e normalizadas pelo índice de preços ao consumidor norte-americano (CPI). Os resultados indicam que a condição IBC pura não se sustenta para a economia brasileira, mas há co-integração entre as séries utilizadas, indicando que o equilíbrio de contas é sustentável.

O objetivo central deste artigo é o de propor e avaliar modelos econométricos de previsão para o PIB industrial brasileiro. Para tanto, foram utilizados diversos modelos de previsão como também combinações de modelos. Foi realizada uma análise criteriosa das séries a serem utilizadas na previsão. Nós concluímos que a utilização de vetores de cointegração melhora substancialmente a performance da previsão. Além disso, os modelos de combinação de previsão, na maioria dos casos, tiveram uma performance superior aos demais modelos, que já apresentavam boa capacidade preditiva.The purpose of this article is to propose and evaluate forecasting models for the Brazilian industrial GDP. Most models are based on vector auto-regressions (VARs) or on restricted VARs, but models on the ARMA class are also entertained. We used many forecasting models and also combinations of these models. The use of cointegration vectors improves substantially the forecast performance of industrial GDP. Furthermore, in general, combining models out-performed individual models, even when the performance of the later was acceptable.

Very useful information, usually ignored, for construction of coincident index is the target quarterly series itself. This can be very inefficient because typically the monthly coincident series keep just high economical correlation, not always tested, with the quarterly target series. Actually, the construction of a mixed-frequency coincident index is statistically complicated. On the ground that, Mariano e Murasawa presented a new methodology, in which this information is aggregated, and the quarterly variable is assumed as a latent variable observed just every three months. In this fashion, is implicitly assumed that the monthly GDP not observed represents the state of industrial economy. Usually a common factor is imposed, as in the Stock-Watson approach. Another possible approach more attractive is not to impose any common factor and instead, like Mariano e Murosawa, to build a mixed frequency VAR model without common factor. In this work we compare the performance in-sample of the mixed frequency VAR model for building coincident indicator with others approaches, dating the business cycle for industrial activity with Bry-Boschan procedure.

In this paper we perform and evaluate, in sample, some methodologies for building of coincident indicators focusing on the detection of business cycle of the Industrial activity. In respect of selection of coincident series for the Brazilian case, we will lean on recent literature (SPACOV, 2001). Specifically, we try a version of coincident indicator based on the coincident indicator for economy of the TCB (The Conference Board), a modified version in which the volatility is modeled and a Stock-Watson approach. Finally, we test if the unemployment series can be a good indicator for recession, as in American case, when submitted to a Hamilton Modeling. We conclude that, in general, the TCB index outperform the others methods tested, being competitive just with the modified method. Furthermore, The TCB Traditional has the tendency to comprehend the recessives periods of the others, in correspondence with the known recessive periods for industrial activity. The Stock-Watson approach and the Hamilton modeling are not appropriate for Brazilian Industrial Economy. The former because ignore almost all recessive periods known and the second, on the contrary, since indicates a excessive ones. In addition, we have concluded that when we compare with preceding research, dating made by Duarte, Issler e Spacov for instance, the standard TCB method is superior and most appropriated.

In this first working paper on leading indicators, we review the literature on the subject and, on this framework, we apply some methods of leading indicators building for industrial activity, testing in-sample, in order to monitor the industrial activity growing and its business cycle detection. Regarding leading series selection for Brazilian case, we support it on recent literature (SPACOV, 2001), testing, selecting the leading series by time leading, Granger-causality and comparison with others works. For leading indicator composition, specifically, we consider the Vectorial Auto-Regressive system, with and without error correction term as well as a simple probit system for business cycle forecast. We conclude, that a VECM can be used successfully and, in-sample, on a visual evaluation, overrule the VAR system and the probit system is quite efficient, despite its simplicity.