The effects of commodity dependence on the brazilian economy: a test of the dutch disease hypothesis

A doença holandesa tornou-se amplamente conhecida na década de 1960, quando a descoberta repentina de reservatórios de gás natural em território holandês, na região do mar do norte, transformou o país em uma economia rica em recursos. A desagradável consequência que proveio da recém-adquirida abundância de commodities foi o declínio da próspera indústria holandesa, que perdeu sua competitividade devido à valorização do florim holandês, como consequência do aumento do influxo de capital estrangeiro no país. Desde então este fenômeno tem sido observado em diversos países que possuem abundância de commodities. O objetivo desta tese é aplicar o modelo da doença holandesa ao Brasil, já que a maior economia da América latina poderá também ter de encarar a ameaça de se tornar prisioneira da “armadilha das commodities”, devido à sua abundância de recursos naturais. O autor revisa a bibliografia básica abordando o tema geral da doença holandesa e dá enfoque a estudos realizados anteriormente no Brasil. Além disso, os quatro maiores sintomas que caracterizam a doença holandesa são testados: (1) valorização das taxas de câmbio do real, (2) declínio do setor industrial, (3) crescimento do setor de serviços, e (4) aumento dos salários. Todos estes sintomas foram observados e podem ser comprovados através das abordagens de cointegração ou de correlação, com exceção do sintoma número dois. Ainda que estes resultados sejam significativos, há muito outros fatores determinantes que influenciam o desenvolvimento dos sintomas examinados, motivo pelo qual futuros estudos serão necessários para se obtiver conclusões definitivas sobre como o Brasil é afetado pela doença holandesa.

Testing the externalities hypothesis of endogenous growth using cointegration

The initial endogenous growth models emphasized the importance of externaI effects in explaining sustainable growth across time. Empirically, this hypothesis can be confirmed if the coefficient of physical capital per hour is unity in the aggregate production function. Although cross-section results concur with theory, previous estimates using time series data rejected this hypothesis, showing a small coefficient far from unity. It seems that the problem lies not with the theory but with the techniques employed, which are unable to capture low frequency movements in high frequency data. This paper uses cointegration - a technique designed to capture the existence of long-run relationships in multivariate time series - to test the externalities hypothesis of endogenous growth. The results confirm the theory' and conform to previous cross-section estimates. We show that there is long-run proportionality between output per hour and a measure of capital per hour. U sing this result, we confmn the hypothesis that the implied Solow residual can be explained by government expenditures on infra-structure, which suggests a supply side role for government affecting productivity and a decrease on the extent that the Solow residual explains the variation of output.

Testing the hypothesis of contagion using multivariate volatility models

The aim of this paper is to test whether or not there was evidence of contagion across the various financial crises that assailed some countries in the 1990s. Data on sovereign debt bonds for Brazil, Mexico, Russia and Argentina were used to implement the test. The contagion hypothesis is tested using multivariate volatility models. If there is any evidence of structural break in volatility that can be linked to financial crises, the contagion hypothesis will be confirmed. Results suggest that there is evidence in favor of the contagion hypothesis.

Testing the long-run implications of the expectation hypothesis using cointegration techniques with structural change

This paper investigates whether or not multivariate cointegrated process with structural change can describe the Brazilian term structure of interest rate data from 1995 to 2006. In this work the break point and the number of cointegrated vector are assumed to be known. The estimated model has four regimes. Only three of them are statistically different. The first starts at the beginning of the sample and goes until September of 1997. The second starts at October of 1997 until December of 1998. The third starts at January of 1999 and goes until the end of the sample. It is used monthly data. Models that allows for some similarities across the regimes are also estimated and tested. The models are estimated using the Generalized Reduced-Rank Regressions developed by Hansen (2003). All imposed restrictions can be tested using likelihood ratio test with standard asymptotic 1 qui-squared distribution. The results of the paper show evidence in favor of the long run implications of the expectation hypothesis for Brazil.

A unit root test based on partially adaptive estimation

This paper constructs a unit root test baseei on partially adaptive estimation, which is shown to be robust against non-Gaussian innovations. We show that the limiting distribution of the t-statistic is a convex combination of standard normal and DF distribution. Convergence to the DF distribution is obtaineel when the innovations are Gaussian, implying that the traditional ADF test is a special case of the proposed testo Monte Carlo Experiments indicate that, if innovation has heavy tail distribution or are contaminated by outliers, then the proposed test is more powerful than the traditional ADF testo Nominal interest rates (different maturities) are shown to be stationary according to the robust test but not stationary according to the nonrobust ADF testo This result seems to suggest that the failure of rejecting the null of unit root in nominal interest rate may be due to the use of estimation and hypothesis testing procedures that do not consider the absence of Gaussianity in the data.Our results validate practical restrictions on the behavior of the nominal interest rate imposed by CCAPM, optimal monetary policy and option pricing models.