US real interest rates and default risk in emerging economies

This paper empirically investigates the impact of changes in US real interest rates on sovereign default risk in emerging economies using the method of identification through heteroskedasticity. Policy-induced increases in US interest rates starkly raise default risk in emerging market economies. However, the overall correlation between US real interest rates and the risk of default is negative, demonstrating that the effects of other variables dominate the anterior relationship

Monetary policy, default risk and the exchange rate

In a country with high probability of default, higher interest rates may render the currency less attractive if sovereign default is costly. This paper develops that intuition in a simple model and estimates the effect of changes in interest rates on the exchange rate in Brazil using data from the dates surrounding the monetary policy committee meetings and the methodology of identification through heteroskedasticity. Indeed, we find that unexpected increases in interest rates tend to lead the Brazilian currency to depreciate. It follows that granting more independence to a central bank that focus solely on inflation is not always a free-lunch.

A inscrição psicológica do sintoma: uma aproximação psicoanalítica a psicossomática

Os discursos familiares encerram, no seu bojo, mitos sociais, sentimentos e fantasias inconscientes a eles conectados, que permaneceram secretos e poderosos através de sua historicidade e que vão se inscrever na matéria-prima pura: o recém-nascido, que deve ser moldado mediante o processo de identificação, investimentos libidinais e registro do discurso das pulsões e seus representantes. A partir de uma abordagem psicanalítica dos sintomas psicossomáticos, estudamos os primórdios da constituição do sujeito do inconsciente, recorrendo à estruturação do narcisismo e do inconsciente, construindo-se como uma linguagem ao encadear os significantes por contiguidade ou semelhança, respectivamente, por deslocamentos e condensações, isto é, construindo-se numa cadeia como metonímias e metáforas que se organizam num discurso. O narcisismo seria a causa do corpo. Este último, suporte biológico, que se torna erógeno a partir do discurso materno cujo desejo antecipa uma gestalt à imagem, organizando e delimitando o corpo despedaçado e anárquico do bebê. Como estrutura narcísica relacionamos a articulação das pulsões, das zonas erógenas, do desejo, do recalcamento fixando os representantes que irão constituir um Ego corporal. O sintoma no real do corpo apareceria como efeito de uma desorganização na articulação dos significantes do desejo materno como legalidade de um discurso, dificultando a inscrição simbólica da significação dos limites e fronteiras do corpo erógeno do bebê, cujo representante da pulsão evidencia-se como demanda de significação.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).