A note on Chambers's "long memory and aggregation in macroeconomic time series"

Chambers (1998) explores the interaction between long memory and aggregation. For continuous-time processes, he takes the aliasing effect into account when studying temporal aggregation. For discrete-time processes, however, he seems to fail to do so. This note gives the spectral density function of temporally aggregated long memory discrete-time processes in light of the aliasing effect. The results are different from those in Chambers (1998) and are supported by a small simulation exercise. As a result, the order of aggregation may not be invariant to temporal aggregation, specifically if d is negative and the aggregation is of the stock type.

The Brazilian financial system and the green economy : alignment with sustainable development

Three studies were prepared and are presented in this document. The first, The Brazilian Financial Sector Institutional Context in the Transition to Sustainable Development looks at the legislation, regulation, and public policies aimed at socio-environmental themes related to the financial sector. The second study, Current Financing for the Green Economy in Brazil, provides an initial estimate of the financial assets already allocated to the green economy, as well as a methodological proposal for the survey and monitoring of the respective flow of assets. The third and final study looks at two important segments of the Brazilian economy and their process of transition to a greener economy: renewable energy and agriculture.

Output convergence in Mercosur: multivariate time series evidence

The aim of this paper is to provide evidence on output convergence among the Mercosur countries and associates, using multivariate time-series tests. The methodology is based on a combination of tests and estimation procedures, both univariate and multivariate, applied to the differences in per capita real income. We use the definitions of time-series convergence proposed by Bernard & Durlauf and apply unit root and tests proposed by Abuaf & Jorion and Taylor & Sarno. In this same multivariate context, the Flôres, Preumont & Szafarz and Breuer, MbNown & Wallace tests, which allow for the existence of correlations across the series without imposing a common speed of mean reversion, identify the countries that convergence. Concerning the empirical results, there is evidence of long-run convergence or, at least, catching up, for the smaller countries, Bolivia, Paraguay, Peru and Uruguay, towards Brazil and, to some extent, Argentina. In contrast, the evidence on convergence for the larger countries is weaker, as they have followed different (or rather opposing) macroeconomic policy strategies. Thus the future of the whole area will critically depend on the ability of Brazil, Argentina and Chile to find some scope for more cooperative policy actions.

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.