No abstract

No abstract

PurposeThe aim of this paper is to assess whether the inclusion of the rental housing market affect the dynamics of the real business cycles (RBCs).Design/methodology/approachFor this investigation, the authors model and estimate two dynamic stochastic general equilibrium (DSGE) versions for the US economy, one with and one without the presence of residential rent.FindingsThe findings provide evidence that the inclusion of the rental housing market can improve the assessment of public policies and the projection of scenarios in the face of sudden macroeconomic shocks. The addition of this secondary housing market augments the effect of total factor productivity (TFP) shock on output and consumption. In addition, it increases the effect of the credit shock on the demand for housing. The latter highlights the role of credit for the real estate market. Therefore, the authors recommend that analysts and macro-prudential authorities consider adding it to their models.Originality/valueThe findings provide evidence that the inclusion of the rental housing market can improve the assessment of public policies and the projection of scenarios in the face of sudden macroeconomic shocks.

Given the relevance of corn for food and fuel industries, analysts and scholars are constantly comparing the forecasting accuracy of econometric models. These exercises test not only for the use of new approaches and methods, but also for the addition of fundamental variables linked to the corn market. This paper compares the accuracy of different usual models in financial macro-econometric literature for the period between 1995 and 2017. The main contribution lies in the use of transition regime models, which accommodate structural breaks and perform better for corn price forecasting. The results point out that the best models as those which consider not only the corn market structure, or macroeconomic and financial fundamentals, but also the non-linear trend and transition regimes, such as threshold autoregressive models.

Este trabalho analisa os determinantes dos custos de transporte das exportações do complexo brasileiro de soja, no período 2008-2014, por uma estimação gravitacional com dados em painel. Os dados foram coletados no Commodity Trade Statistics (COMTRADE), da Organização das Nações Unidas (ONU) e desagregados sob classificação Harmonized System (HS).  As estimativas da regressão informaram que para cada quilômetro de distância entre o Brasil e o país importador de soja, os custos se elevam em 6,34%, e uma melhoria de 1% em infraestrutura causa uma redução de 74% nos custos de transporte.