O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORDewachter, HansIania, LeonardoLyrio, Marco Túlio Pereira2023-07-192023-07-192011https://repositorio.insper.edu.br/handle/11224/5864We estimate a New-Keynesian macro-finance model of the yield curve incorporating learning by private agents with respect to the long-run expectation of ináation and the equilibrium real interest rate. A preliminary analysis shows that some liquidity premia, expressed as a degree of mispricing relative to no-arbitrage restrictions, and time variation in the prices of risk are important features of the data. These features are, therefore, included in our learning model. The model is estimated on U.S. data using Bayesian techniques. The learning model succeeds in explaining the yield curve movements in terms of macroeconomic shocks. The results also show that the introduction of a learning dynamics is not sufficient to explain the rejection of the extended expectations hypothesis. The learning mechanism, however, reveals some interesting points. We observe an important diference between the estimated ináation target of the central bank and the perceived long-run ináation expectation of private agents, implying the latter were weakly anchored. This is especially the case for the period from mid-1970s to mid-1990s. The learning model also allows a new interpretation of the standard level, slope, and curvature factors based on macroeconomic variables. In line with standard macro-finance models, the slope and curvature factors are mainly driven by exogenous monetary policy shocks. Most of the variation in the level factor, however, is due to shocks to the output-neutral real rate, in contrast to the mentioned literature which attributes most of its variation to long-run ináation expectations.39 p.DigitalInglêsworking paperNew-Keynesian modelAffine yield curve modelLearningBayesian estimationBEWP 134/2011