Real Return Bonds, Inflation Expectations, and the Break-Even Inflation Rate

According to the Fisher hypothesis, the gap between Canadian nominal and Real Return Bond yields (or break-even inflation rate) should be a good measure of inflation expectations. The authors find that this measure was higher, on average, and more variable than survey measures of inflation expectations between 1992 and 2003. They examine whether risk premiums and distortions embedded in this interest rate gap can account for these facts. Their results indicate that distortions were likely an important reason for the high level and variation of this measure over much of the 1990s. There is little evidence that the distortions examined were as important between 2000 and 2003, but the high level of the break-even inflation rate in 2004 may be evidence of their return. Given the potential distortions, and the difficulty in identifying them, the authors conclude that it is premature to consider this measure a reliable gauge of monetary policy credibility. In addition, it is not as useful as competing tools for short- and medium-term inflation forecasting.Interest rates; Inflation and prices; Market structure and pricing

Review of 'Reading by Moonlight' by Brenda Walker.

Review of 'Reading by Moonlight' by Brenda Walker

Real Return Bonds: Monetary Policy Credibility and Short-Term Inflation Forecasting

The break-even inflation rate (BEIR) is calculated by comparing the yields on conventional and Real Return Bonds. Defined as the average rate of inflation that equates the expected returns on these two bonds, the BEIR has the potential to contain useful information about long-run inflation expectations. Yet the BEIR has been higher, on average, and more variable than survey measures of inflation expectations, which may be explained by the effects of premiums and distortions embedded in the BEIR. Because of the difficulty in accounting for these distortions, the BEIR should not be given a large weight as a measure of long-run inflation expectations at this time. However, as the Real Return Bond market continues to develop, the BEIR should become a more useful indicator of inflation expectations. At present, it demonstrates no clear advantage over survey measures and even past inflation rates in forecasting near-term inflation.

Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception task since the same place is perceptually very different in the morning, at night, or over different seasons. This work addresses place recognition as a domain translation task. Using a pair of coupled Generative Adversarial Networks (GANs), we show that it is possible to generate the appearance of one domain (such as summer) from another (such as winter) without requiring image-to-image correspondences across the domains. Mapping between domains is learned from sets of images in each domain without knowing the instance-to-instance correspondence by enforcing a cyclic consistency constraint. In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition. Experiments show that learned features correspond to visual similarity and can be effectively used for place recognition across seasons.Comment: Accepted for publication in IEEE International Conference on Robotics and Automation (ICRA), 201