Search

Real rigidities are an important feature of modern sticky price models and are policy-relevant because of their welfare consequences, but cannot be structurally identified from time series. I evaluate the plausibility of capital specificity as a source of real rigidities using a two-dimensional generalized (s,S) model calibrated to micro evidence.

This paper describes a new test for evaluating conditional density functions that remains valid when the data are time-dependent and that is therefore applicable to forecasting problems. We show that the test statistic is asymptotically distributed standard normal under the null hypothesis, and diverges to infinity when the null hypothesis is false.

This paper examines the contributions of population aging, mortgage innovation and historically low interest rates to the sharp rise in U.S. house prices and mortgage debt between 1994 and 2005.

In this paper, we use an economics decision-making experiment to test a key assumption underpinning the efficacy of price-level targeting relative to inflation targeting for business cycle stabilization and mitigating the effects of the zero lower bound on nominal interest rates.

I use a search-theoretic model of money to study how open market operations affect the conduct of monetary policy and what this means for households along the wealth distribution. In the model, households vary in the size and composition of their portfolios, which in turn implies that they may be unevenly affected by open market operations.

This paper presents a multifactor asset pricing model for currency, bond, and stock returns for ten emerging markets to investigate the effect of the exchange rate regime on the cost of capital and the integration of emerging financial markets. Since there is evidence that a fixed exchange rate regime reduces the currency risk premia demanded by foreign investors, the tentative conclusion is that a fixed exchange rate regime system can help reduce the cost of capital in emerging markets.

For central banks, conducting policy in an environment of uncertainty is a daily fact of life. This uncertainty can take many forms, ranging from incomplete knowledge of the correct economic model and data to future economic and geopolitical events whose precise magnitudes and effects cannot be known with certainty.

We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.

During downturns, workers get stuck in low-productivity jobs and wages remain stagnant. I build an heterogenous agent incomplete market model with a full job ladder that accounts for these facts. An adverse financial shock calibrated to the US Great Recession replicates the period’s slow recovery and missing disinflation.

Consumers often purchase more than one differentiated product, assembling a portfolio, which might potentially affect substitution patterns of demand and, as a consequence, oligopolistic firms’ pricing strategies.