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Estimation of the quantile model, especially with a large data set, can be computationally burdensome. This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model.

Many emerging-market economies (EMEs) have significantly improved their macroeconomic fundamentals and undergone structural reforms since the Asian crisis. These developments have enhanced the composition of capital flows to EMEs through an improved debt structure, a larger share of capital flows as foreign direct investment, and greater access to international debt markets for corporations in EMEs. Structural changes in the global financial landscape have also increased capital flows, bringing economic and financial benefits to EMEs. During the recent financial crisis, however, the opening up of capital accounts and increased financial and trade linkages left many countries vulnerable to external disruptions. Countries with sound fundamentals have weathered the crisis relatively well. Policy-makers in EMEs need to implement policies that support capital flows and ensure that controls imposed to deal with detrimental outflows during periods of stress or rapid inflows are only temporary.

I examine the impact of non-regulated lenders in the mortgage market using a dynamic stochastic general equilibrium (DSGE) model. My model features two types of financial intermediaries that differ in three ways: (i) only regulated intermediaries face a capital requirement, (ii) non-regulated intermediaries finance themselves by selling securities and cannot accept deposits, and (iii) non-regulated intermediaries face a more elastic demand.

This paper considers an economy where central-bank-issued fiat money competes with privately issued e-money. We study a policy-setting game between the central bank and the e-money issuer and find (1) the optimal monetary policy of the central bank depends on the policy of the private issuer and may deviate from the Friedman rule; (2) multiple equilibria may exist; (3) when the economy approaches a cashless state, the central bank’s optimal policy improves the market power of the e-money issuer and can lead to a discrete decrease in welfare and a discrete increase in inflation; and (4) first best cannot be achieved.

The authors use Jarrow and Turnbull's (1995) reduced-form methodology to model the evolution of the term structure of interest rates in the United States for different credit classes and different industries.

We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges.

The objective of this paper is to propose an early warning system that can predict the likelihood of the occurrence of financial stress events within a given period of time. To achieve this goal, the signal extraction approach proposed by Kaminsky, Lizondo and Reinhart (1998) is used to monitor the evolution of a number of economic indicators that tend to exhibit an unusual behaviour in the periods preceding a financial stress event.

We develop a dynamic model to capture key features of decentralized finance lending. We identify a price-liquidity feedback: the market outcome in any given period depends on agents' expectations about lending activities in future periods, with higher future price expectations leading to more lending and higher prices in that period.

We identify and quantify fiscal news shocks, compiling events (2001–2023) that altered the expected path of U.S. defense expenditure. For each event, we estimate market-implied shifts in expected spending. A shift-share analysis yields a two-year, metropolitan statistical area–level GDP multiplier of approximately 1 for U.S. military build-ups.

This paper examines the cross-border spillovers from major economies’ quantitative easing (QE) policies to their trading partners. We concentrate on spillovers from the US to Canada during the zero lower bound period when QE policies were actively used.