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We propose a theory of monetary policy and macroprudential interventions in financial markets. We focus on economies with nominal rigidities in goods and labor markets and subject to constraints on monetary policy, such as the zero lower bound or fixed exchange rates. We identify an aggregate demand externality that can be corrected by macroprudential interventions in financial markets. Ex post, the distribution of wealth across agents affects aggregate demand and output. Ex ante, however, these effects are not internalized in private financial decisions. We provide a simple formula for the required financial interventions that depends on a small number of measurable sufficient statistics. We also characterize optimal monetary policy. We extend our framework to incorporate pecuniary externalities, providing a unified approach to both externalities. Finally, we provide a number of applications which illustrate the relevance of our theory.

We develop a probit choice model under optimal sequential search and apply it to the study of aggregate demand of consumer durable goods. In our joint model of search and choice, we derive an expression for the probability of choice that obeys the full set of restrictions imposed by optimal sequential search. Estimation of our partially analytic model avoids the computation of high-dimensional integrations in the evaluation of choice probabilities, which is of particular benefit when search sets are large. We demonstrate the advantages of our approach in data experiments and apply the model to aggregate search and choice data from the camcorder product category at Amazon.com. We show that the joint use of search and choice data provides better performance in terms of inferences and predictions than using search data alone and leads to realistic estimates of consumer substitution patterns. Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2545 . This paper was accepted by Pradeep Chintagunta, marketing .

We develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution patterns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate their negative economic impact, which run counter to simple commonly used heuristics.

This paper examines the distribution of output around capacity when money demand is a nonlinear function of the nominal interest rate such that nominal interest rates cannot become negative. When fluctuations in output result primarily from disturbances to the money market, the variance of output is shown to be an increasing function of the trend inflation rate. When they result from disturbances to the goods market, the variance of output is a decreasing function of the trend inflation rate. When both disturbances are significant, there exists, in general, a critical non-zero trend inflation rate that minimizes the variance of output.

With an estimated New Keynesian model, this paper compares the \"Great Recession\" of 2007—09 to its two immediate predecessors in 1990—91 and 2001. The model attributes all three downturns to a similar mix of aggregate demand and supply disturbances. The most recent series of adverse shocks lasted longer and became more severe, however, prolonging and deepening the Great Recession. In addition, the zero lower bound on the nominal interest rate prevented monetary policy from stabilizing the U.S. economy as it had previously; counterfactual simulations suggest that without this constraint, output would have recovered sooner and more quickly in 2009.

Many technological innovations replace workers with machines. But this capital–labor substitution need not reduce aggregate labor demand, because it simultaneously induces four countervailing responses: own-industry output effects; cross-industry input–output effects; between-industry shifts; and final demand effects. We quantify these channels using four decades of harmonized cross-country and industry data, whereby we measure automation as industry-level movements in total factor productivity that are common across countries. We find that automation displaces employment and reduces labor’s share of value added in the industries where it originates (a direct effect). In the case of employment, these own-industry losses are reversed by indirect gains in customer industries and induced increases in aggregate demand. By contrast, own-industry labor share losses are not recouped elsewhere. Our framework can account for a substantial fraction of the reallocation of employment across industries and the aggregate fall in the labor share over the last three decades. It does not, however, explain why the labor share fell more rapidly during the 2000s.

Recent booms and busts in commodity prices have generated concerns that financial speculation causes excessive commodity-price comovement, driving prices away from levels implied by supply and demand under rational expectations. We develop a structural vector autoregression model of a commodity futures market and use it to explain two recent spikes in cotton prices. In doing so, we make two contributions to the literature on commodity price dynamics. First, we estimate the extent to which cotton price booms and busts can be attributed to comovement with other commodities. Finding such comovement would be necessary but would not be sufficient evidence to establish that broad-based financial speculation drives commodity prices. Second, after controlling for aggregate demand and comovement, we develop a new method to point identify shocks to precautionary demand for cotton separately from shocks to current supply and demand. To do so, we use differences in volatility across time implied by the rational expectations competitive storage model. We find limited evidence that financial speculation caused cotton prices to spike in 2008 or 2011. We conclude that the 2008 price spike was driven mostly by precautionary demand for cotton, and the 2011 spike was caused by a net supply shortfall.

This paper studies a popular statistical model of permanent and transitory shocks to output using a set of arguably more plausible structural assumptions. One way to structurally interpret the model is by assuming aggregate demand has no long-run output effect. However, many economic theories are inconsistent with that assumption. Instead, we reinterpret the statistical model assuming a positive shock to aggregate supply lowers the price level and in the long run raises output while a positive shock to aggregate demand raises the price level. Under these assumptions, a puzzling finding from the empirical literature implies that a positive aggregate demand shock had a long-run positive effect on output in pre-World War I economies.

We provide a Keynesian growth theory in which pessimistic expectations can lead to very persistent, or even permanent, slumps characterized by high unemployment and weak growth. We refer to these episodes as stagnation traps, because they consist in the joint occurrence of a liquidity and a growth trap. In a stagnation trap, the central bank is unable to restore full employment because weak growth depresses aggregate demand and pushes the policy rate against the zero lower bound, while growth is weak because low aggregate demand results in low profits, limiting firms’ investment in innovation. Aggressive policies aiming at restoring growth, such as subsidies to investment, can successfully lead the economy out of a stagnation trap by generating a regime shift in agents’ growth expectations.

Making inferences about aggregate business cycles from regional variation alone is difficult because of economic channels and shocks that differ between regional and aggregate economies. However, we argue that regional business cycles contain valuable information that can help discipline models of aggregate fluctuations. We begin by documenting a strong relationship across U.S. states between local employment and wage growth during the Great Recession. This relationship is much weaker in U.S. aggregates. Then, we present a methodology that combines such regional and aggregate data in order to estimate a medium-scale New Keynesian DSGE model. We find that aggregate demand shocks were important drivers of aggregate employment during the Great Recession, but the wage stickiness necessary for them to account for the slow employment recovery and the modest fall in aggregate wages is inconsistent with the flexibility of wages we observe across U.S. states. Finally, we show that our methodology yields different conclusions about the causes of aggregate employment and wage dynamics between 2007 and 2014 than either estimating our model with aggregate data alone or performing back-of-the-envelope calculations that directly extrapolate from well-identified regional elasticities.