scholarly journals Inequality, Business Cycles, and Monetary‐Fiscal Policy

Econometrica ◽  
2021 ◽  
Vol 89 (6) ◽  
pp. 2559-2599 ◽  
Author(s):  
Anmol Bhandari ◽  
David Evans ◽  
Mikhail Golosov ◽  
Thomas J. Sargent
Keyword(s):  
Business Cycles ◽  
Incomplete Markets ◽  
New Keynesian ◽  
Fiscal Policies ◽  
Price Stabilization ◽  
Small Noise ◽  
Aggregate Shocks ◽  
Keynesian Model ◽  
Optimal Policies ◽  
Order Of Magnitude

We study optimal monetary and fiscal policies in a New Keynesian model with heterogeneous agents, incomplete markets, and nominal rigidities. Our approach uses small‐noise expansions and Fréchet derivatives to approximate equilibria quickly and efficiently. Responses of optimal policies to aggregate shocks differ qualitatively from what they would be in a corresponding representative agent economy and are an order of magnitude larger. A motive to provide insurance that arises from heterogeneity and incomplete markets outweighs price stabilization motives.

Is risk shock a key factor driving business cycles in China?

2017 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhe Li ◽  
Shuixing Luo
Keyword(s):  
Business Cycles ◽  
Chinese Economy ◽  
New Keynesian ◽  
Macroeconomic Policies ◽  
Us Economy ◽  
Keynesian Model ◽  
The Us ◽  
Key Factor ◽  
Investment Capital ◽  
The Impact

Abstract This paper studies the impact of risk shock on the Chinese economy using a New-Keynesian model with financial frictions. The study shows that risk shock is an important driving force for the fluctuations of GDP, investment, capital, credit, and credit spread in China. However, the role of risk shock in driving China’s business cycles is not as crucial as in the US economy (see Christiano, Motto, and Rostagno 2014). There are three main reasons that explain the different performance of risk shocks in China and the US: the volatility of risk shock, the effect of equity shock, and the influence of macroeconomic policies are all different in China and in the US. Our paper contributes to an understanding of the business cycles in China during the period from 1999 to 2015, particularly in comparison with business cycles in the US.

scholarly journals Understanding HANK: Insights From a PRANK

Econometrica ◽  
2020 ◽  
Vol 88 (3) ◽  
pp. 1113-1158 ◽  
Author(s):  
Sushant Acharya ◽  
Keshav Dogra
Keyword(s):  
Monetary Policy ◽  
Fiscal Policy ◽  
Incomplete Markets ◽  
New Keynesian ◽  
New Keynesian Model ◽  
Income Risk ◽  
Heterogeneous Agent ◽  
Liquidity Trap ◽  
Forward Guidance ◽  
Keynesian Model

Using an analytically tractable heterogeneous agent New Keynesian model, we show that whether incomplete markets resolve New Keynesian “paradoxes” depends on the cyclicality of income risk. Incomplete markets reduce the effectiveness of forward guidance and multipliers in a liquidity trap only with procyclical risk. Countercyclical risk amplifies these “puzzles.” Procyclical risk permits determinacy under a peg; countercyclical risk may generate indeterminacy even under the Taylor principle. By affecting the cyclicality of risk, even “passive” fiscal policy influences the effects of monetary policy.

scholarly journals Monetary Policy, Bounded Rationality, and Incomplete Markets

2019 ◽  
Vol 109 (11) ◽  
pp. 3887-3928 ◽  
Author(s):  
Emmanuel Farhi ◽  
Iván Werning
Keyword(s):  
Monetary Policy ◽  
Bounded Rationality ◽  
Incomplete Markets ◽  
Idiosyncratic Risk ◽  
Borrowing Constraints ◽  
New Keynesian ◽  
New Keynesian Model ◽  
Benchmark Model ◽  
Forward Guidance ◽  
Keynesian Model

This paper extends the benchmark New-Keynesian model by introducing two frictions: (i) agent heterogeneity with incomplete markets, uninsurable idiosyncratic risk, and occasionally-binding borrowing constraints; and (ii) bounded rationality in the form of level-k thinking. Compared to the benchmark model, we show that the interaction of these two frictions leads to a powerful mitigation of the effects of monetary policy, which is more pronounced at long horizons, and offers a potential rationalization of the “forward guidance puzzle.” Each of these frictions, in isolation, would lead to no or much smaller departures from the benchmark model. (JEL D52, D81, E12, E52)

scholarly journals Using the Sequence‐Space Jacobian to Solve and Estimate Heterogeneous‐Agent Models

Econometrica ◽  
2021 ◽  
Vol 89 (5) ◽  
pp. 2375-2408 ◽  
Author(s):  
Adrien Auclert ◽  
Bence Bardóczy ◽  
Matthew Rognlie ◽  
Ludwig Straub
Keyword(s):  
General Equilibrium ◽  
Sequence Space ◽  
New Keynesian ◽  
New Keynesian Model ◽  
Heterogeneous Agent ◽  
Aggregate Shocks ◽  
Perfect Foresight ◽  
Keynesian Model ◽  
Derivatives Of ◽  
Heterogeneous Agent Models

We propose a general and highly efficient method for solving and estimating general equilibrium heterogeneous‐agent models with aggregate shocks in discrete time. Our approach relies on the rapid computation of sequence‐space Jacobians—the derivatives of perfect‐foresight equilibrium mappings between aggregate sequences around the steady state. Our main contribution is a fast algorithm for calculating Jacobians for a large class of heterogeneous‐agent problems. We combine this algorithm with a systematic approach to composing and inverting Jacobians to solve for general equilibrium impulse responses. We obtain a rapid procedure for likelihood‐based estimation and computation of nonlinear perfect‐foresight transitions. We apply our methods to three canonical heterogeneous‐agent models: a neoclassical model, a New Keynesian model with one asset, and a New Keynesian model with two assets.

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