nep-dge New Economics Papers
on Dynamic General Equilibrium
Issue of 2024–12–16
three papers chosen by
Christian Zimmermann


  1. Monetary Policy with Persistent Supply Shocks By Galo Nuño; Philipp Renner; Simon Scheidegger
  2. Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning By Jésus Fernández-Villaverde; Galo Nuño; Jesse Perla; Jesús Fernández-Villaverde
  3. Income Inequality and Job Creation By Sebastian K. Doerr; Thomas Drechsel; Donggyu Lee

  1. By: Galo Nuño; Philipp Renner; Simon Scheidegger
    Abstract: This paper studies monetary policy in a New Keynesian model with persistent supply shocks, that is, sustained increases in production costs due to factors such as wars or geopolitical fragmentation. First, we demonstrate that Taylor rules fail to stabilize long-term inflation due to endogenous shifts in the natural interest rate. Second, we analyze optimal policy responses under discretion and commitment. Under discretion, a systematic inflationary bias emerges when the shock impacts the economy. Under commitment, the optimal policy adopts a lean-against-the-wind approach without compensating for past inflation, implying that “bygones are bygones”. We further extend the model to incorporate the zero lower bound (ZLB) and show that the optimal policy supports preemptive easing.
    Keywords: deep learning, Markov switching model, cost-push shocks
    JEL: E32 E58 E63
    Date: 2024
    URL: https://d.repec.org/n?u=RePEc:ces:ceswps:_11463
  2. By: Jésus Fernández-Villaverde; Galo Nuño; Jesse Perla; Jesús Fernández-Villaverde
    Abstract: We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges posed by solving dynamic equilibrium models, especially the feedback loop between individual agents’ decisions and the aggregate consistency conditions required by equilibrium. Following this, we introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude with a survey of neural network applications in quantitative economics and offer reasons for cautious optimism.
    Keywords: deep learning, quantitative economics
    JEL: C61 C63 E27
    Date: 2024
    URL: https://d.repec.org/n?u=RePEc:ces:ceswps:_11448
  3. By: Sebastian K. Doerr; Thomas Drechsel; Donggyu Lee
    Abstract: We propose a novel channel through which rising income inequality affects job creation and macroeconomic outcomes. High-income households save relatively more in stocks and bonds but less in bank deposits. A rising top income share thereby increases the relative financing cost for bank-dependent firms, which in turn create fewer jobs. Exploiting variation in top income shares across US states and an instrumental variable strategy, we provide evidence for this channel. We then build a general equilibrium macro model with heterogeneous households and heterogeneous firms and calibrate it to our empirical estimates. The model shows that the secular rise in top incomes accounts for 13% of the decline in the employment share of small firms since 1980. Through the new channel, rising inequality also reduces the labor share and aggregate output. Model experiments show that ignoring the link between inequality and job creation understates welfare effects of income redistribution.
    JEL: D22 D31 E44 E60 L25
    Date: 2024–11
    URL: https://d.repec.org/n?u=RePEc:nbr:nberwo:33137

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