IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/16183.html

Heterogeneity and Aggregate Fluctuations

Author

Listed:
  • Schorfheide, Frank
  • Chang, Minsu
  • Chen, Xiaohong

Abstract

We develop a state-space model with a state-transition equation that takes the form of a functional vector autoregression and stacks macroeconomic aggregates and a cross-sectional density. The measurement equation captures the error in estimating log densities from repeated cross-sectional samples. The log densities and the transition kernels in the law of motion of the states are approximated by sieves, which leads to a finite-dimensional representation in terms of macroeconomic aggregates and sieve coefficents. We use this model to study the joint dynamics of technology shocks, per capita GDP, employment rates, and the earnings distribution. We find that the estimated spillovers between aggregate and distributional dynamics are generally small, a positive technology shock tends to decrease inequality, and a shock that raises the inequality of earnings leads to a small but not significant increase in GDP.

Suggested Citation

  • Schorfheide, Frank & Chang, Minsu & Chen, Xiaohong, 2021. "Heterogeneity and Aggregate Fluctuations," CEPR Discussion Papers 16183, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:16183
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP16183
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. is not listed on IDEAS
    2. Hilde C. Bjørnland & Yoosoon Chang & Jamie L. Cross, 2023. "Oil and the Stock Market Revisited: A Mixed Functional VAR Approach," CAMA Working Papers 2023-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Matthew D. Cocci & Mikkel Plagborg-M{o}ller, 2021. "Standard Errors for Calibrated Parameters," Papers 2109.08109, arXiv.org, revised Jun 2024.
    4. Denis Koshelev & Alexey Ponomarenko & Sergei Seleznev, 2023. "Amortized Neural Networks for Agent-Based Model Forecasting," Bank of Russia Working Paper Series wps115, Bank of Russia.
    5. Bilbiie, F. & Primiceri, G. E. & Tambalotti, A., 2022. "Inequality and Business Cycles," Cambridge Working Papers in Economics 2275, Faculty of Economics, University of Cambridge.
    6. Dong, Chaohua & Chen, Rong & Xiao, Zhijie & Liu, Weiyi, 2024. "Functional quantile autoregression," Journal of Econometrics, Elsevier, vol. 244(2).
    7. Matthew D. Cocci & Mikkel Plagborg-Møller, 2021. "Standard Errors for Calibrated Parameters," Working Papers 2021-20, Princeton University. Economics Department..
    8. Stephanie Ettmeier, 2022. "No Taxation without Reallocation: The Distributional Effects of Tax Changes," Discussion Papers of DIW Berlin 2022, DIW Berlin, German Institute for Economic Research.
    9. Pesce, Simone & Errico, Marco & Pollio, Luigi, 2025. "Nonlinearities and heterogeneity in firms response to aggregate fluctuations: what can we learn from machine learning?," Working Paper Series 3107, European Central Bank.
    10. Yoosoon Chang & Soyoung Kim & Joon Y. Park, 2025. "How Do Macroaggregates and Income Distribution Interact Dynamically? A Novel Structural Mixed Autoregression with Aggregate and Functional Variables," Working Papers No 01/2025, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. Laura Liu & Mikkel Plagborg‐Møller, 2023. "Full‐information estimation of heterogeneous agent models using macro and micro data," Quantitative Economics, Econometric Society, vol. 14(1), pages 1-35, January.
    12. Yoosoon Chang & Ana María Herrera & Elena Pesavento, 2023. "Oil prices uncertainty, endogenous regime switching, and inflation anchoring," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 820-839, September.
    13. Marcella Lucchetta, 2025. "Bank Heterogeneity and Crisis Migration: A General Equilibrium Analysis of Systemic Risk," Working Papers 2025: 05, Department of Economics, University of Venice "Ca' Foscari".
    14. Zheng Gong, 2025. "When Does Household Heterogeneity Matter for Aggregate Fluctuations?," CRC TR 224 Discussion Paper Series crctr224_2025_624v2, University of Bonn and University of Mannheim, Germany, revised Sep 2025.
    15. Won-Ki Seo & Dakyung Seong, 2025. "Functional Linear Projection and Impulse Response Analysis," Papers 2503.08364, arXiv.org, revised Apr 2025.
    16. Laura Liu & Mikkel Plagborg-M?ller, 2021. "Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data," CAEPR Working Papers 2021-001 Classification- , Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    17. Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2024. "Common Trends and Country Specific Heterogeneities in Long-Run World Energy Consumption," CAMA Working Papers 2024-04, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Sven Otto & Luis Winter, 2025. "Functional Factor Regression with an Application to Electricity Price Curve Modeling," Papers 2503.12611, arXiv.org, revised Aug 2025.
    19. Yoosoon Chang & Fabio Gomez-Rodriguez & Christian Matthes, 2023. "The Influence of Fiscal and Monetary Policies on the Shape of the Yield Curve," CAMA Working Papers 2023-65, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cpr:ceprdp:16183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://www.cepr.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.