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Nonparametric Local Projections

Author

Listed:
  • Goncalves, Silvia
  • Herrera, Ana Maria
  • Kilian, Lutz
  • Pesavento, Elena

Abstract

Nonlinearities play an increasingly important role in applied work when studying the responses of macroeconomic aggregates to policy shocks. Seemingly natural adaptations of the popular local linear projection estimator to nonlinear settings may fail to recover the population responses of interest. In this paper we study the properties of an alternative nonparametric local projection estimator of the conditional and unconditional responses of an outcome variable to an observed identified shock. We discuss alternative ways of implementing this estimator and how to allow for data-dependent tuning parameters. Our results are based on data generating processes that involve, respectively, nonlinearly transformed regressors, state-dependent coefficients, and nonlinear interactions between shocks and state variables. Monte Carlo simulations show that a local-linear specification of the estimator tends to work well in reasonably large samples and is robust to nonlinearities of unknown form.

Suggested Citation

  • Goncalves, Silvia & Herrera, Ana Maria & Kilian, Lutz & Pesavento, Elena, 2024. "Nonparametric Local Projections," CEPR Discussion Papers 19684, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:19684
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    Cited by:

    1. Klieber, Karin & Coulombe, Philippe Goulet, 2025. "Opening the black box of local projections," Working Paper Series 3105, European Central Bank.
    2. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Machine learning the macroeconomic effects of financial shocks," Economics Letters, Elsevier, vol. 250(C).
    3. Niko Hauzenberger Massimiliano Marcellino Michael Pfarrhofer Anna Stelzer, 2026. "Direct Gaussian Process Predictive Regressions with Mixed Frequency Data," BAFFI CAREFIN Working Papers 26265, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    4. Joel M. David & Raffaella Giacomini & Xiyu Jiao & Weining Wang, 2026. "Causal State-Dependent Local Projections," Papers 2605.05404, arXiv.org, revised May 2026.
    5. Martin Bruns & Helmut Lütkepohl, 2026. "Review of Proxy Vector Autoregressive Analysis," Reviews of Economic Literature, Stanford University Press, vol. 1.
    6. Fabrizio Renzi, 2025. "New evidence on state-dependent fiscal multipliers," Temi di discussione (Economic working papers) 1512, Bank of Italy, Economic Research and International Relations Area.
    7. Martin Bruns & Helmut Lütkepohl, 2026. "Review of Proxy Vector and Autoregressive Analysis," University of East Anglia School of Economics Working Paper Series 2026-01, School of Economics, University of East Anglia, Norwich, UK..
    8. Damiano Di Francesco & Omar Pietro Carnevale, 2025. "Are Hysteresis Effects Nonlinear?," LEM Papers Series 2025/32, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

    More about this item

    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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