About

Oscar Jorda is a professor of economics at UC Davis, specializing in econometrics and applied macroeconomics. He holds a Ph.D. in Economics from the University of California, San Diego, obtained in 1997, and a B.S. in Economics from Universidad Complutense de Madrid, earned in 1991. His research focuses on time series econometrics, macroeconomics, monetary economics, and international finance. Professor Jorda has contributed to the understanding of the long-run effects of monetary policy, the economic consequences of pandemics, and issues related to bank solvency and liquidity during crises. He teaches undergraduate and graduate courses in econometrics and time series analysis, sharing his expertise with students at UC Davis.

Research topics

• Monetary economics
• Economics
• Computer Science
• Market economy
• Development economics
• Macroeconomics
• Finance
• Labour economics
• Econometrics
• Mathematics

Selected publications

• Financial Conditions and Capital Investment Choices

  Federal Reserve Bank of San Francisco, Working Paper Series · 2026-02-26

  article1st authorCorresponding

  We show, both theoretically and empirically, that tight financial conditions shift investment toward cheaper but less energy-efficient capital. In a small open-economy model with vintage capital, higher financing costs reduce the present value of future energy savings, tilting firms’ choices along a cost efficiency frontier. Using 150 years of macroeconomic and energy data from 17 advanced economies, we find that tighter financial conditions reduce output, capital, and total energy consumption, but raise the amount of energy per unit of capital (energy intensity), a composition effect that persists for 6 to 8 years. Tight financial conditions lower energy use in the short run by depressing activity, but increase energy use in the medium run through worse energy efficiency.

  PublisherDOI

• Uniform Validity of the Subset Anderson-Rubin Test under Heteroskedasticity and Nonlinearity

  ArXiv.org · 2025-07-01

  preprintOpen access

  We consider the Anderson-Rubin (AR) statistic for a general set of nonlinear moment restrictions. The statistic is based on the criterion function of the continuous updating estimator (CUE) for a subset of parameters not constrained under the Null. We treat the data distribution nonparametrically with parametric moment restrictions imposed under the Null. We show that subset tests and confidence intervals based on the AR statistic are uniformly valid over a wide range of distributions that include moment restrictions with general forms of heteroskedasticity. We show that the AR based tests have correct asymptotic size when parameters are unidentified, partially identified, weakly or strongly identified. We obtain these results by constructing an upper bound that is using a novel perturbation and regularization approach applied to the first order conditions of the CUE. Our theory applies to both cross-sections and time series data and does not assume stationarity in time series settings or homogeneity in cross-sectional settings.

  PublisherOA PDFDOI

• Local Projections Bootstrap Inference

  Federal Reserve Bank of San Francisco, Working Paper Series · 2025-10-03

  article

  Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local projection at horizon 1. Although convenient, it is well documented that a VAR can be a poor approximation to impulse dynamics at horizons beyond its lag length. In this paper we assume instead that the precise form of the parametric model generating the data is not known. If one is willing to assume that the DGP is perhaps an infinite order process, a larger class of models can be accommodated and more tailored bootstrap procedures can be constructed. Using the moving average representation of the data, we construct appropriate bootstrap procedures.

  PublisherDOI

• Local Projections

  Journal of Economic Literature · 2025-03-01 · 72 citations

  article1st authorCorresponding

  A central question in applied research is to estimate the effect of an exogenous intervention or shock on an outcome. The intervention can affect the outcome and controls on impact and over time. Moreover, there can be subsequent feedback between outcomes, controls, and the intervention. Many of these interactions can be untangled using local projections. This method’s simplicity makes it a convenient and versatile tool in the empiricist’s kit, one that is generalizable to complex settings. This article reviews the state of the art for the practitioner and discusses best practices and possible extensions of local projections methods, along with their limitations. (JEL C32, C33, C36, E23, E24)

  PublisherDOI

• Decomposing the monetary policy multiplier

  Journal of Monetary Economics · 2025-05-01 · 2 citations

  articleCorresponding
  PublisherDOI

• Inference for local projections

  Econometrics Journal · 2025-01-30 · 3 citations

  article

  Summary Inference for impulse responses estimated with local projections presents interesting challenges and opportunities. Analysts typically want to assess the precision of individual estimates, explore the dynamic evolution of the response over particular regions, and generally determine whether the impulse generates a response that is any different from the null of no effect. Each of these goals requires a different approach to inference. In this article, we provide an overview of results that have appeared in the literature in the past twenty years along with some new procedures that we introduce here.

  PublisherDOI

• Local Projections Bootstrap Inference

  ArXiv.org · 2025-09-22

  preprintOpen accessSenior author

  Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local projection at horizon 1. Although convenient, it is well documented that a VAR can be a poor approximation to impulse dynamics at horizons beyond its lag length. In this paper we assume instead that the precise form of the parametric model generating the data is not known. If one is willing to assume that the DGP is perhaps an infinite order process, a larger class of models can be accommodated and more tailored bootstrap procedures can be constructed. Using the moving average representation of the data, we construct appropriate bootstrap procedures.

  PublisherOA PDFDOI

• Comment: Dynamic Causal Effects in a Nonlinear World: The Good, the Bad, and the Ugly

  Journal of Business and Economic Statistics · 2025-10-02

  article1st authorCorresponding
  PublisherDOI

• A Local Projections Approach to Difference‐in‐Differences

  Journal of Applied Econometrics · 2025-07-19 · 28 citations

  articleOpen access

  ABSTRACT We propose a local projections (LPs)‐based difference‐in‐differences (DiD) approach that subsumes many of the recent solutions proposed in the literature to address possible biases arising from negative weighting. We combine LPs with a flexible “clean control” condition to define appropriate sets of treated and control units. Our proposed LP‐DiD estimator can be implemented with various weighting and normalization schemes for different target estimands, can be extended to include covariates or accommodate nonabsorbing treatment, and is simple and fast to implement. A simulation and two empirical applications demonstrate that the LP‐DiD estimator performs well in common applied settings.

  PublisherDOI

• A New Labor Market Stress Indicator

  Federal Reserve Bank of San Francisco, Working Paper Series · 2025-12-31

  article

  Recessions are periods where the labor market deteriorates rapidly. Supporting business conditions to prevent such deterioration is a core objective of policymakers. In this paper we construct a labor market stress indicator (LMSI) primarily based on state-level unemployment insurance claims data that are observable as often as at weekly frequency. By examining both the geographical spread and the depth of labor market stress buildup, we provide an early indicator whose main function is to alert policymakers of potential economic slowdowns. Because the majority (but not all) of these slowdowns coincide with NBER recessions, the LMSI is also a useful signal of whether the economy is in recession. The paper then evaluates this feature of the LMSI compared with other recent indicators and highlights the strengths and weaknesses of each.

  PublisherDOI

Frequent coauthors

• Alan M. Taylor

  Centre for Economic Policy Research

  227 shared

• Moritz Schularick

  155 shared

• Massimiliano Marcellino

  53 shared

• Guido M. Kuersteiner

  University of Maryland, College Park

  47 shared

• Travis J. Berge

  Federal Reserve Board of Governors

  33 shared

• Malte Knüppel

  Deutsche Bundesbank

  29 shared

• Joshua D. Angrist

  28 shared

• Fernanda Nechio

  21 shared