Working papers

  • Pettenuzzo, D., Poon, A., Zhu, D., Revealing Growth-at-Risk Through Daily Corporate Activity: New Evidence from a Functional Quantile MIDAS Model (January 2026)
    [Link to SSRN]

This paper introduces a new Growth-at-Risk (GaR) framework that incorporates daily firm-level corporate activity data, an information source previously unused in GaR analysis. Motivated by macro-finance theory emphasizing the role of firm balance sheets in amplifying shocks, we exploit a novel dataset of daily accounting-based indicators capturing firms’ liquidity, leverage, and operating performance. We link these high-frequency measures to quarterly GDP by developing a quantile MIDAS model with a continuous Fourier weighting scheme. We apply this framework to U.S. data and show that both the levels and cross-sectional dispersion of corporate activity provide significant early-warning signals of macroeconomic stress. Our model delivers substantial improvements in predictive accuracy relative to the benchmark GaR approach and MIDAS-based extensions using monthly financial indicators. The results highlight the value of high-frequency corporate information for real-time monitoring of downside macroeconomic risks.

This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches. In recent times, LLMs have surged in popularity for forecasting due to their ability to capture intricate patterns in data and quickly adapt across very different domains. However, their effectiveness in forecasting macroeconomic time series data compared to conventional methods remains an area of interest. To address this, we conduct a rigorous evaluation of LLMs against traditional macro forecasting methods, using as common ground the FRED-MD database. Our findings provide valuable insights into the strengths and limitations of LLMs in forecasting macroeconomic time series, shedding light on their applicability in real-world scenarios.

We develop a novel framework for modeling and forecasting time series of count data, extending the traditional Vector Autoregression (VAR) framework to accommodate count-like outcomes. Our approach allows for joint modeling of multiple count and continuous variables, capturing their dynamic interactions within a unified system. By introducing a latent utility-based structure and incorporating multivariate stochastic volatility, our method can flexibly handle over-dispersion, skewness, and time-varying volatility in the data. We cast our model in a Bayesian framework and introduce a novel state-space representation and an efficient sampler to handle its estimation. An extensive simulation study and two empirical macroeconomic applications illustrate the robustness and forecasting accuracy of the proposed approach.

What do companies’ 10-Q filings reveal about the state of the macro economy and do specific accounting variables contain particularly relevant information? To address these questions, we analyze the lead-lag patterns of more than twenty accounting variables in relation to aggregate economic activity. We develop new daily corporate account business activity indices that aggregate firm-level accounting information while controlling for shifts in the composition of announcers and reducing firm-specific noise. Our new indices show that firm liquidity becomes significantly lower while corporate debt grows significantly faster several months prior to recessions, and thus can be used as leading indicators. Conversely, operations, earnings, and profitability measures tend to be significantly lower after recessions, suggesting they are mostly lagging, pro-cyclical indicators of economic activity.

Published and forthcoming articles

  1. Chan, J., Pettenuzzo, D., Poon, A., Zhu, D. (2025), Conditional Forecasts in Large Bayesian VARs with Multiple Equality and Inequality Constraints , Journal of Economic Dynamics and Control, 173
    [Published version]     [Link to SSRN]

  2. Pettenuzzo, D., Timmermann, A., Sabbatucci, R. (2023), Payout suspensions during the Covid-19 pandemic, Economics Letters, 224:111024
    [Published version]    

  3. Pettenuzzo, D., Timmermann, A., Sabbatucci, R. (2023), Dividend Suspensions and Cash Flows During the Covid-19 Pandemic: A Dynamic Econometric Model, Journal of Econometrics, 235:1522–1541
    [Published version]     [Working paper]

  4. Pettenuzzo, D., Timmermann, A., Yong, S. (2022), Corrigendum to “Predictability of stock returns and asset allocation under structural breaks” [J. Econometrics 164 (2011) 60–78], Journal of Econometrics, 227: 513-517
    [Published version]    

  5. Pettenuzzo, D., Timmermann, A., Sabbatucci, R. (2021), Outlasting the pandemic: Corporate payout and financing decisions during Covid-19, COVID Economics, 78
    [Published version]     [Working paper]    

  6. Korobilis, D., Pettenuzzo, D. (2020) Machine Learning Econometrics: Bayesian Algorithms and Methods, Oxford Research Encyclopedia: Economics and Finance
    [Published version]     [Working paper]    

  7. Pettenuzzo, D., Timmermann, A., Sabbatucci, R. (2020) Cash Flow News and Stock Price Dynamics, Journal of Finance, 75: 2221-2270
    [Published version]     [Working paper]     [Online Appendix]

  8. Carvalho, C., Fisher, J., Pettenuzzo, D. (2020) Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models, Annals of Applied Statistics, 14: 299-338
    [Published version]     [Working paper]    

  9. Pan, Z., Pettenuzzo, D., Wang, Y. (2020) Forecasting Stock Returns: A Predictor-constrained Approach Journal of Empirical Finance, 55: 200-217
    [Published version]     [Working paper]    

  10. Korobilis, D., Pettenuzzo, D. (2019) Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions, Journal of Econometrics, 212: 241-271
    [Published version]     [Working paper]    

  11. Koop, G., Korobilis, D. , Pettenuzzo, D. (2019) Bayesian Compressed Vector Autoregressions, Journal of Econometrics, 210: 135-154
    [Published version]     [Working paper]     [Online Appendix]

  12. Gargano, A., Pettenuzzo, D., Timmermann, A. (2019) Bond Return Predictability: Economic Value and Links to the Macroeconomy, Management Science, 65: 508-540
    [Published version]     [Working paper]     [Online Appendix]

  13. Metaxoglou, K., Pettenuzzo, D., Smith, A. (2019) Option-Implied Equity Premium Predictions via Entropic Tilting, Journal of Financial Econometrics, 17: 559-586
    [Published version]     [Working paper]     [Online Appendix]

  14. Pettenuzzo, D., Timmermann, A. (2017) Forecasting Macroeconomic Variables Under Model Instability, Journal of Business and Economic Statistics, 35: 183-201
    [Published version]     [Working paper]     [Online Appendix]

  15. Pettenuzzo, D., Timmermann, A., Valkanov, R. (2016) A MIDAS Approach to Modeling First and Second Moment Dynamics, Journal of Econometrics, 193: 315-334
    [Published version]     [Working paper]

  16. Pettenuzzo, D., Ravazzolo, F. (2016) Optimal Potfolio Choice under Decision-Based Model Combinations, Journal of Applied Econometrics, 31: 1312-1332
    [Published version]     [Working paper]     [Online Appendix]

  17. Pettenuzzo, D., Timmermann, A., Valkanov, R. (2014) Forecasting Stock Returns under Economic Constraints, Journal of Financial Economics, 114: 517-553
    [Published version]     [Working paper]

  18. Pettenuzzo, D., White, H. (2014) Granger Causality, Exogeneity, Cointegration, and Economic Policy Analysis, Journal of Econometrics, 178: 316-330
    [Published version]     [Working paper]

  19. Pettenuzzo, D., Timmermann, A. (2011) Predictability of Stock Returns and Asset Allocation under Structural Breaks, Journal of Econometrics, 164: 60-78
    [Published version]     [Working paper]     [Matlab codes]

  20. Pesaran, H., Pettenuzzo, D., Timmermann, A. (2007) Learning, Structural Instability, and Present Value Calculations, Econometric Reviews, 26: 253-288
    [Published version]     [Working paper]

  21. Pesaran, H., Pettenuzzo, D., Timmermann, A. (2006) Forecasting Time Series subject to Structural Breaks, Review of Economic Studies, 73: 1057-1084
    [Published version]     [Working paper]     [Matlab codes]


Work in progress and older working papers