Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to generate conditional forecasts tend to be very computationally intensive, especially when working with large VARs or when multiple soft and hard constraints are imposed at once. We introduce a novel precision-based sampler that is fast, scales well, and yields conditional forecasts from both hard or soft constraints. We show in a simulation study that the proposed method produces forecasts that are identical to those from the existing algorithms but in a fraction of the time. We then illustrate the performance of our method in a large Bayesian VAR where we impose, simultaneously, a mix of soft and hard constraints on the future trajectories of a few key US macroeconomic indicators over the 2020–2022 time period.
We estimate the term structure of cash flow risk and its price of risk for the most prominent equity anomalies, at different frequencies, by directly modeling the dividend growth series instead of relying on a VAR-residual approach. We find the term structure of cash flow risk to be upward sloping for most anomaly portfolios. Moreover, the price of cash flow risk appears to be anomaly-specific – different anomalies tend to display heterogeneous sensitivity to cash flow news – and frequency-dependent – for a given anomaly, this sensitivity varies with the horizon at which portfolios are evaluated.
We use high-frequency data on firms’ dividend and buyback suspensions to estimate the effect on firm value from preserving cash during periods of financial market distress such as the Global Financial Crisis and the Covid-19 pandemic. Our results suggest that saving one percent in cash by suspending dividends is associated with a 2.5 percent increase in firm value. New dynamic tests based on the sequencing of firms’ financing decisions suggest that firm behavior was more in line with the Myers and Majluf (1984) pecking order theory during the pandemic than during the Global Financial Crisis.
Pettenuzzo, D., Timmermann,
A., Sabbatucci,
R. (2023), Payout suspensions during the Covid-19 pandemic,
Economics Letters, forthcoming
[Published
version]
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]
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]
Korobilis,
D., Pettenuzzo, D. (2020) Machine Learning Econometrics: Bayesian
Algorithms and Methods, Oxford Research Encyclopedia: Economics
and Finance
[Published
version] [Working
paper]
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]
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]
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]
Korobilis,
D., Pettenuzzo, D. (2019) Adaptive Hierarchical Priors for
High-Dimensional Vector Autoregressions, Journal of
Econometrics, 212: 241-271
[Published
version] [Working
paper]
Koop, G.,
Korobilis,
D. , Pettenuzzo, D. (2019) Bayesian Compressed Vector
Autoregressions, Journal of Econometrics, 210:
135-154
[Published
version] [Working
paper] [Online
Appendix]
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]
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]
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]
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]
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]
Pettenuzzo, D., Timmermann,
A., Valkanov, R.
(2014) Forecasting Stock Returns under Economic Constraints,
Journal of Financial Economics, 114: 517-553
[Published
version] [Working
paper]
Pettenuzzo, D., White,
H. (2014) Granger Causality, Exogeneity, Cointegration, and Economic
Policy Analysis, Journal of Econometrics, 178:
316-330
[Published
version] [Working
paper]
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]
Pesaran,
H., Pettenuzzo, D., Timmermann,
A. (2007) Learning, Structural Instability, and Present Value
Calculations, Econometric Reviews, 26: 253-288
[Published
version] [Working
paper]
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]
Pettenuzzo, D., Sabbatucci, R. Timmermann, A. High-frequency fundamentals
Kastner, G., Pettenuzzo, D., Timmermann, A. Modeling Stock-Bond Correlations