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Working Paper
Monetary Policy, Self-Fulfilling Expectations and the U.S. Business Cycle
Nicolo, Giovanni (2020-05-05)
I estimate a medium-scale New-Keynesian model and relax the conventional assumption that the central bank adopted an active monetary policy by pursuing inflation and output stability over the entire post-war period. Even after accounting for a rich structure, I find that monetary policy was passive prior to the Volcker disinflation. Sunspot shocks did not represent quantitatively relevant sources of volatility. By contrast, such passive interest rate policy accommodated fundamental productivity and cost shocks that de-anchored inflation expectations, propagated via self-fulfilling inflation ...
Finance and Economics Discussion Series , Paper 2020-035

Working Paper
Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR
Owyang, Michael T.; McCracken, Michael W.; Sekhposyan, Tatevik (2020-04-10)
We use a mixed-frequency vector autoregression to obtain intraquarter point and density forecasts as new, high frequency information becomes available. This model, delineated in Ghysels (2016), is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. As this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. We obtain high-frequency updates to forecasts by treating new data releases as conditioning information. The same ...
Working Papers , Paper 2015-030

Journal Article
Factor-based prediction of industry-wide bank stress
Grover, Sean P.; McCracken, Michael W. (2014)
This article investigates the use of factor-based methods for predicting industry-wide bank stress. Specifically, using the variables detailed in the Federal Reserve Board of Governors? bank stress scenarios, the authors construct a small collection of distinct factors. We then investigate the predictive content of these factors for net charge-offs and net interest margins at the bank industry level. The authors find that the factors do have significant predictive content, both in and out of sample, for net interest margins but significantly less predictive content for net charge-offs. ...
Review , Volume 96 , Issue 2 , Pages 173-194

Working Paper
Facts and Fiction in Oil Market Modeling
Kilian, Lutz (2020-12-21)
A series of recent articles has called into question the validity of VAR models of the global market for crude oil. These studies seek to replace existing oil market models by structural VAR models of their own based on different data, different identifying assumptions, and a different econometric approach. Their main aim has been to revise the consensus in the literature that oil demand shocks are a more important determinant of oil price fluctuations than oil supply shocks. Substantial progress has been made in recent years in sorting out the pros and cons of the underlying econometric ...
Working Papers , Paper 1907

Working Paper
Monetary Policy and Macroeconomic Stability Revisited
Kurozumi, Takushi; Hirose, Yasuo; Van Zandweghe, Willem (2017-01-04)
A large literature with canonical New Keynesian models has established that the Fed's policy change from a passive to an active response to inflation led to U.S. macroeconomic stability after the Great Inflation of the 1970s. We revisit this view by estimating a staggered price model with trend inflation using a Bayesian method that allows for equilibrium indeterminacy and adopts a sequential Monte Carlo algorithm. {{p}} The model empirically outperforms a canonical New Keynesian model and demonstrates an active response to inflation even in the Great Inflation era, during which the U.S. ...
Research Working Paper , Paper RWP 17-1

Working Paper
Mining for Oil Forecasts
Calomiris, Charles W.; Cakir Melek, Nida; Mamaysky, Harry (2020-12-23)
In this paper, we study the usefulness of a large number of traditional determinants and novel text-based variables for in-sample and out-of-sample forecasting of oil spot and futures returns, energy company stock returns, oil price volatility, oil production, and oil inventories. After carefully controlling for small-sample biases, we find compelling evidence of in-sample predictability. Our text measures hold their own against traditional variables for oil forecasting. However, none of this translates to out-of-sample predictability until we data mine our set of predictive variables. Our ...
Research Working Paper , Paper RWP 20-20

Working Paper
Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks
Chudik, Alexander; Sharifvaghefi, Mahrad; Pesaran, M. Hashem (2021-04-17)
This paper is concerned with the problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows and exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection, which is complicated by time variations in the effects of signal variables. In this study we investigate whether or not we should use weighted observations at the variable selection stage in the presence of ...
Globalization Institute Working Papers , Paper 394

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Bognanni, Mark; Herbst, Edward (2015-12-18)
Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC) estimators accurately estimate Bayesian MS-VAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. For our second contribution, we use SMC's ...
Finance and Economics Discussion Series , Paper 2015-116

Working Paper
Bootstrapping out-of-sample predictability tests with real-time data
Goncalves, Silvia; McCracken, Michael W.; Yao, Yongxu (2024-09-03)
In this paper we develop a block bootstrap approach to out-of-sample inference when real-time data are used to produce forecasts. In particular, we establish its first-order asymptotic validity for West-type (1996) tests of predictive ability in the presence of regular data revisions. This allows the user to conduct asymptotically valid inference without having to estimate the asymptotic variances derived in Clark and McCracken’s (2009) extension of West (1996) when data are subject to revision. Monte Carlo experiments indicate that the bootstrap can provide satisfactory finite sample size ...
Working Papers , Paper 2023-029

Discussion Paper
Measuring and Managing COVID-19 Model Risk
Jensen, Mark J. (2020-06-18)
One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation's (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
Policy Hub , Paper 2020-07

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