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Working Paper
Evaluating Conditional Forecasts from Vector Autoregressions
Clark, Todd E.; McCracken, Michael W. (2014-09-01)
Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and inflation from a VAR, based on conditions on the short-term interest rate. Throughout ...
Working Papers , Paper 2014-25

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
Facts and Fiction in Oil Market Modeling
Kilian, Lutz (2019-09-06)
Baumeister and Hamilton (2019a) assert that every critique of their work on oil markets by Kilian and Zhou (2019a) is without merit. In addition, they make the case that key aspects of the economic and econometric analysis in the widely used oil market model of Kilian and Murphy (2014) and its precursors are incorrect. Their critiques are also directed at other researchers who have worked in this area and, more generally, extend to research using structural VAR models outside of energy economics. The purpose of this paper is to help the reader understand what the real issues are in this ...
Working Papers , Paper 1907

Working Paper
Sovereigns versus Banks: Credit, Crises, and Consequences
Taylor, Alan M.; Jordà, Òscar; Schularick, Moritz (2013)
Two separate narratives have emerged in the wake of the Global Financial Crisis. One speaks of private financial excess and the key role of the banking system in leveraging and deleveraging the economy. The other emphasizes the public sector balance sheet over the private and worries about the risks of lax fiscal policies. However, the two may interact in important and understudied ways. This paper studies the co-evolution of public and private sector debt in advanced countries since 1870. We find that in advanced economies financial stability risks have come from private sector credit booms ...
Working Paper Series , Paper 2013-37

Working Paper
Tests of Conditional Predictive Ability: A Comment
McCracken, Michael W. (2019-07-29)
We investigate a test of equal predictive ability delineated in Giacomini and White (2006; Econometrica). In contrast to a claim made in the paper, we show that their test statistic need not be asymptotically Normal when a fixed window of observations is used to estimate model parameters. An example is provided in which, instead, the test statistic diverges with probability one under the null. Simulations reinforce our analytical results.
Working Papers , Paper 2019-18

Working Paper
The Role of the Prior in Estimating VAR Models with Sign Restrictions
Inoue, Atsushi; Kilian, Lutz (2020-12-03)
Several recent studies have expressed concern that the Haar prior typically imposed in estimating sign-identified VAR models may be unintentionally informative about the implied prior for the structural impulse responses. This question is indeed important, but we show that the tools that have been used in the literature to illustrate this potential problem are invalid. Specifically, we show that it does not make sense from a Bayesian point of view to characterize the impulse response prior based on the distribution of the impulse responses conditional on the maximum likelihood estimator of ...
Working Papers , Paper 2030

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 2020-7

Working Paper
Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR
Owyang, Michael T.; McCracken, Michael W.; Sekhposyan, Tatevik (2015-10-08)
We assess point and density forecasts from a mixed-frequency vector autoregression (VAR) to obtain intra-quarter forecasts of output growth as new information becomes available. The econometric model is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. We impose restrictions on the VAR to account explicitly for the temporal ordering of the data releases. Because this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. The ...
Working Papers , Paper 2015-30

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Bognanni, Mark; Herbst, Edward (2014-11-12)
Vector autoregressions with Markov-switching parameters (MS-VARs) offer dramatically better data fit than their constant-parameter predecessors. However, computational complications, as well as negative results about the importance of switching in parameters other than shock variances, have caused MS-VARs to see only sparse usage. For our first contribution, we document the effectiveness of Sequential Monte Carlo (SMC) algorithms at estimating MSVAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of being simpler to implement, readily parallelizable, ...
Working Papers (Old Series) , Paper 1427

Working Paper
Joint Bayesian Inference about Impulse Responses in VAR Models
Kilian, Lutz; Inoue, Atsushi (2020-07-17)
Structural VAR models are routinely estimated by Bayesian methods. Several recent studies have voiced concerns about the common use of posterior median (or mean) response functions in applied VAR analysis. In this paper, we show that these response functions can be misleading because in empirically relevant settings there need not exist a posterior draw for the impulse response function that matches the posterior median or mean response function, even as the number of posterior draws approaches infinity. As a result, the use of these summary statistics may distort the shape of the impulse ...
Working Papers , Paper 2022

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