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Working Paper
Latent Variables Analysis in Structural Models: A New Decomposition of the Kalman Smoother
Chung, Hess T.; Fuentes-Albero, Cristina; Paustian, Matthias; Pfajfar, Damjan (2020-12-04)
This paper advocates chaining the decomposition of shocks into contributions from forecast errors to the shock decomposition of the latent vector to better understand model inference about latent variables. Such a double decomposition allows us to gauge the inuence of data on latent variables, like the data decomposition. However, by taking into account the transmission mechanisms of each type of shock, we can highlight the economic structure underlying the relationship between the data and the latent variables. We demonstrate the usefulness of this approach by detailing the role of ...
Finance and Economics Discussion Series , Paper 2020-100

Report
Inflation in the Great Recession and New Keynesian models
Giannoni, Marc; Del Negro, Marco; Schorfheide, Frank (2013)
It has been argued that existing DSGE models cannot properly account for the evolution of key macroeconomic variables during and following the recent great recession. We challenge this argument by showing that a standard DSGE model with financial frictions available prior to the recent crisis successfully predicts a sharp contraction in economic activity along with a modest and protracted decline in inflation following the rise in financial stress in the fourth quarter of 2008. The model does so even though inflation remains very dependent on the evolution of economic activity and of monetary ...
Staff Reports , Paper 618

Working Paper
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
Chudik, Alexander; Sharifvaghefi, Mahrad; Pesaran, M. Hashem (2024-08-05)
This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigates the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also ...
Globalization Institute Working Papers , Paper 394

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

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

Working Paper
A one-covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models
Chudik, Alexander; Kapetanios, George; Pesaran, M. Hashem (2016-11-01)
Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised regression has become the de facto benchmark technique used to trade off parsimony and fit when the number of possible covariates is large, often much larger than the number of available observations. However, issues such as the choice of a penalty function and tuning parameters associated with the use ...
Globalization Institute Working Papers , Paper 290

Working Paper
Understanding the Estimation of Oil Demand and Oil Supply Elasticities
Kilian, Lutz (2020-09-03)
This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models. I not only summarize the state of the literature, but also draw attention to a number of econometric problems that have been overlooked in this literature. Once these problems are recognized, seemingly conflicting conclusions in the recent literature can be resolved. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the ...
Working Papers , Paper 2027

Working Paper
Refining the Workhorse Oil Market Model
Zhou, Xiaoqing (2019-09-06)
The Kilian and Murphy (2014) structural vector autoregressive model has become the workhorse model for the analysis of oil markets. I explore various refinements and extensions of this model, including the effects of (1) correcting an error in the measure of global real economic activity, (2) explicitly incorporating narrative sign restrictions into the estimation, (3) relaxing the upper bound on the impact price elasticity of oil supply, (4) evaluating the implied posterior distribution of the structural models, and (5) extending the sample. I demonstrate that the substantive conclusions of ...
Working Papers , Paper 1910

Working Paper
On the Real-Time Predictive Content of Financial Conditions Indices for Growth
McCracken, Michael W.; Amburgey, Aaron (2022-06-03)
We provide evidence on the real-time predictive content of the National Financial Conditions Index (NFCI), for conditional quantiles of U.S. real GDP growth. Our work is distinct from the literature in two specific ways. First, we construct (unofficial) real-time vintages of the NFCI. This allows us to conduct out-of-sample analysis without introducing the kind of look-ahead biases that are naturally introduced when using a single current vintage. We then develop methods for conducting asymptotic inference on tests of equal tick loss between nested quantile regression models when the data are ...
Working Papers , Paper 2022-003

Working Paper
Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks
Chudik, Alexander; Pesaran, M. Hashem; Sharifvaghefi, Mahrad (2020-08-19)
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 or exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection. It is clear that, in the absence of breaks, researchers should weigh the observations equally at both the variable selection and forecasting stages. In this study, we investigate whether or not we should use ...
Globalization Institute Working Papers , Paper 394

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