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Working Paper
A Likelihood-Based Comparison of Macro Asset Pricing Models
Chen, Andrew Y.; Wasyk, Rebecca; Winkler, Fabian (2017-03)
We estimate asset pricing models with multiple risks: long-run growth, long-run volatility, habit, and a residual. The Bayesian estimation accounts for the entire likelihood of consumption, dividends, and the price-dividend ratio. We find that the residual represents at least 80% of the variance of the price-dividend ratio. Moreover, the residual tracks most recognizable features of stock market history such as the 1990's boom and bust. Long run risks and habit contribute primarily in crises. The dominance of the residual comes from the low correlation between asset prices and consumption ...
Finance and Economics Discussion Series , Paper 2017-024

Working Paper
Complementarity and Macroeconomic Uncertainty
Plante, Michael D.; Atkinson, Tyler; Throckmorton, Nathaniel A.; Richter, Alexander W. (2020-03-31)
Macroeconomic uncertainty—the conditional volatility of the unforecastable component of a future value of a time series—shows considerable variation in the data. A typical assumption in business cycle models is that production is Cobb-Douglas. Under that assumption, this paper shows there is usually little, if any, endogenous variation in output uncertainty, and first moment shocks have similar effects in all states of the economy. When the model departs from Cobb-Douglas production and assumes capital and labor are gross complements, first-moment shocks have state-dependent effects and ...
Working Papers , Paper 2009

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Herbst, Edward P.; Bognanni, Mark (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
The Roles of Price Points and Menu Costs in Price Rigidity
Knotek, Edward S. (2019-11-12)
Macroeconomic models often generate nominal price rigidity via menu costs. This paper provides empirical evidence that treating menu costs as a structural explanation for sticky prices may be spurious. Using scanner data, I note two empirical facts: (1) price points, embodied in nine-ending prices, account for approximately two-thirds of prices; and (2) at the conclusion of sales, post-sale prices return to their pre-sale levels more than three-fourths of the time. I construct a model that nests roles for menu costs and price points and estimate model variants. Excluding the two facts yields ...
Working Papers , Paper 19-23

Working Paper
Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values
Cook, Thomas R.; Modig, Zach; Palmer, Nathan M. (2024-09-20)
Machine learning and artificial intelligence are often described as “black boxes.” Traditional linear regression is interpreted through its marginal relationships as captured by regression coefficients. We show that the same marginal relationship can be described rigorously for any machine learning model by calculating the slope of the partial dependence functions, which we call the partial marginal effect (PME). We prove that the PME of OLS is analytically equivalent to the OLS regression coefficient. Bootstrapping provides standard errors and confidence intervals around the point ...
Finance and Economics Discussion Series , Paper 2024-075

Working Paper
Minimum distance estimation of possibly non-invertible moving average models
Ng, Serena; Gospodinov, Nikolay (2013)
This paper considers estimation of moving average (MA) models with non-Gaussian errors. Information in higher-order cumulants allows identification of the parameters without imposing invertibility. By allowing for an unbounded parameter space, the generalized method of moments estimator of the MA(1) model has classical (root-T and asymptotic normal) properties when the moving average root is inside, outside, and on the unit circle. For more general models where the dependence of the cumulants on the model parameters is analytically intractable, we consider simulation-based estimators with two ...
FRB Atlanta Working Paper , Paper 2013-11

Working Paper
Inference in Bayesian Proxy-SVARs
Rubio-Ramirez, Juan F.; Waggoner, Daniel F.; Arias, Jonas E. (2021-01-14)
Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop an algorithm for exact finite sample inference in this class of time series models, commonly known as Proxy-SVARs. Our algorithm makes independent draws from any posterior distribution over the structural parameterization of a Proxy-SVAR. Our approach allows researchers to simultaneously use proxies and traditional zero and sign restrictions to identify structural shocks. We illustrate our methods with two applications. In particular, we show how to generalize the ...
FRB Atlanta Working Paper , Paper 2018-16a

Working Paper
exuber: Recursive Right-Tailed Unit Root Testing with R
http://fedora:8080/fcrepo/rest/objects/authors/; Martinez-Garcia, Enrique; Pavlidis, Efthymios (2020-05-12)
This paper introduces the R package exuber for testing and date-stamping periods of mildly explosive dynamics (exuberance) in time series. The package computes test statistics for the supremum ADF test (SADF) of Phillips, Wu and Yu (2011), the generalized SADF (GSADF) of Phillips, Shi and Yu (2015a,b), and the panel GSADF proposed by Pavlidis, Yusupova, Paya, Peel, Martínez-García, Mack and Grossman (2016); generates finite-sample critical values based on Monte Carlo and bootstrap methods; and implements the corresponding date-stamping procedures. The recursive least-squares algorithm that ...
Globalization Institute Working Papers , Paper 383

Working Paper
Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach
Knotek, Edward S.; Zaman, Saeed (2020-10-22)
We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework: (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. Together these features provide density nowcasts that can accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our ...
Working Papers , Paper 20-31

Working Paper
CardSim: A Bayesian Simulator for Payment Card Fraud Detection Research
Allen, Jeffrey (2025-02-28)
Payment fraud has been high in recent years, and as criminals gain access to capability-enhancing generative AI tools, there is a growing need for innovative fraud detection research. However, the pace, diversity, and reproducibility of such research are inhibited by the dearth of publicly available payment transaction data. A few payment simulation methodologies have been developed to help narrow the payment transaction data gap without compromising important data privacy and security expectations. While these simulation approaches have enabled research advancements, more work is needed to ...
Finance and Economics Discussion Series , Paper 2025-017

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