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Working Paper
Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions
Clark, Todd E.; Carriero, Andrea; Marcellino, Massimiliano (2020-09-22)
A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. We note that this pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and ...
Working Papers , Paper 20-02R

Working Paper
Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility
Zito, John; Bognanni, Mark (2019-12-16)
We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model’s posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm’s computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for ...
Working Papers , Paper 19-29

Report
Vulnerable growth
Giannone, Domenico; Boyarchenko, Nina; Adrian, Tobias (2016-09-29)
We study the conditional distribution of GDP growth as a function of economic and financial conditions. Deteriorating financial conditions are associated with an increase in the conditional volatility and a decline in the conditional mean of GDP growth, leading the lower quantiles of GDP growth to vary with financial conditions and the upper quantiles to be stable over time: Upside risks to GDP growth are low in most periods while downside risks increase as financial conditions become tighter. We argue that amplification mechanisms in the financial sector generate the observed growth ...
Staff Reports , Paper 794

Working Paper
The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models
Zha, Tao; Waggoner, Daniel F.; Wu, Hongwei (2014-11-01)
Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the "dynamic striated Metropolis-Hastings (DSMH)" sampler. Grounded in the Metropolis-Hastings algorithm, it draws its strengths from both the equi-energy sampler and the sequential Monte Carlo sampler by avoiding the weaknesses of the straight Metropolis-Hastings algorithm as well as those of importance sampling. In particular, the DSMH sampler possesses the capacity to cope with incredibly irregular distributions that are full ...
FRB Atlanta Working Paper , Paper 2014-21

Working Paper
Manufacturing Sentiment: Forecasting Industrial Production with Text Analysis
Cajner, Tomaz; Crane, Leland D.; Kurz, Christopher J.; Morin, Norman J.; Soto, Paul E.; Vrankovich, Betsy (2024-05-03)
This paper examines the link between industrial production and the sentiment expressed in natural language survey responses from U.S. manufacturing firms. We compare several natural language processing (NLP) techniques for classifying sentiment, ranging from dictionary-based methods to modern deep learning methods. Using a manually labeled sample as ground truth, we find that deep learning models partially trained on a human-labeled sample of our data outperform other methods for classifying the sentiment of survey responses. Further, we capitalize on the panel nature of the data to train ...
Finance and Economics Discussion Series , Paper 2024-026

Report
Forecasting CPI Shelter under Falling Market-Rent Growth
Cotton, Christopher D.; O'Shea, John (2023-02-16)
Shelter (housing) costs constitute a large component of price indexes, including 42 percent of the widely followed core Consumer Price Index (CPI). The shelter prices measured in the CPI capture new and existing renters and tend to lag market rents. This lag explains how in recent months the shelter-price index (CPI shelter) has accelerated while market rents have pulled back. We construct an error correction model using data at the metropolitan statistical area level to forecast how CPI shelter will evolve. We forecast that CPI shelter will grow 5.88 percent from September 2022 to September ...
Current Policy Perspectives

Working Paper
Oil prices and the global economy: is it different this time around?
Mohaddes, Kamiar; Pesaran, M. Hashem (2016-07-21)
The recent plunge in oil prices has brought into question the generally accepted view that lower oil prices are good for the US and the global economy. In this paper, using a quarterly multi-country econometric model, we first show that a fall in oil prices tends relatively quickly to lower interest rates and inflation in most countries, and increase global real equity prices. The effects on real output are positive, although they take longer to materialize (around 4 quarters after the shock). We then re-examine the effects of low oil prices on the US economy over different sub-periods using ...
Globalization Institute Working Papers , Paper 277

Working Paper
An Empirical Evaluation of Some Long-Horizon Macroeconomic Forecasts
Lunsford, Kurt Graden; West, Kenneth D. (2024-09-24)
We use long-run annual cross-country data for 10 macroeconomic variables to evaluate the long-horizon forecast distributions of six forecasting models. The variables we use range from ones having little serial correlation to ones having persistence consistent with unit roots. Our forecasting models include simple time series models and frequency domain models developed in Müller and Watson (2016). For plausibly stationary variables, an AR(1) model and a frequency domain model that does not require the user to take a stand on the order of integration appear reasonably well calibrated for ...
Working Papers , Paper 24-20

Discussion Paper
Economic Predictions with Big Data: The Illusion of Sparsity
Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E. (2018-05-21)
The availability of large data sets, combined with advances in the fields of statistics, machine learning, and econometrics, have generated interest in forecasting models that include many possible predictive variables. Are economic data sufficiently informative to warrant selecting a handful of the most useful predictors from this larger pool of variables? This post documents that they usually are not, based on applications in macroeconomics, microeconomics, and finance.
Liberty Street Economics , Paper 20180521

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