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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
Financial Conditions and Economic Activity: Insights from Machine Learning
Kiley, Michael T. (2020-11-16)
Machine learning (ML) techniques are used to construct a financial conditions index (FCI). The components of the ML-FCI are selected based on their ability to predict the unemployment rate one-year ahead. Three lessons for macroeconomics and variable selection/dimension reduction with large datasets emerge. First, variable transformations can drive results, emphasizing the need for transparency in selection of transformations and robustness to a range of reasonable choices. Second, there is strong evidence of nonlinearity in the relationship between financial variables and economic ...
Finance and Economics Discussion Series , Paper 2020-095

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
The Inflationary Effects of Sectoral Reallocation
Ferrante, Francesco; Graves, Sebastian; Iacoviello, Matteo (2023-02-14)
The COVID-19 pandemic has led to an unprecedented shift of consumption from services to goods. We study this demand reallocation in a multi-sector model featuring sticky prices, input-output linkages, and labor reallocation costs. Reallocation costs hamper the increase in the supply of goods, causing inflationary pressures. These pressures are amplified by the fact that goods prices are more flexible than services prices. We estimate the model allowing for demand reallocation, sectoral productivity, and aggregate labor supply shocks. The demand reallocation shock explains a large portion of ...
International Finance Discussion Papers , Paper 1369

Working Paper
Understanding the New Normal : The Role of Demographics
López-Salido, J. David; Johannsen, Benjamin K.; Gagnon, Etienne (2016-09-28)
Since the onset of the Great Recession, the U.S. economy has experienced low real GDP growth and low real interest rates, including for long maturities. We show that these developments were largely predictable by calibrating an overlapping-generation model with a rich demographic structure to observed and projected changes in U.S. population, family composition, life expectancy, and labor market activity. The model accounts for a 1?percentage point decline in both real GDP growth and the equilibrium real interest rate since 1980?essentially all of the permanent declines in those variables ...
Finance and Economics Discussion Series , Paper 2016-080

Working Paper
Forecasting Core Inflation and Its Goods, Housing, and Supercore Components
Clark, Todd E.; Gordon, Matthew V.; Zaman, Saeed (2023-12-20)
This paper examines the forecasting efficacy and implications of the recently popular breakdown of core inflation into three components: goods excluding food and energy, services excluding energy and housing, and housing. A comprehensive historical evaluation of the accuracy of point and density forecasts from a range of models and approaches shows that a BVAR with stochastic volatility in aggregate core inflation, its three components, and wage growth is an effective tool for forecasting inflation's components as well as aggregate core inflation. Looking ahead, the model's baseline ...
Working Papers , Paper 23-34

Working Paper
Specification Choices in Quantile Regression for Empirical Macroeconomics
Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano (2022-08-31)
Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks to macroeconomic indicators. In this paper we examine various choices in the specification of quantile regressions for macro applications, for example, choices related to how and to what extent to include shrinkage, and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, using for evaluation both quantile scores and quantile-weighted continuous ranked probability scores at a range of quantiles spanning from the left to ...
Working Papers , Paper 22-25

Working Paper
The FOMC versus the Staff: Do Policymakers Add Value in Their Tales?
Filippou, Ilias; Mitchell, James; Nguyen, My T. (2023-08-30)
Using close to 40 years of textual data from FOMC transcripts and the Federal Reserve staff's Greenbook/Tealbook, we extend Romer and Romer (2008) to test if the FOMC adds information relative to its staff forecasts not via its own quantitative forecasts but via its words. We use methods from natural language processing to extract from both types of document text-based forecasts that capture attentiveness to and sentiment about the macroeconomy. We test whether these text-based forecasts provide value-added in explaining the distribution of outcomes for GDP growth, the unemployment rate, and ...
Working Papers , Paper 23-20

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
Significance Bands for Local Projections
Inoue, Atsushi; Jordà, Òscar; Kuersteiner, Guido M. (2023-05-31)
An impulse response function describes the dynamic evolution of an outcome variable following a stimulus or treatment. A common hypothesis of interest is whether the treatment affects the outcome. We show that this hypothesis is best assessed using significance bands rather than relying on commonly displayed confidence bands. Under the null hypothesis, we show that significance bands are trivial to construct with standard statistical software using the LM principle, and should be reported as a matter of routine when displaying impulse responses graphically.
Working Paper Series , Paper 2023-15

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