Showing results 1 to 10 of approximately 13.(refine search)
A review of the experience of fielding the Survey of Consumer Expectations
Remarks at the Barclays Global Inflation Conference, New York City.
On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates
We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even the ex post best forecaster) are outperformed by our regularized mixtures. From the Great Recession onward, the optimal regularization tends to move density forecasts’ probability mass from the centers to the tails, correcting for overconfidence.
Avoiding Nash Inflation : Bayesian and Robust Responses to Model Uncertainty
We examine learning, model misspecification, and robust policy responses to misspecification in a quasi-real-time environment. The laboratory for the analysis is the Sargent (1999) explanation for the origins of inflation in the 1970s and the subsequent disinflation. Three robust policy rules are derived that differ according to the extent that misspecification is taken as a parametric phenomenon. These responses to drifting estimated parameters and apparent misspecification are compared to the certainty-equivalent case studied by Sargent. We find gains from utilizing robust approaches to ...
Is It Time to Reassess the Focal Role of Core PCE Inflation?
In this paper, I review the history of “core” PCE inflation and its rationale: remove volatile items with transitory shocks to better highlight the trend in inflation. Structural changes in the inflation process imply that, on a “reducing volatility” basis, the list of items excluded from the “core” inflation basket (aside from gasoline) is far from optimal. This is true whether one assesses volatility on the basis of a weighted component monthly, or an index monthly, or a 12-month index, or a 5-year index. In addition, I demonstrate other deficiencies of exclusion indexes. ...
Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach
This paper focuses on forecasting quarterly energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information in this large database, we apply a dynamic factor model based on a penalized maximum likelihood approach that allows us to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show ...
Creativity and economic growth: theory, measures, and potentials for morocco
The current era of globalization is dominated by the rise of investments in intangible capital rather than tangible capital ? the ascendance of creativity over plant and equipment. This brief paper is motivated by the possibility that emerging market economies such as Morocco might take greater advantage of new tools and policies designed for this new era. To begin, I discuss the transformation of the global economy and the consequences of the transformed global economy for economic thinking and measurement. I refer to both old and new literature on the measurement of intangible investment ...
FRED-QD: A Quarterly Database for Macroeconomic Research
In this paper we present and describe a large quarterly frequency, macroeconomic database. The data provided are closely modeled to that used in Stock and Watson (2012a). As in our previous work on FRED-MD, our goal is simply to provide a publicly available source of macroeconomic “big data” that is updated in real time using the FRED database. We show that factors extracted from this data set exhibit similar behavior to those extracted from the original Stock and Watson data set. The dominant factors are shown to be insensitive to outliers, but outliers do affect the relative influence ...
Partially Disaggregated Household-level Debt Service Ratios: Construction and Validation
Currently published data series on the United States household debt service ratio are constructed from aggregate household debt data provided by lenders and estimates of the average interest rate and loan terms of a range of credit products. The approach used to calculate those debt service ratios could be prone to missing changes in loan terms. Better measurement of this important indicator of financial health can help policymakers anticipate and react to crises in household finance. We develop and estimate debt service ratio measures based on individual-level debt payments data obtained ...
Seasonal adjustment of state and metro ces jobs data
Hybrid time series data often require special care in estimating seasonal factors. Series such as the state and metro area Current Employment Statistics produced by the Bureau of Labor Statistics (BLS) are composed of two different source series that often have two different seasonal patterns. In this paper we address the process to test for differing seasonal patterns within the hybrid series. We also discuss how to apply differing seasonal factors to the separate parts of the hybrid series. Currently the BLS simply juxtaposes the two different sets of seasonal factors at the transition ...
Modeling to Inform Economy-Wide Pandemic Policy: Bringing Epidemiologists and Economists Together
Facing unprecedented uncertainty and drastic trade-offs between public health and other forms of human well-being, policymakers during the Covid-19 pandemic have sought the guidance of epidemiologists and economists. Unfortunately, while both groups of scientists use many of the same basic mathematical tools, the models they develop to inform policy tend to rely on different sets of assumptions and, thus, often lead to different policy conclusions. This divergence in policy recommendations can lead to uncertainty and confusion, opening the door to disinformation, distrust of institutions, and ...