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Keywords:factor models OR Factor models OR Factor Models 

Working Paper
Term Structure Modeling with Supply Factors and the Federal Reserve's Large Scale Asset Purchase Programs

This paper estimates an arbitrage-free term structure model with both observable yield factors and Treasury and Agency MBS supply factors, and uses it to evaluate the term premium effects of the Federal Reserve's large-scale asset purchase programs. Our estimates show that the first and the second large-scale asset purchase programs and the maturity extension program jointly reduced the 10-year Treasury yield by about 100 basis points.
Finance and Economics Discussion Series , Paper 2014-07

Report
Deconstructing the yield curve

We introduce a novel nonparametric bootstrap for the yield curve which is agnostic to the true factor structure of interest rates. We deconstruct the yield curve into primitive objects, with weak cross-sectional and time-series dependence, that serve as building blocks for resampling the data. We analyze the properties of the bootstrap for mimicking salient features of the data and conducting valid inference. We demonstrate the benefits of our general method by revisiting the predictability of bond returns based on slow-moving fundamentals. We find that trend inflation, but not the ...
Staff Reports , Paper 884

Report
A Measure of Trend Wage Inflation

We extend time-series models that have so far been used to study price inflation (Stock and Watson [2016a]) and apply them to a micro-level dataset containing worker-level information on hourly wages. We construct a measure of aggregate nominal wage growth that (i) filters out noise and very transitory movements, (ii) quantifies the importance of idiosyncratic factors for aggregate wage dynamics, and (iii) strongly co-moves with labor market tightness, unlike existing indicators of wage inflation. We show that our measure is a reliable real-time indicator of wage pressures and a good ...
Staff Reports , Paper 1067

Working Paper
Equity Financing Risk

A risk factor linked to aggregate equity issuance conditions explains the empirical performance of investment factors based on the asset growth anomaly of Cooper, Gulen, and Schill (2008). This new risk factor, dubbed equity financing risk (EFR) factor, subsumes investment factors in leading linear factor models. Most importantly, when substituted for investment factors, the EFR factor improves the overall pricing performance of linear factor models, delivering a significant reduction in absolute pricing errors and their associated t-statistics for several anomalies, including the ones ...
Finance and Economics Discussion Series , Paper 2020-037

Working Paper
Forecasting with Sufficient Dimension Reductions

Factor models have been successfully employed in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. When the objective is to forecast a target variable y with a large set of predictors x, the construction of the summary of the xs should be driven by how informative on y it is. Most existing methods first reduce the predictors and then forecast y in independent phases of the modeling process. In this paper we present an alternative and potentially more attractive alternative: summarizing x as it relates to y, so ...
Finance and Economics Discussion Series , Paper 2015-74

Working Paper
A Unified Framework for Dimension Reduction in Forecasting

Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two separate and independent phases. We introduce a potentially more attractive alternative, Sufficient Dimension Reduction (SDR), that summarizes x as it relates to y, so that all the information in the conditional distribution of y|x is preserved. We study the relationship between SDR and popular estimation ...
Finance and Economics Discussion Series , Paper 2017-004

Working Paper
FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially-disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments ...
Working Papers , Paper 2020-031

Working Paper
Tests of Equal Accuracy for Nested Models with Estimated Factors

In this paper we develop asymptotics for tests of equal predictive ability between nested models when factor-augmented regression models are used to forecast. We provide conditions under which the estimation of the factors does not affect the asymptotic distributions developed in Clark and McCracken (2001) and McCracken (2007). This enables researchers to use the existing tabulated critical values when conducting inference. As an intermediate result, we derive the asymptotic properties of the principal components estimator over recursive windows. We provide simulation evidence on the finite ...
Working Papers , Paper 2015-25

Working Paper
FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially-disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments ...
Working Papers , Paper 2020-031

Discussion Paper
How Easy Is It to Forecast Commodity Prices?

Over the last decade, unprecedented spikes and drops in commodity prices have been a recurrent source of concern to both policymakers and the general public. Given all the recent attention, have economists and analysts made any progress in their ability to predict movements in commodity prices? In this post, we find there is no easy answer. We consider different strategies to forecast near-term commodity price inflation, but find that no particular approach is systematically more accurate and robust. Additionally, the results warn against interpreting current forecasts of commodity prices ...
Liberty Street Economics , Paper 20110627

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