Search Results
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 ...
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 ...
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 Paper
Identification Through Sparsity in Factor Models
Factor models are generally subject to a rotational indeterminacy, meaning that individual factors are only identified up to a rotation. In the presence of local factors, which only affect a subset of the outcomes, we show that the implied sparsity of the loading matrix can be used to solve this rotational indeterminacy. We further prove that a rotation criterion based on the 1-norm of the loading matrix can be used to achieve identification even under approximate sparsity in the loading matrix. This enables us to consistently estimate individual factors, and to interpret them as structural ...
Working Paper
Factor Models with Local Factors—Determining the Number of Relevant Factors
We extend the theory on factor models by incorporating “local” factors into the model. Local factors affect only an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. We de-rive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. We then introduce a new class of estimators to determine the number of those relevant factors. Un-like existing estimators, our estimators use not only ...
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 ...
Working Paper
Factor Selection and Structural Breaks
We develop a new approach to select risk factors in an asset pricing model that allows the set to change at multiple unknown break dates. Using the six factors displayed in Table 1 since 1963, we document a marked shift towards parsimonious models in the last two decades. Prior to 2005, five or six factors are selected, but just two are selected thereafter. This finding offers a simple implication for the factor zoo literature: ignoring breaks detects additional factors that are no longer relevant. Moreover, all omitted factors are priced by the selected factors in every regime. Finally, ...
Working Paper
A Generalized Factor Model with Local Factors
I extend the theory on factor models by incorporating local factors into the model. Local factors only affect an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. I derive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. I then introduce a new class of estimators to determine the number of those relevant factors. Unlike existing estimators, my estimators use not only the ...
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 ...
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 ...