We use a dynamic factor model to disentangle changes in prices due to economy-wide (common) shocks, from changes in prices due to idiosyncratic shocks. Using 146 disaggregated individual price series from the U.S. PCE price index, we find that most of the fluctuations in core PCE prices observed...

Seasonal adjustment usually relies on statistical models of seasonality that treat seasonal fluctuations as noise corrupting the 'true' data. But seasonality in economic series often stems from economic behavior such as Christmas-time spending. Such economic seasonality invalidates the...

This paper decomposes consumer price inflation into pure inflation, relative price inflation, and idiosyncratic inflation by estimating a dynamic factor model á la Reis and Watson (2010) on a data set of 146 monthly disaggregated prices from 1995 to 2019. We find that pure inflation is the...

This paper argues that changes in the propagation of idiosyncratic shocks along firm networks are important to understanding variations in asset returns. When calibrated to match key features of supplier-customer networks in the United States, an equilibrium model in which investors have...

How much, if at all, should an endowment invest in a firm whose activities run counter to the charitable missions the endowment funds? Endowments typically disregard the objectionable nature of or divest from such firms. However, if firm returns increase with activities the endowment combats,...

In many settings, misaligned incentives and inadequate monitoring lead employees to take self-interested actions contrary to their employer's wishes, giving rise to the classic principal-agent problem. In this paper, I identify and quantify the costs of misaligned incentives in the context of an...

Using novel methodology and proprietary daily store-level sporting goods and apparel brand data, I find that, consistent with long-run adaptation to climate, sales sensitivity to weather declines with historical norms and variability of weather. Short-run adaptation to weather shocks is...

This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample...

We propose a coherent framework using support vector regression (SRV) for generating and ranking a set of high quality models for predicting emerging market sovereign credit spreads. Our framework adapts a global optimization algorithm employing an hv-block cross-validation metric, pertinent for...

This paper develops measures of transaction costs in the absence of transaction timestamps and information about who initiates transactions, which are data limitations that often arise in studies of over-the-counter markets. I propose new measures of the effective spread and study the...