Search Results
Working Paper
Microstructure Invariance in U.S. Stock Market Trades
This paper studies invariance relationships in tick-by-tick transaction data in the U.S. stock market. Over the 1993?2001 period, the estimated monthly regression coefficients of the log of trade arrival rate on the log of trading activity have an almost constant value of 0.666, strikingly close to the value of 2/3 predicted by the invariance hypothesis. Over the 2001?14 period, the estimated coefficients rise, and their average value is equal to 0.79, suggesting that the reduction in tick size in 2001 and the subsequent increase in algorithmic trading resulted in a more intense order ...
Working Paper
Constructing Applicants from Loan-Level Data: A Case Study of Mortgage Applications
We develop a clustering-based algorithm to detect loan applicants who submit multiple applications (“cross-applicants”) in a loan-level dataset without personal identifiers. A key innovation of our approach is a novel evaluation method that does not require labeled training data, allowing us to optimize the tuning parameters of our machine learning algorithm. By applying this methodology to Home Mortgage Disclosure Act (HMDA) data, we create a unique dataset that consolidates mortgage applications to the individual applicant level across the United States. Our preferred specification ...
Working Paper
Making Friends Meet: Network Formation with Introductions
This paper proposes a parsimonious model of network formation with introductions in the presence of intermediation rents. Introductions allow two nodes to form a new connection on favorable terms with the help of a common neighbor. The decision to form links via introductions is subject to a trade-off between the gains from having a direct connection at lower cost and the potential losses for the introducer from lower intermediation rents. When nodes take advantage of introductions, stable networks tend to exhibit a minimum amount of clustering. At the same time, intermediary nodes have ...
Report
Latent Heterogeneity in the Marginal Propensity to Consume
We estimate the unconditional distribution of the marginal propensity to consume (MPC) using clustering regression and the 2008 stimulus payments. Since we do not measure heterogeneity as the variation of MPCs with observables, we can recover the full distribution of MPCs. Households spent at least one quarter of the rebate, and individual households used rebates for different goods. While many observables are individually correlated with our estimated MPCs, these relationships disappear when tested jointly, except for nonsalary income and the average propensity to consume. Household ...
Working Paper
Making Friends Meet: Network Formation with Introductions
High levels of clustering—the tendency for two nodes in a network to share a neighbor—are ubiquitous in economic and social networks across different applications. In addition, many real-world networks show high payoffs for nodes that connect otherwise separate network regions, representing rewards for filling “structural holes” in the sense of Burt (1992) and keeping distances in networks short. This paper proposes a parsimonious model of network formation with introductions and intermediation rents that can explain both these features. Introductions make it cheaper to create ...
Working Paper
Making Friends Meet: Network Formation with Introductions
This paper proposes a parsimonious model of network formation with introductions in the presence of intermediation rents. Introductions allow two nodes to form a new connection on favorable terms with the help of a common neighbor. The decision to form links via introductions is subject to a trade-off between the gains from having a direct connection at lower cost and the potential losses for the introducer from lower intermediation rents. When nodes take advantage of introductions, stable networks tend to exhibit a minimum of clustering. At the same time, intermediary nodes have incentives ...
Report
Approximating Grouped Fixed Effects Estimation via Fuzzy Clustering Regression
We propose a new, computationally-efficient way to approximate the “grouped fixed-effects” (GFE) estimator of Bonhomme and Manresa (2015), which estimates grouped patterns of unobserved heterogeneity. To do so, we generalize the fuzzy C-means objective to regression settings. As the regularization parameter m approaches 1, the fuzzy clustering objective converges to the GFE objective; moreover, we recast this objective as a standard Generalized Method of Moments problem. We replicate the empirical results of Bonhomme and Manresa (2015) and show that our estimator delivers almost identical ...
Report
Clustering in Natural Disaster Damages
Empirical research in climate economics often relies on panel regressions of different outcomes on disaster damages. Interpreting these regressions requires an assumption that error terms are uncorrelated across counties and time, which climate science research suggests is unlikely to hold. We introduce a methodology to identify spatial and temporal clusters in natural disaster damages datasets, and show that accounting for clustering affects observed economic effects of disasters. Specifically, counties tend to experience 0.45% more disaster damage for every 1% increase in damage across ...
Working Paper
Global Inflation, Regional Factors
This paper shows that global inflation dynamics have a sizable regional component. Using a balanced panel of 61 countries that starts in 1970, we document that while the global factor, defined as the dominant principal component, explains a large portion of inflation variation in advanced economies, a model with only one principal component is less successful for developing countries. By contrast, a hierarchical dynamic factor model, which includes a global (unconstrained) factor and regional (restricted) factors, performs substantially better for emerging market and developing economies. The ...
Discussion Paper
What Is Natural Disaster Clustering—and Why Does It Matter for the Economy?
Understanding the economic and financial consequences of natural disasters is a major concern for researchers and policymakers. The way in which overlapping natural disaster systems interact, as exemplified by the recent fires in Los Angeles being exacerbated by strong winds, is a major area of study in environmental science but has received comparatively little attention in the economics literature. Examining these potential interactions would likely be important for financial institutions, since such assessments would, in many instances, increase the estimated financial impact of a given ...