We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Economics Of Discrimination

Enclosed is a copy of the Board of Governors’ Semiannual Agenda of Regulations for the period August 1, 1980 through February 1, 1981. The Semiannual Agenda provides you with information on those regulatory matters that the Board now has under consideration or anticipates considering over the next six months, and is divided into three parts: (1) regulatory matters that the Board had considered during the previous six months on which final action has been taken; (2) regulatory matters that have been proposed for public comment and that require further Board consideration; and (3) regulatory matters that the Board may consider over the next six months.

Federal Reserve Bank of New Yorkの制定せる財務諸表様式について

Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...

https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.31.2.87

Machine Learning: An Applied Econometric Approach

The collapse of so many AAA-rated structured finance products in 2007-2008 has brought renewed attention to the causes of ratings failures and the conflicts of interest in the Credit Ratings Industry. We provide a model of competition among Credit Ratings Agencies (CRAs) in which there are three possible sources of conflicts: 1) the CRA conflict of interest of understating credit risk to attract more business; 2) the ability of issuers to purchase only the most favorable ratings; and 3) the trusting nature of some investor clienteles who may take ratings at face value. We show that when combined, these give rise to three fundamental equilibrium distortions. First, competition among CRAs can reduce market efficiency, as competition facilitates ratings shopping by issuers. Second, CRAs are more prone to inflate ratings in boom times, when there are more trusting investors, and when the risks of failure which could damage CRA reputation are lower. Third, the industry practice of tranching of structured products distorts market efficiency as its role is to deceive trusting investors. We argue that regulatory intervention requiring: i) upfront payments for rating services (before CRAs propose a rating to the issuer), ii) mandatory disclosure of any rating produced by CRAs, and iii) oversight of ratings methodology can substantially mitigate ratings inflation and promote efficiency.

The Credit Ratings Game

The Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the typical use of a Bartik instrument assumes a pooled exposure research design, where the shares measure differential exposure to common shocks, and identification is based on exogeneity of the shares. Next, we show how the Bartik instrument weights each of the exposure designs. Finally, we discuss how to assess the plausibility of the research design. We illustrate our results through two applications: estimating the elasticity of labor supply, and estimating the elasticity of substitution between immigrants and natives.

/pdf/bartik-instruments-what-when-why-and-how-pna9pchmik.pdf

Bartik Instruments: What, When, Why, and How

There is a large and growing literature on peer effects in economics. In the current article, we focus on a Manski-type linear-in-means model that has proved to be popular in empirical work. We critically examine some aspects of the statistical model that may be restrictive in empirical analyses. Specifically, we focus on three aspects. First, we examine the endogeneity of the network or peer groups. Second, we investigate simultaneously alternative definitions of links and the possibility of peer effects arising through multiple networks. Third, we highlight the representation of the traditional linear-in-means model as an autoregressive model, and contrast it with an alternative moving-average model, where the correlation between unconnected individuals who are indirectly connected is limited. Using data on friendship networks from the Add Health dataset, we illustrate the empirical relevance of these ideas.

Social networks and the identification of peer effects

We study credit ratings on subprime and Alt-A mortgage-backed-securities (MBS) deals issued between 2001 and 2007, the period leading up to the subprime crisis. The fraction of highly rated securities in each deal is decreasing in mortgage credit risk (measured either ex ante or ex post), suggesting that ratings contain useful information for investors. However, we also find evidence of significant time variation in risk-adjusted credit ratings, including a progressive decline in standards around the MBS market peak between the start of 2005 and mid-2007. Conditional on initial ratings, we observe underperformance (high mortgage defaults and losses and large rating downgrades) among deals with observably higher risk mortgages based on a simple ex ante model and deals with a high fraction of opaque low-documentation loans. These findings hold over the entire sample period, not just for deal cohorts most affected by the crisis.

/pdf/mbs-ratings-and-the-mortgage-credit-boom-54bigytqx3.pdf

MBS Ratings and the Mortgage Credit Boom

Recent innovations in statistical technology, including in evaluating creditworthiness, have sparked concerns about impacts on the fairness of outcomes across categories such as race and gender. We build a simple equilibrium model of credit provision in which to evaluate such impacts. We find that as statistical technology changes, the effects on disparity depend on a combination of the changes in the functional form used to evaluate creditworthiness using underlying borrower characteristics and the cross-category distribution of these characteristics. Employing detailed data on US mortgages and applications, we predict default using a number of popular machine learning techniques, and embed these techniques in our equilibrium model to analyze both extensive margin (exclusion) and intensive margin (rates) impacts on disparity. We propose a basic measure of cross-category disparity, and find that the machine learning models perform worse on this measure than logit models, especially on the intensive margin. We discuss the implications of our findings for mortgage policy.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID3280928_code267374.pdf?abstractid=3072038&mirid=1

Paul Goldsmith-Pinkham

Papers

Bartik Instruments: What, When, Why, and How

Social networks and the identification of peer effects

MBS Ratings and the Mortgage Credit Boom

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Social Networks and the Identification of Peer Effects