The Bias of the RSR Estimator and the Accuracy of Some Alternatives


Book Description

This paper analyzes the implications of cross-sectional heteroskedasticity in repeat sales regression (RSR). RSR estimators are essentially geometric averages of individual asset returns because of the logarithmic transformation of price relatives. We show that the cross sectional variance of asset returns affects the magnitude of bias in the average return estimate for that period, while reducing the bias for the surrounding periods. It is not easy to use an approximation method to correct the bias problem. We suggest a maximum-likelihood alternative to the RSR that directly estimates index returns that are analogous to the RSR estimators but are arithmetic averages of individual returns. Simulations show that these estimators are robust to time-varying cross-sectional variance and may be more accurate than RSR and some alternative methods of RSR.




The Worth of Art


Book Description

Silver Medal Winner, 2024 Axiom Business Book Award, Personal Finance / Retirement Planning / Investing The market for art can be as eye-catching as artworks themselves. Works by artists from da Vinci and Rembrandt to Picasso and Modigliani have sold for hundreds of millions of dollars. The world’s ultrawealthy increasingly treat art as part of their portfolios. Since artworks are often valuable assets, how should financial professionals analyze them? Arturo Cifuentes and Ventura Charlin provide an expert guide to the methods, risks, and rewards of investing in art. They detail how to apply the financial and statistical tools and techniques used to evaluate more traditional investments such as stocks, bonds, and real estate to art markets. The Worth of Art: Financial Tools for the Art Markets shows readers how to use empirical evidence to answer questions such as: How do the returns on Basquiat compare to the S&P 500? Are Monet’s portraits as valuable as his landscapes? Do red paintings fetch higher prices than blue ones, and does the color palette matter equally to the sales of abstract Rothkos and figurative Hockneys? How much should be loaned to a borrower who is pledging one of Joan Mitchell’s late abstract paintings as collateral? Would the risk-return profile of a conventional portfolio benefit from exposure to Warhol? Rigorous and readable, this book also demonstrates how quantitative analysis can deepen aesthetic appreciation of art.




Simple and Bias-corrected Matching Estimators for Average Treatment Effects


Book Description

Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. In this article, we develop a new framework to analyze the properties of matching estimators and establish a number of new results. First, we show that matching estimators include a conditional bias term which may not vanish at a rate faster than root-N when more than one continuous variable is used for matching. As a result, matching estimators may not be root-N-consistent. Second, we show that even after removing the conditional bias, matching estimators with a fixed number of matches do not reach the semiparametric efficiency bound for average treatment effects, although the efficiency loss may be small. Third, we propose a bias-correction that removes the conditional bias asymptotically, making matching estimators root-N-consistent. Fourth, we provide a new estimator for the conditional variance that does not require consistent nonparametric estimation of unknown functions. We apply the bias-corrected matching estimators to the study of the effects of a labor market program previously analyzed by Lalonde (1986). We also carry out a small simulation study based on Lalonde's example where a simple implementation of the biascorrected matching estimator performs well compared to both simple matching estimators and to regression estimators in terms of bias and root-mean-squared-error. Software for implementing the proposed estimators in STATA and Matlab is available from the authors on the web.




Art of the Deal


Book Description

Art today is defined by its relationship to money as never before. Prices of living artists' works have been driven to unprecedented heights, conventional boundaries within the art world have collapsed, and artists now think ever more strategically about how to advance their careers. Artists no longer simply make art, but package, sell, and brand it. Noah Horowitz exposes the inner workings of the contemporary art market, explaining how this unique economy came to be, how it works, and where it's headed. He takes a unique look at the globalization of the art world and the changing face of the business, offering the clearest analysis yet of how investors speculate in the market and how emerging art forms such as video and installation have been drawn into the commercial sphere. By carefully examining these developments against the backdrop of the deflation of the contemporary art bubble in 2008, "Art of the Deal" is a must-read book that demystifies collecting and investing in today's art market.




Handbook on Residential Property Price Indices


Book Description

This Handbook provides, for the first time, comprehensive guidelines for the compilation of Residential Property Price Indexes and explains in depth the methods and best practices used to calculate an RPPI.




Identification and Estimation of Discrete Games of Complete Information


Book Description

We discuss the identification and estimation of discrete games of complete information. Following Bresnahan and Reiss (1990, 1991), a discrete game is a generalization of a standard discrete choice model where utility depends on the actions of other players. Using recent algorithms to compute all of the Nash equilibria to a game, we propose simulation-based estimators for static, discrete games. With appropriate exclusion restrictions about how covariates enter into payoffs and influence equilibrium selection, the model is identified with only weak parametric assumptions. Monte Carlo evidence demonstrates that the estimator can perform well in moderately-sized samples. As an application, we study the strategic decision of firms in spatially-separated markets to establish a presence on the Internet.




Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics


Book Description

We study the identification and estimation of preferences in hedonic discrete choice models of demand for differentiated products. In the hedonic discrete choice model, products are represented as a finite dimensional bundle of characteristics, and consumers maximize utility subject to a budget constraint. Our hedonic model also incorporates product characteristics that are observed by consumers but not by the economist. We demonstrate that, unlike the case where all product characteristics are observed, it is not in general possible to uniquely recover consumer preferences from data on a consumer's choices. However, we provide several sets of assumptions under which preferences can be recovered uniquely, that we think may be satisfied in many applications. Our identification and estimation strategy is a two stage approach in the spirit of Rosen (1974). In the first stage, we show under some weak conditions that price data can be used to nonparametrically recover the unobserved product characteristics and the hedonic pricing function. In the second stage, we show under some weak conditions that if the product space is continuous and the functional form of utility is known, then there exists an inversion between a consumer's choices and her preference parameters. If the product space is discrete, we propose a Gibbs sampling algorithm to simulate the population distribution of consumers' taste coefficients.




Estimating Hedonic Models


Book Description

In this paper we consider the conditions under which instrumental variables methods are required in estimating a hedonic price function and its accompanying demand and supply relations. We assume simple functional forms that permit an explicit solution for the equilibrium hedonic price function. The principles are the same for models in which no analytic solution exists, but having the solutions makes the issues far more transparent. The need for instrumental variables estimation is directly analogous for the classical demand and supply model with undifferentiated products and for the hedonic model with differentiated products. In estimating individual demand and supply functions, instrumental variables estimation is required if the consumer and firm unobservables, which give rise to the error terms in the demand and supply functions, are correlated across consumers/firms within a community. In estimating inverse demand/supply functions, which are referred to as bid/offer functions in the hedonic model, instrumental variables estimation is required even if the unobservables are not correlated across agents within a community. If the unobservables are not correlated across agents within a community, then community binaries or the means of observable consumer and firm characteristics can be used as instruments. If the unobservables are correlated then only the latter can be used. The error term in the hedonic price function is often assumed to be uncorrelated with the chosen attributes. This assumption may be reasonable if consumers have quasilinear preferences. If not, then the error term in the price function may affect the utility-maximizing amounts of the attributes. The feasible instruments again depend upon whether the error term is correlated for agents within a community. If not, then community binaries or observed individual characteristics may be used as instruments. If so, then the community binaries are correlated with the error terms and cannot serve as instruments.




The American Economic Review


Book Description

Includes papers and proceedings of the annual meeting of the American Economic Association. Covers all areas of economic research.




Identification and Inference in Nonlinear Difference-in-differences Models


Book Description

This paper develops an alternative approach to the widely used Difference-In-Difference (DID) method for evaluating the effects of policy changes. In contrast to the standard approach, we introduce a nonlinear model that permits changes over time in the effect of unobservables (e.g., there may be a time trend in the level of wages as well as the returns to skill in the labor market). Further, our assumptions are independent of the scaling of the outcome. Our approach provides an estimate of the entire counterfactual distribution of outcomes that would have been experienced by the treatment group in the absence of the treatment, and likewise for the untreated group in the presence of the treatment. Thus, it enables the evaluation of policy interventions according to criteria such as a mean-variance tradeoff. We provide conditions under which the model is nonparametrically identified and propose an estimator. We consider extensions to allow for covariates and discrete dependent variables. We also analyze inference, showing that our estimator is root-N consistent and asymptotically normal. Finally, we consider an application.