The European Union Emissions Trading System is set to substantially increase the effective carbon price faced by airlines. To quantify the impact of this carbon regulation on the European airline industry, we estimate a two-stage model of airline competition with endogenous route entry, flight frequencies, and pricing using European data on market shares and prices. Counterfactual simulations reveal that the impacts of carbon pricing are highly asymmetric across carrier types and market segments. Consumer surplus declines by up to 25

Key economic variables are often published with a significant delay of over a month. The nowcasting literature has arisen to provide fast, reliable estimates of delayed economic indicators and is closely related to filtering methods in signal processing. The path signature is a mathematical object which captures geometric properties of sequential data; it naturally handles missing data from mixed frequency and/or irregular sampling -- issues often encountered when merging multiple data sources -- by embedding the observed data in continuous time. Calculating path signatures and using them as features in models has achieved state-of-the-art results in fields such as finance, medicine, and cyber security. We look at the nowcasting problem by applying regression on signatures, a simple linear model on these nonlinear objects that we show subsumes the popular Kalman filter. We quantify the performance via a simulation exercise, and through application to nowcasting US GDP growth, where we see a lower error than a dynamic factor model based on the New York Fed staff nowcasting model. Finally we demonstrate the flexibility of this method by applying regression on signatures to nowcast weekly fuel prices using daily data. Regression on signatures is an easy-to-apply approach that allows great flexibility for data with complex sampling patterns.

This paper is about estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters, some of which may be zero. The paper gives conditions under which, with probability approaching 1 as the sample size increases, penalized maximum likelihood (PML) estimation with the adaptive LASSO (AL) penalty distinguishes correctly between zero and non-zero parameters. The paper also gives conditions under which PML reduces the asymptotic mean-square estimation error of any continuously differentiable function of the model’s parameters. The paper describes a method for computing PML estimates and presents the results of Monte Carlo experiments that illustrate their performance. It also presents the results of PML estimation of a random coefficients logit model of choice among brands of butter and margarine in the British groceries market.

Using the English Housing Survey, we estimate a supply side selection model of the allocation of properties to the owner-occupied and rental sectors.We find that location, structure and unobserved quality are important for understanding housing prices, rents and selection.Structural characteristics and unobserved quality are important for selection.Location is not.Accounting for selection is important for estimates of rent-to-price ratios and can explain some puzzling correlations between rent-to-price ratios and homeownership rates.We interpret this as strong evidence in favor of contracting frictions in the rental market likely related to housing maintenance.

We propose a demand model where consumers simultaneously choose a few different goods from a large menu of available goods, and choose how much to consume of each good. The model nests multinomial discrete choice and continuous demand systems as special cases. Goods can be substitutes or complements. Random coefficients are employed to capture the wide variation in the composition of consumption baskets. Non-negativity constraints produce corners that account for different consumers purchasing different numbers of types of goods. We show semiparametric identification of the model. We apply the model to the demand for fruit in the United Kingdom. We estimate the model’s parameters using UK scanner data for 2008 from the Kantar World Panel. Using our parameter estimates, we estimate a matrix of demand elasticities for 27 categories of fruit and analyze a range of tax and policy change scenarios.

Random utility models are widely used to study consumer choice. The vast majority of applications make strong assumptions about the marginal utility of income, which restricts income effects, demand curvature and pass-through. We show that flexibly modeling income effects can be important, particularly if one is interested in the distributional effects of a policy change, even in a market in which, a priori, the expectation is that income effects will play a limited role. We allow for much more flexible forms of income effects than is common and we illustrate the implications by simulating the introduction of an excise tax.

This paper discusses nonparametric estimation of the distribution of random coefficients in a structural model that is nonlinear in the random coefficients. We establish that the problem of recovering the probability density function ( pdf ) of random parameters falls into the class of convexly-constrained inverse problems. The framework offers an estimation method that separates computational solution of the structural model from estimation. We first discuss nonparametric identification. Then, we propose two alternative estimation procedures to estimate the density and derive their asymptotic properties. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several structural models are provided which illustrate the performance of our estimation procedure.

We present evidence from 260,000 online auctions of second‐hand cars to identify the impact of public reserve prices on auction outcomes. We exploit multiple discontinuities in the relationship between reserve prices and vehicle characteristics to present causal regression‐discontinuity estimates of reserve price impacts. We find an increase in reserve price decreases the number of bidders, increases the likelihood the object remains unsold, and increases expected revenue conditional on sale. We then combine these estimates to calibrate the reserve price effect on the auctioneer's ex ante expected revenue. This reveals the auctioneer's reserve price policy to be locally optimal. (JEL D44, L11, L62)

We use a large repeated cross-section of houses to estimate a selection model of the supply of owner-occupied and rental housing. We find that physical characteristics and unobserved heterogeneity and not location are important for selection. We interpret this as strong evidence in favor of contracting frictions in the rental market relating to maintenance and modification of a dwellings physical characteristics.

Abstract We use a large repeated cross-section of properties to estimate a selection model of the supply of owner-occupied and rental housing. We find that physical characteristics and unobserved quality and not location are important for selection. We interpret this as strong evidence in favor of contracting frictions in the rental market relating to maintenance and modification of physical housing characteristics. Accounting for selection is important for estimates of rent-to-price ratios and can explain some puzzling correlations between rent-to-price ratios and homeownership rates.

In this paper we generalize the so-called CCP estimator of Hotz and Miller (1993) to a broader class of dynamic discrete choice (DDC) models that allow period payoff functions to be non-separable in observable and unobservable (to the econometrician) variables. Such nonseparabilities are common in applied microeconomic environments and our generalized CCP estimator allows for computationally simple estimation in this class of DDC models. We first establish invertibility results between conditional choice probabilities (CCPs) and value functions and use this to derive a policy iteration mapping in our more general framework. This is used to develop a pseudo-maximum likelihood estimator of model parameters that side-step the need for solving the model. To make the inversion operational, we use Mathematical Programming with Equilibrium Constraints (MPEC), following Judd and Su (2012) and demonstrate its applicability in a series of Monte Carlo experiments that replicate the model used in Keane and Wolpin (1997). ∗We thank Victor Aguirregabiria, Andrew Chesher, Xavier d’Haultfoeuille, Jim Heckman, Bo Honoré, Hiro Kasahara, John Kennan, Pedro Mira, Jean-MarcRobin, Petra Todd and Ken Wolpin together with participants at the Econometric Society Summer Meetings 2014 in Toulouse, and various seminars for helpful comments and suggestions. This research was supported by the Economic and Social Research Council through the ESRC Centre for Microdata Methods and Practice grant RES-589-28-0001. Kristensen acknowledges support from the Danish National Research Foundation (through a grant to CREATES) and the European Research Council (grant no. ERC-2012-StG 312474). de Paula acknowledges support from the European Research Council (grant no. ERC-2013-StG 338187). †University College London, London, UK, CeMMAP, London, UK, and IFS, London, UK. E-mail: d.kristensen@ucl.ac.uk ‡University College London, London, UK, CeMMAP, London, UK, and IFS, London, UK. E-mail: l.nesheim@ucl.ac.uk §University College London, London, UK, São Paulo School of Economics, São Paulo, Brazil, CeMMAP, London, UK, and IFS, London, UK. E-mail: apaula@ucl.ac.uk

The book aims to further our understanding of how economic reasoning and legal expertise complement each other in defining the fundamental issues and principles in competition policy. In specially commissioned chapters the book provides a scholarly review of economic theory, empirical evidence and standards of legal evaluation with respect to monopolization of markets, exploitation of market power and mergers, among other issues.

Existing hedonic methods cannot be easily adapted to estimate willingness to pay for product characteristics when willingness to pay depends on a very large basket of goods. We show how to marry these methods with revealed preference arguments to estimate bounds on willingness to pay using data on purchases of seemingly impossibly high dimensional baskets of goods. This allows us to use observed purchase prices and quantities on a large basket of products to learn about individual household's willingness to pay for characteristics, while maintaining a high degree of flexibility and also avoiding the biases that arise from inappropriate aggregation.We illustrate the approach using scanner data on food purchases to estimate bounds on willingness to pay for the organic characteristic. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved.

We model individual demand for housing over the life cycle, and show the aggregate implications of this behaviour. Individuals delay purchasing their first home when incomes are low or uncertain. Higher house prices lead households to downsize, rather than to stop being owners. Fixed costs (property transactions taxes) have important impacts on welfare (a wealth effect) and house purchase decisions (substitution effect). In aggregate, positive house price shocks lead to consumption booms among the old but falls in consumption for the young, and reduced housing demand; positive income shocks lead to consumption booms among the young and increased housing demand.

Abstract. This paper revisits identification and estimation in nonseparable triangular models. Particular emphasis is put on the role of nonlinearity of the relationship between the endogenous regressor and the instrumental variable in overidentifying local features of structural objects of interest. All identification results rely on an implicit control function in order to deal with endogeneity. A framework for structural estimation via implicit conditioning is introduced, and a localization scheme is described as a generic procedure for the construction of estimators allowing for multivariate sources of stochastic variations.

In structural economic models, individuals are usually characterized as solving a decision problem that is governed by a …nite set of parameters. This paper discusses the nonparametric estimation of the density of these parameters if they are allowed to vary continuously across the population. We establish that the problem of recovering the density of random parameters falls into the class of non-linear inverse problem. This framework helps us to answer the question whether there exist densities that satisfy this relationship. It also allows us to characterize the identi…ed set of such densities, to obtain conditions for point identi…cation, and to establish that point identi…cation is weak. Given this insight, we propose a consistent nonparametric estimator, and derive its asymptotic distribution. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several structural models are provided which illustrate the performance of our estimation procedure.