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64
Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach
, 2005
"... We reconsider the identification and estimation of GormanLancasterstyle hedonic models of demand for differentiated products in the spirit of Sherwin Rosen. We generalize Rosen’s first stage to account for product characteristics that are not observed and to allow the hedonic pricing function to ha ..."
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Cited by 46 (1 self)
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We reconsider the identification and estimation of GormanLancasterstyle hedonic models of demand for differentiated products in the spirit of Sherwin Rosen. We generalize Rosen’s first stage to account for product characteristics that are not observed and to allow the hedonic pricing function to have a general nonseparable form. We take an alternative semiparametric approach to Rosen’s second stage in which we assume that the parametric form of utility is known, but we place no restrictions on the aggregate distribution of utility parameters. If there are only a small number of products, we show how to construct bounds on individuals’ utility parameters, as well as other economic objects such as aggregate demand and consumer surplus. We apply our methods to estimating the demand for personal computers.
Structural identification of production functions. Working
, 2006
"... This paper examines some of the recent literature on the empirical identification of production functions. We focus on structural techniques suggested in two recent papers, Olley and Pakes (1996), and Levinsohn and Petrin (2003). While there are some solid and intuitive indentification ideas in thes ..."
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Cited by 44 (0 self)
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This paper examines some of the recent literature on the empirical identification of production functions. We focus on structural techniques suggested in two recent papers, Olley and Pakes (1996), and Levinsohn and Petrin (2003). While there are some solid and intuitive indentification ideas in these papers, we argue that the techniques, particularly those of Levinsohn and Petrin, suffer from collinearity problems which we believe cast doubt on the methodology. We then suggest alternative methodologies which make use of the ideas in these papers, but do not suffer from these collinearity problems. 1
An Extended Class of Instrumental Variables for the Estimation of Causal Effects
 UCSD DEPT. OF ECONOMICS DISCUSSION PAPER
, 1996
"... This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regresso ..."
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Cited by 30 (11 self)
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This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regressors and standard “valid” and “relevant” instrumental variables. We then build on this interpretation to characterize extended instrumental variables (EIV) methods, that is methods that make use of variables that need not be valid instruments in the standard sense, but that are nevertheless instrumental in the recovery of causal effects of interest. After examining special cases of single and double EIV methods, we provide necessary and sufficient conditions for the identification of causal effects by means of EIV and provide consistent and asymptotically normal estimators for the effects of interest.
Nonparametric Identification of Dynamic Models with Unobserved State Variables
, 2008
"... We consider the identification of a Markov process {Wt, X ∗ t} when only {Wt}, a subset of the variables, are observed. In structural dynamic models, Wt includes the sequences of choice variables and observed state variables of an optimizing agent, while X ∗ t denotes the sequence of serially correl ..."
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Cited by 23 (8 self)
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We consider the identification of a Markov process {Wt, X ∗ t} when only {Wt}, a subset of the variables, are observed. In structural dynamic models, Wt includes the sequences of choice variables and observed state variables of an optimizing agent, while X ∗ t denotes the sequence of serially correlated unobserved state variables. The to depend Markov setting allows the distribution of the unobserved state variable X ∗ t on Wt−1 and X ∗ t−1. In the nonstationary case, we show that the Markov transition density fWt,X ∗ t Wt−1,X ∗ is identified from the observation of five periods of data t−1 Wt+1, Wt, Wt−1, Wt−2, Wt−3 under reasonable assumptions. In the stationary case, only four observations Wt+1, Wt, Wt−1, Wt−2 are required. Identification of fWt,X ∗ t Wt−1,X ∗ t−1 is a crucial input in methodologies for estimating Markovian dynamic models based on the “conditionalchoiceprobability (CCP)” approach pioneered by Hotz and Miller.
THE SCIENTIFIC MODEL OF CAUSALITY
, 2005
"... Causality is a very intuitive notion that is difficult to make precise without lapsing into tautology. Two ingredients are central to any definition: (1) a set of possible outcomes (counterfactuals) generated by a function of a set of ‘‘factors’ ’ or ‘‘determinants’ ’ and (2) a manipulation where on ..."
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Cited by 20 (1 self)
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Causality is a very intuitive notion that is difficult to make precise without lapsing into tautology. Two ingredients are central to any definition: (1) a set of possible outcomes (counterfactuals) generated by a function of a set of ‘‘factors’ ’ or ‘‘determinants’ ’ and (2) a manipulation where one (or more) of the ‘‘factors’ ’ or ‘‘determinants’’ is changed. An effect is realized as a change in the argument of a stable function that produces the same change in the outcome for a class of interventions that change the ‘‘factors’ ’ by the same amount. The outcomes are compared at different levels of the factors or generating variables. Holding all factors save one at a constant level, the change in the outcome associated with manipulation of the varied factor is called a causal effect of the manipulated factor. This definition, or some version of it, goes back to Mill (1848) and Marshall (1890). Haavelmo’s (1943) made it more precise within the context of linear equations models. The phrase ‘ceteris paribus’ (everything else held constant) is a mainstay of economic analysis
Identification and Estimation of a Nonparametric Panel Data Model with Unobserved Heterogeneity
, 2009
"... This paper considers a nonparametric panel data model with nonadditive unobserved heterogeneity. As in the standard linear panel data model, two types of unobservables are present in the model: individualspecific effects and idiosyncratic disturbances. The individualspecific effects enter the stru ..."
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Cited by 17 (1 self)
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This paper considers a nonparametric panel data model with nonadditive unobserved heterogeneity. As in the standard linear panel data model, two types of unobservables are present in the model: individualspecific effects and idiosyncratic disturbances. The individualspecific effects enter the structural function nonseparably and are allowed to be correlated with the covariates in an arbitrary manner. The idiosyncratic disturbance term is additively separable from the structural function. Nonparametric identification of all the structural elements of the model is established. No parametric distributional or functional form assumptions are needed for identification. The identification result is constructive and only requires panel data with two time periods. Thus, the model permits nonparametric distributional and counterfactual analysis of heterogeneous marginal effects using short panels. The paper also develops a nonparametric estimation procedure and derives its rate of convergence. As a byproduct the rates of convergence for the problem of conditional deconvolution are obtained. The proposed estimator is easy to compute and does not require numeric optimization. A MonteCarlo study indicates that the estimator performs very well in finite sample properties.
The browser war  econometric analysis of Markov perfect equilibrium in markets with network effects
 TRIAL EXHIBIT: MICROSOFT OEM SALES FY ’98 MIDYEAR REVIEW (GX 421) IN UNITED STATES V. MICROSOFT CORPORATION, CIVIL ACTION NO
, 2004
"... When demands for heterogeneous goods in a concentrated market shift over time due to network or contagion effects, forwardlooking firms consider the strategic impact of investment, pricing, and other conduct. Network effects may be a substantial barrier to entry, giving both entrants and incumbents ..."
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Cited by 15 (2 self)
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When demands for heterogeneous goods in a concentrated market shift over time due to network or contagion effects, forwardlooking firms consider the strategic impact of investment, pricing, and other conduct. Network effects may be a substantial barrier to entry, giving both entrants and incumbents powerful strategic incentives to “tip” the market. A Markov perfect equilibrium model captures this strategic behavior, and permits the comparison of “as is ” market trajectories with “but for ” trajectories under counterfactuals where “bad acts ” by some firms are eliminated. Our analysis is applied to a stylized description of the browser war between Netscape and Microsoft. Appendices give conditions for econometric identification and estimation of a Markov perfect equilibrium model from observations on partial trajectories, and discuss estimation of the impacts of
On The Nonparametric Identification Of Nonlinear Simultaneous Equations Models: Comment On Brown
 Econometrica
, 2006
"... This note revisits the identification theorems of B. Brown (1983) and Roehrig (1988). We describe an error in the proofs of the main identification theorems in these papers, and provide an important counterexample to the theorems on the identification of the reduced form. Specifically, contrary to t ..."
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Cited by 14 (0 self)
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This note revisits the identification theorems of B. Brown (1983) and Roehrig (1988). We describe an error in the proofs of the main identification theorems in these papers, and provide an important counterexample to the theorems on the identification of the reduced form. Specifically, contrary to the theorems, the reduced form of a nonseparable simultaneous equations model is not identified even under the assumptions of those papers. We conclude the note with a conjecture that it may be possible to use classical exclusion restrictions to recover some of the key implications of the theorems. ∗We have had very helpful conversations with Pat Bayer, Don Brown, Yossi Feinberg, Guido Imbens, Yuliy Sannikov, Andy Skrzypacz, and Chris Timmons. Any remaining In this note, we reconsider the nonparametric identification of nonlinear simultaneous equations models, as in B. Brown (1983) and Roehrig (1988). We
Nonparametric Demand Systems, Instrumental Variables and a Heterogeneous Population,”Mannheim
, 2005
"... This paper is concerned with empirically modelling the demand behavior of a population with heterogeneous preferences under a weak conditional independence assumption. More specifically, we characterize the testable implications of negative semidefiniteness and symmetry of the Slutsky matrix across ..."
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Cited by 11 (7 self)
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This paper is concerned with empirically modelling the demand behavior of a population with heterogeneous preferences under a weak conditional independence assumption. More specifically, we characterize the testable implications of negative semidefiniteness and symmetry of the Slutsky matrix across a heterogeneous population without assuming anything on the functional form of individual preferences. In the same spirit, implications of a linear budget set are being considered. Since the conditional independence assumption is the only substantial restriction in this model, we analyze possible alternatives and solutions if this assumption is violated. In particular, we consider in detail the concept of instruments in this framework. Besides being able to integrate econometric concepts, the same framework admits also economic extensions. As an example we consider welfare analysis. Finally, we provide asymptotic distribution theory for the new test statistics that emerge out of this framework, and apply these to Canadian data. 1
Nonparametric estimation of nonadditive hedonic models
, 2002
"... We present methods to estimate marginal utility and marginal product functions that are nonadditive in the unobservable random terms, using observations from a single hedonic equilibrium market. We show that nonadditive marginal utility and nonadditive marginal product functions are capable of gener ..."
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Cited by 10 (3 self)
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We present methods to estimate marginal utility and marginal product functions that are nonadditive in the unobservable random terms, using observations from a single hedonic equilibrium market. We show that nonadditive marginal utility and nonadditive marginal product functions are capable of generating equilibria that exhibit bunching, as well as other types of equilibria. We provide conditions under which these types of utility and production functions are nonparametrically identified, and we propose nonparametric estimators for them. The estimators are shown to be consistent and asymptotically normal.