# Nonparametric Errors in Variables Models with Measurement Errors on both sides of the Equation

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1 Nonparametric Errors in Variables Models with Measurement Errors on both sides of the Equation Michele De Nadai University of Padova Arthur Lewbel Boston College First version July 2011, revised July 2013 Abstract Measurement errors are often correlated, as in surveys where respondent s biases or tendencies to err affect multiple reported variables. We etend Schennach (2007) to identify moments of the conditional distribution of a true Y given a true X when both are measured with error, the measurement errors in Y and X are correlated, and the true unknown model of Y given X has nonseparable model errors. We also provide a nonparametric sieve estimator of the model, and apply it to nonparametric Engel curve estimation. In our application measurement errors on the ependitures of a good Y are by construction correlated with measurement errors in total ependitures X. This feature of most consumption data sets has been ignored in almost all previous demand applications. We find accounting for this feature casts doubt on Hildenbrand s (1994) increasing dispersion assumption. Keywords: Engel curve; errors-in-variables model; Fourier transform; generalized function; sieve estimation. This paper benefited from helpful discussions with Erich Battistin. Address for correspondence: Department of Statistics, Via Cesare Battisti 241, Padova - Italy and Department of Economics, 140 Commonwealth Avenue, Chestnut Hill, MA - USA. and 1

2 1 Introduction We consider identification and estimation of conditional moments of Y given X in nonparametric regression models where both the dependent variable Y and a regressor X are mismeasured, and the measurement errors in Y and X are correlated. An eample application where this occurs is consumer demand estimation, where Y is the quantity or ependitures demanded of some good or service, and X is total consumption ependitures on all goods. In most consumption data sets (e.g., the US Consumer Ependiture Survey or the UK Family Ependiture Survey), total consumption X is constructed as the sum of ependitures on individual goods, so by construction any measurement error in Y will also appear as a component of, and hence be correlated with, the measurement error in X. Similar problems arise in profit, cost, or factor demand equations in production, and in autoregressive or other dynamic models where sources of measurement error are not independent over time. Correlated measurement errors are also likely in survey data where each respondent s biases or tendencies to err affect multiple variables that he or she self reports. Our identification procedure allows us to distinguish measurement errors from other sources of error that are due to unobserved structural or behavioural heterogeneity. This is important in applications because many policies may depend on the distribution of structural unobserved heterogeneity, but not on measurement error. For eample, the effects of an income ta on aggregate demand or savings depend on the distribution of income elasticities in the population. In contrast to our results, most empirical analyses implicitly or eplicitly attribute either none or all of estimated model errors to unobserved heterogeneity. It has long been known that for most goods, empirical estimates of Var(Y X) are increasing in X. For eample, Hildenbrand (1994, figures 3.6 and 3.7) documents this phenomenon for a variety of goods in two different countries, calls it the increasing dispersion assumption, and eploits it as a behavioral feature that helps give rise to the aggregate law of demand. This property is also often used to justify estimating Engel curves in budget share form, to reduce the resultin error heteroskedasticity. However, we find empirically that while this phenomenon clearly holds in estimates of V ar(y X) on raw data, after nonparametrically accounting for joint measurement error in Y and X, the evidence becomes considerably weaker, suggesting that this well documented feature of Engel curve estimates may be at least in part an artifact of measurement errors rather 2

3 than a feature of behavior. Our identification strategy is an etension of Schennach (2007), who provides nonparametric identification of the conditional mean of Y given X (using instruments Q) when X is a classically mismeasured regressor. We etend Schennach(2007) primarily by allowing for a measurement error term in Y, but an additional etension is to identify higher moments of the true Y given the true X instead of just the conditional mean. A further etension allows the measurement error in X to be multiplicative instead of additive. Building on estimators like Newey (2001), Schennach (2007) bases identification on taking Fourier transforms of the conditional means of Y and of XY given instruments. These Fourier transforms have both an ordinary and a singular component, and identification is based just on the ordinary components. Our main insight is that, if the additive measurement errors in X and Y are correlated with each other but otherwise have some of the properties of classical measurement errors, then their presence will only affect the singular component of the Fourier transform, so identification based on the ordinary components can proceed as before. Our further etensions eploit similar properties in different measurement error specifications, and our empirical application makes use of some special features of Engel curves to fully identify higher moments. There is a large literature on the estimation of measurement error models. In addition to Schennach (2007), more recent work on measurement errors in nonparametric regression models includes Delaigle, Fan, and Carroll (2009), Rummel, Augustin, and Küchenhof (2010), Carroll, Chen, and Hu (2010), Meister (2011), and Carroll, Delaigle, and Hall (2011). Recent surveys containing many earlier references include Carroll, Ruppert, Stefanski, and Crainiceanu (2006) and Chen, Hong, and Nekipelov (2011). 1 Eamples of Engel curve estimation with measurement errors include the following. Hausman, Newey, Ichimura, and Powell(1991) and Hausman, Newey, and Powell(1995) provide estimators for polynomial Engel curves with classically mismeasured X, Newey (2001) estimates a nonpolynomial parametric Engel curve with mismeasured X, Blundell, Chen, and Kristensen (2007) estimate a semi-parametric model of Engel curves that allows X to be endogenous and hence mismeasured, and Lewbel (1996) identifies and estimates Engel curves allowing for correlated measurement errors 1 Earlier econometric papers closely related to Schennach (2007), but eploiting repeated measurements, are Hausman, Newey, Ichimura, and Powell (1991), Schennach (2004) and Li (2002). Most of these assume two mismeasures of the true X are available, one of which could have errors correlated with the measurement error Y. 3

4 in X and Y as we do, but does so in the contet of a parametric model of Y given X. 2 The conditional distribution of the true Y given the true X in Engel curves corresponds to the distribution of preference heterogeneity parameters in the population, which can be of particular interest for policy analysis. For eample, consider the effect on demand of introducing a ta cut or ta increase that shifts households total ependiture levels. This will in general affect the entire distribution of demand, not just its mean, both because Engel curves are generally nonlinear and because preferences are heterogeneous. Recovering moments of the distribution of demand is useful because many policy indicators, such as the welfare implication of a ta change, will in turn depend on more features of the distribution of demand than just its mean. The net two sections show identification of the model with standard additive measurement error and of the specification more specifically appropriate for Engel curve data respectively. We then describe our sieve based estimator, and provide a simulation study. After that is an empirical application to estimating food and clothing ependitures in US Consumer Ependiture Survey data, followed by conclusions and an appendi providing proofs. 2 Overview With i indeing observations, suppose that scalar random variables Y i and X i are measured with error, so we only observe Y i and X i where: Y i = Y i +S i, X i = X i +W i, with S i and W i being unobservedmeasurement errors that may be correlated with each other (more general models of measurement errors are considered below). Without loss of generality we can 2 More generally, within econometrics there is a large recent literature on nonparametric identification of models having nonseparable errors (e.g., Chesher 2003, Matzkin 2007, Hoderlein and Mammen 2007, and Imbens and Newey 2009), multiple errors (e.g. random coefficient models like Beran, Feuerverger, and Hall 1996 and generalizations like Hoderlein, Nesheim, and Simoni 2011 and Lewbel and Pendakur 2011) or both (e.g., Matzkin 2003). This paper contributes to that literature by identifying models that have both additive measurement error and structural nonseparable unobserved heterogeneity. 4

5 write Y i = H(X i,u i ), where Yi equals an unknown function H(, ) of a scalar random regressor Xi, and a random scalar or vector of nonseparable unobservables U i, which can be interpreted as regression model errors or unobserved heterogeneity in the population. The etension to inclusion of other (observed) covariates will be straightforward, so we drop them for now. Our primary goal is identification (and later estimation) of the nonparametric regression function E[H(Xi,U i) Xi ], but we more generally consider identification of conditional moments E [ H(Xi,U i) k Xi ] for integers k. Thus our results can be interpreted as separating the impact of unobserved heterogeneity U from the effects of measurement errors on the relationship of Y to X. We do not deal directly with estimation of H and of U, but these could be recovered with some additional assumptions given our results. 3 In the statistics literature, a measurement error W i in X i is called differential if it affects the observed outcome Y i, after conditioning on the true Xi, that is, if Y i Xi,W does not equal Y i X i (see, e.g., Carroll, Ruppert, Stefanski, and Crainiceanu (2006)). In our setup W i is in general differential since W is correlated with the measurement error S i in Y i, while it would be nondifferential if the true Y i were observed. An alternative application of our identification results would be for a model in which Y i is not mismeasured, and the additive error S i instead represents the effect of differential measurement error W i on the true outcome Y i. To ease eposition this case will no longer be considered, and we will focus on the interpretation in which both X i and Y i are mismeasured. To aid identification, assume we observe instruments Q i satisfying X i = m(q i )+V i, (1) where the function m is unknown and defined by m(q i ) = E(X i Q i ), and V i Q i. This independence assumption, which is also made by Schennach (2007), is common in the control 3 If the moment generating function of Yi given Xi eists in an open interval around zero, then E [ ] Yi k Xi is finite for all integers k and our results can be used to identify these moments, which in turn can be used to identify the conditional distribution of Yi given Xi via the moment generating function. Then results such as those in Matzkin (2007) can be applied to identify H(Xi,U i). If the conditional distribution of Yi given Xi is continuous, then one such result defines U i to equal the conditional distribution function of Yi given Xi, and constructs H as the inverse function of this distribution. Alternative identifying assumptions would similarly follow from full knowledge of the conditional distribution function. 5

6 function literature when dealing with nonlinear models and will be maintained throughout the paper. Assume for now that the measurement errors have the classical properties that they are mean zero with S i,w i Y i,x i,q i. This can be equivalently stated as S i,w i U i,v i,q i. Our formal results will substantially rela these independence assumptions (specifically, they are replaced by Assumption 1 below). This independence is just made here now to ease eposition. The two main complications in our set up compared to a classical measurement error problem are that the true regression model H is an unknown function containing nonseparable model errors U, and that measurement errors in Y i and X i may be correlated. Assuming S i is identically zero so Y i Y i, Schennach (2007) shows identification of the conditional mean function E(Y i X i ), based on features of the Fourier transforms of E[Y i m(q i )] and of E[Y i X i m(q i )]. These transforms are in general not proper functions, but generalized functions, being the Fourier transforms of not-absolutely integrable functions. We follow Schennach (2007) in assuming that the generalized functions that arise in our contet may be decomposed into the sum of an ordinary component that is a well behaved function, and a purely singular component, which may be seen as a linear combination of generalized derivatives of the Dirac delta function (see Lighthill 1962 and the supplementary material in Schennach 2007). The conditional mean E(Yi Xi ) is then shown by Schennach (2007) to be identified by knowledge of just the ordinary components of these Fourier transforms. The intuition behind our etension builds on the fact that measurement errors S i and W i do not affect E[Y i m(q i )], and they affect E[Y i X i m(q i )] only by adding the term E[S i W i m(q i )]. Under the identifying assumption that the covariance between S i and W i does not depend on the instruments Q i (which is an implication of classical measurement error), this additional term E(S i W i ) is a constant. Since the Fourier Transform of a constant is a purely singular generalized function, the ordinary component of the Fourier Transform of E[Y i X i m(q i )] is unaffected by this additional term, and so the conditional mean function of interest E(Yi Xi ) remains identified as before. We generalize this argument to also identify higher order conditional moments. The same intuition can be applied to different specifications of the measurement error. An alternative specification we consider which we show is particularly appropriate for Engel curves 6

7 (see also Lewbel 1996) is: Y i = Y i +X i S i, X i = X i W i. This model is consistent with empirical evidence that measurement errors in ependitures increase with total ependiture Xi, and is consistent with the standard survey data generating process in which total reported ependitures X i are constructed by summing the reported ependitures on individual goods like Y i. In this Engel curve model we identify E ( Y i k X i ) = E[H(X i,u i ) k Xi ] for an arbitrary integer k, thereby separating the effects on Y i of observed and unobserved heterogeneity (X i and U i ) from the measurement errors S i and W i. For the Engel curve model we provide two different identification strategies, depending on different assumptions regarding the measurement errors. The first approach builds on the fact that for utility derived Engel curves H(0,U i ) 0, while the second approach makes use of the specific dependence structure between W i and S i implied by the definition of Y i and the construction of X i in the Engel curve framework. We show that this second approach has some features that make it more appropriate for our data, and we use it in our empirical application. 3 Identification As discussed in the previous section, we begin by writing Y i and X i as Y i = H(X i,u i) and X i = m(q i )+V i, where Q i is a vector of instruments, V i is a scalar unobserved random variable independent of Q i, U i is a vector of unobserved disturbances, and the function H(, ) is unknown. The scalar random variable Y i is unobserved, but, encompassing and generalizing the eamples given in the previous section, assume that the observed Y i is given by: Y i = Y i +X i l S i (2) for some non-negative integer l, where E[S i X i ] = 0, so that the measurement error X i l S i is mean zero, but has higher moments that can depend on Xi. Note that l = 0 corresponds to the case of 7

8 classical measurement error in Y while the generalization to l > 0 is useful for dealing with models such as Engel curves, where the variance in measurement errors increases with X i. The regressor X i is also measured with error, with X i satisfying either : X i = X i +W i with E[W i ] = 0 (3) or X i = X i W i with E[W i ] = 1. (4) thereby allowing for either additive or multiplicative measurement errors while retaining the property that E[X i ] = E[Xi ]. Let µk ( i ) = E[Y i k X i ] be the k-th conditional moment of the observed random variable Y i given X i. The goal of this Section is first to provide identification of µk ( i ) for k = 1,...,K, given only knowledge of (Y i,x i,q i ) where X i is defined by either (3) or (4). We then consider identification of moments E[Y k i X i ] We assume the following, which will be maintained throughout: Assumption 1. The random variables Q i, U i, V i, W i and S i are jointly i.i.d. and 1. (i) E[Wi k Q i,v i,u i ] = E[W k ] for k = 1,...,K, (ii) E[Si k Q i,v i,u i ] = E[Si k ] for k = 1,...,K, (iii) V i is independent of Q i, i (iv) E[W i S i Q i ] = E[W i S i ]. Themean independenceassumptions(i) and (ii) with K = 1 are standardfor errorsin variables models and are basically stating that measurement errors are classical. We assume these for higher K because we consider identification of these higher moments, not just the k = 1 conditional moment as in standard models. Assumption (iii), which is also made by Schennach (2007), is a standard control function assumption commonly used for identification and estimation of nonlinear models using instruments. 4 4 As pointed out by Schennach (2008), this assumption is testable, by looking at the dependence between the estimated residuals of the feasible first stage and the instruments, given the maintained assumptions (i) and (ii). 8

9 Assumption (iv) is in fact less restrictive than standard measurement error assumptions. In particular, standard models that do not allow for correlations between measurement errors will trivially satisfy this assumption with E[W i S i Q i ] = E[W i S i ] = 0. Assumption (iv) would also follow from, and is strictly weaker than, the standard classical assumption that measurement errors be independent of correctly measured covariates. Without loss of generality, assume that V i has mean zero, so m(q i ) E[X i Q i ] is nonparametrically identified. Defining Z i = m(q i ) and Ṽi = V i, we may conveniently rewrite equation (1) as: Xi = Z i Ṽi, (5) which we will do hereafter. Following Newey (2001) and Schennach (2007) we will show that, under Assumption 1, knowledge of the conditional moments E[Y k i Z i], for k = 1,...,K, and E[X i Y i Z i ] is enough to identify µ k ( i ) for k = 1,...,K. In the remainder of the paper, for ease of notation, we will drop the subscript i when not needed. It follows from Assumption 1 that E[Y k Z] = E [ [ ] ] E Y k X,Z Z, = E[µ k ( ) Z], (6) and, if measurement error in X is additive (i.e. if (3) holds), then E[XY Z] = E [ [ ] ] (X +W) H(X,U)+X l S Z, = E[X H(X,U) Z]+E[X l Z]E[WS Z], = E[ µ 1 ( ) Z]+E[X l Z]E[WS]. (7) With a similar argument it may be shown that, if measurement error in X is multiplicative (i.e. if (4) holds), then E[XY Z] = E[ µ 1 ( ) Z]+E[X l+1 Z]E[WS]. (8) The proof of identification of µ k ( ) is obtained by eploiting properties of the Fourier transform of these conditional epectations. The following assumption guarantees that these transforms and 9

10 related objects are well defined. Assumption 2. µ k ( ), E[Y k Z] and E[XY Z] are defined and bounded by polynomials for and z R and for any k = 1,...,K. Assumption 2 essentially ecludes specifications for µ k ( ) which rapidly approach infinity like the eponential function and suffices for the following Lemma to hold: Lemma 1. Under Assumption 2, equations (6), (7) and (8) are equivalent to ε y k(ζ) = γ k (ζ)φ(ζ) (9) iε y (ζ) = γ 1 (ζ)φ(ζ)+λiψ(ζ)φ(ζ) (10) with i = 1, overdots denote derivatives with respect to z and ε y k(ζ) = ε y (ζ) = E[Y k Z = z]e iζz dz, γ k (ζ) = µ k ( )e iζ d E[XY Z = z]e iζz dz, φ(ζ) = e iζv df(v) where F(v) is the cdf of Ṽ, λ = E[WS] and ψ(ζ) = l e iζ d if (3) holds or ψ(ζ) = l+1 e iζ d if (4) holds. Lemma 1, whose proof is given in the Appendi, is a generalization of Lemma 1 in Schennach (2007), who considers the special case where l = 0 and k = 1. It is important to note that while φ(ζ), being the characteristic function of Ṽi, is a proper function, ε y (ζ), ε y k(ζ), γ k (ζ) and ψ(ζ) are more abstract generalized functions. Products of generalized functions are not necessarily well defined, so that equations (9) and (10) cannot be manipulated as usual functions to get rid of the characteristic function φ(ζ). Also note that the unknown quantities here are γ k (ζ) and φ(ζ), while ψ(ζ) is the Fourier transform of a power function, and hence is known and equal the suitable generalized derivative of a Dirac delta function. For a more detailed treatment of generalized functions see Lighthill (1962) or the supplementary material in Schennach (2007). Assumption 3. E[ Ṽ ] < and φ(ζ) 0 for all ζ R. Assumption 4. For each k = 1,...,K there eists a finite or infinite constant ζ k such that γ k (ζ) 0 almost everywhere in [ ζ k, ζ k ] and γ k (ζ) = 0 for all ζ > ζ k, 10

11 Assumption 5. The functions γ k (ζ) are such that the following decomposition holds: γ k (ζ) = γ k;o (ζ)+γ k;s (ζ), where γ k;o is an ordinary function, while γ k;s is a purely singular component, which may be seen as a linear combination of generalized derivatives of the Dirac delta function. Assumptions 3 and 4 are the equivalent of Assumptions 2 and 3 in Schennach (2007) and are standardin thedeconvolution literature. Sinceweare seekingnonparametricidentification of γ k (ζ), the characteristic function of Ṽi needs to be non-vanishing, thusecluding uniformor triangular like distributions, while γ k (ζ) needs to be either non-vanishing or must vanish on an infinite interval. This is required for γ k (ζ) to be fully nonparametrically identified. Assumption 4 essentially rules out only specifications for γ k (ζ) which ehibit sinusoidal behaviours, which is very unlikely in most economic applications. 5 Assumption 5 only ecludes pathological (and generally empirically irrelevant) cases in which such decomposition may not hold on a set of measure zero. The following theorem states the main identification result. Theorem 1. Under Assumptions 1-5, µ k ( ) for k = 1,...,K are nonparametrically identified. Also, if ζ 1 > 0 in Assumption 4 then µ k ( ) = (2π) 1 γ k (ζ)e iζ dζ where 0 if ε y k(ζ) = 0 γ k (ζ) = ε y k(ζ)/φ(ζ) otherwise, (11) φ(ζ) is the characteristic function of Ṽ V given, for ζ < ζ 1, by ( ζ φ(ζ) = ep 0 iε (z )y,o (ζ) ε y,o (ζ) ) dζ (12) and where ε y,o (ζ) and ε (z )y,o (ζ) denote the ordinary function components of ε y (ζ) = E[Y Z = z]e iζz dz and ε (z )y (ζ) = E[(Z X)Y Z = z]e iζz dz respectively. 5 If H(X,U) were parametrically specified, then Assumption 4 could be relaed, because in that case information obtained from a finite number of points of γ k (ζ) would generally suffice for identification. 11

12 Our Theorem 1 is a generalization of Theorem 1 in Schennach (2007). The proof is in the appendi, but essentially works as follows. By Assumption 5, γ k (ζ) can be written as the sum of an ordinary function and a linear combination of generalized derivatives of the Dirac delta function, which correspond to the purely singular component. Hence by Lemma 1 a similar decomposition also holds for ε y k(ζ) and ε y (ζ). The last term in (10) is a purely singular generalized function and so only affects the singular component of ε y (ζ). Since Schennach (2007) proved that φ(ζ) is identified by knowledge of just the ordinary components of ε y (ζ) and ε y (ζ), allowing for W and S to be correlated does not alter the identification of φ(ζ). The function of interest γ k (ζ) is then obtained from equation (9) as in (11), and its inverse Fourier transform gives µ k ( ). Theorem 1 provides nonparametric identification of the first K conditional moments of Y given X. If Y given X has a well-defined moment generating function, then applying Theorem 1 with K infinite provides nonparametric identification of the entire conditional distribution of Y given X, as follows: Corollary 1. Under the Assumptions of Theorem 1 if ζ 1 = then the characteristic function of the unobserved X, φ X (ζ), is identified and is given by: φ X (ζ) = φ Z(ζ) φ(ζ), (13) where φ Z (ζ) is the characteristic function of the observed random variable Z. This result immediately follows from equation (5) and by noting that equation (12) gives the epression of the characteristic function of Ṽ. The assumption of ζ 1 = encompasses most of the empirically relevant specifications for the conditional mean, and so is not unduly restrictive. Note that in the proof of Theorem 1 we only considered ζ k > 0 since the case where ζ k = 0 only occurs if µ k ( ) is a polynomial in X, and that specification has already been shown to be identified by Hausman, Newey, Ichimura, and Powell (1991). Theorem 1 establishes a set of assumptions under which µ k ( ) = E[Y k X ] is identified for integers k. However, the objects of policy relevance are usually the conditional moments of the true, unobserved Y, that is ω k ( ) = E[Y k X ], or more generally the conditional distribution of Y given X. For k = 1 we have E[Y X ] = E[Y X ], so ω 1 ( ) = µ 1 ( ) and the primary moment of interest is already identified by Theorem 1. Now consider identification of higher moments, that 12

13 is ω k ( ) for k > 1. Corollary 2. Let Assumptions of Theorem 1 hold, then: k 1 ( ) k ω k ( ) = µ k ( ) ω j ( ) l(k j) E[S k j ], (14) j j=0 hence ω k ( ) is identified up to knowledge of E[S j ] for j = 2,...,k. Corollary 2 shows that what is needed to identify ω k ( ) for k > 1 is knowledge of corresponding moments of the unconditional distribution of S. Identifying moments of the distribution of S requires additional information which may be provided by a combination of additional data, restrictions imposed on the dependence structure between W and S, or additional information regarding features of H(X,U). Such information needs to be considered on a case by case basis. In the net section we consider the Engel curve setting where the nature of the variables involved implies the eistence of a specific dependence structure between W and S, or a boundary restriction on H(X,U), either of which will provide sufficient additional information to identify moments of S and hence ω k ( ) using Corollary 2. 4 Identification of Engel curves Let Y l be unobserved ependitures on a particular good (or group of goods) l, for l = 1,...,L and let X = L l=1 Y l be total ependitures on all goods. Let Y = Y1 denote the particular good of interest. Then Y = H(X,U) is the Engel curve for the good of interest, where U is a vector of individual consumer specific utility (preference) related parameters. The goal is then identification of the conditional distribution of Y given X. We now describe two different possible models of measurement errors, each of which yields information that may be used to identify the conditional distribution of demands Y given total ependiture X using Theorem 1 and Corollary 2. In each case it is assumed, as is generally true empirically, that observed total ependitures X are obtained by summing the observed ependitures Y l on each good l, so X = L l=1 Y l. For the first of these two models, assume Y l = Y l + S l for each good l, corresponding to measurement error in the form of equation (2) with l = 0. It then follows that the observed 13

14 X = X + W where the measurement error W = L l=1 S l is of the form given in equation (3). We may then apply Theorem 1 to identify µ k ( ). To net apply Corollary 2 assume first that one cannot purchase negative amounts of any good, so Y l 0 since each Y l is defined as a level of ependitures. It then follows that the boundary condition H(0,U) 0 holds for all values of U, because if X = L l=1 Y l = 0 and every Y l 0, then every Y l = 0. The restriction that H(0,U) 0 in turn implies that ω k (0) = 0 for all k 1, and by equation (14) we obtain k E[S k ] = µ k (0) ω j (0)E[S k j ] = µ k (0) (15) j=1 which, with µ k ( ) identified by Theorem 1, identifies E[S k ], and this in turn permits application of Corollary 2 to identify the moments of interest ω k ( ) for all k, and by etension provides identification of the entire conditional distribution of Y given X, assuming the eistence of a well defined moment generating function for this distribution. This first model of measurement error illustrates our identification methodology, but it imposes the restrictive (for Engel curves) assumption that the independent measurement error is additive even though consumption must be non-negative. Moreover, it s likely that measurement errors in ependitures increase with the level of ependitures. Therefore, for estimation later we will focus on a nonparametric generalization of an alternative specification proposed by Lewbel (1996) in the contet of a parametric Engel curve model. This model assumes Y l = Y l +X S l for each good l, corresponding to measurement error in the form of equation (2) with l = 1 for S = S 1. Let S = L l=2 S l. Assume that S and S (corresponding to measurement errors for different goods) have mean zero and are independent of each other. Then X = L l=1 Y l = X (1+S + S ), corresponding to multiplicative measurement error in X given by equation (4) with W = 1+S + S (16) and E(W) = 1 as required by Theorem 1. Given this structure, moments of S are identified given 14

15 knowledge of E[W k ] and E[W k S] for k = 1,..., as follows. Using equation (16) we obtain: E[W k S] = E[[S +(1+ S)] k S] k ( ) k = E S S j (1+ j = k j=0 j=0 S) k j ( ) k E[S j+1 ]E[(1+ j S) k j ], which implies: k 1 ( ) k E[S k+1 ] = E[W k S] E[S j+1 ]E[(1+ j S) k j ], j=0 where E[(1+ S) k ] is given by: E[(1+ S) k ] = E[W k ] k j=1 ( ) k E[S j ]E[(1+ j S) k j ]. The following Theorem establishes formal identification for these quantities. Theorem 2. Let Assumptions 1-4 and equations (1) and (4) hold. Let the first K moments of X eist finite with E[X k ] 0 and E[X k Z] 0 for every k = 1,...,K, then the first K moments of W are identified and E[W k ] = k j=0 E[X k ] ( k j) ( i) k j E[Z j ]φ (k j) (0). (17) Furthermore if ζ 1 = in Assumption 4 then the moments E[W k S] for k = 1,...,K l are also identified and E[W k S] = E[Xk Y Z = z] (2π) 1 E[W k ] ( i) k γ (k) 1 (ζ)φ(ζ)e iζz dζ ). (18) z j ( i) k+l j φ (k+l j) (0) k+l j=0 ( k+l j where γ (k) 1 (ζ) is the k-th derivative of γ 1(ζ) as defined in equation (11), while φ(ζ) is as in (12). The proof of Theorem 2 is given in the Appendi. Intuitively identification of E[W k ] for k = 1,...,K follows by noting that, from equation (4) and by Assumption 1, E[X k ] = E[X k ]E[W k ], and since the unobserved distribution X is identified by Corollary 1 every moment of W is also 15

16 identified. Furthermore from equation (4) we have E[X k Y Z] = E[X µ 1 ( ) Z]E[W k ]+E[X k+l Z]E[W k S], whichonlyinvolvesidentifiedmomentsapartfrome[w k S], henceprovingidentificationofe[w k S]. BothY andx arenon-negativerandomvariables, sotherequirementthatthefirstk marginal and conditional moments of X be non-zero is satisfied as long as X is non-degenerate. This is because we are considering raw moments and not central ones, hence we are not for instance ruling out symmetric distributions, for which the third central moment would be zero. Furthermore, the assumption of ζ 1 =, generally covers empirically relevant frameworks as was discussed in Section 3. Under the assumptions of Theorems 1 and 2 any conditional moment of the distribution of the unobserved Y on X is identified. It then follows that the entire conditional distribution of Y given X is identified, assuming that this conditional distribution has a well defined moment generating function. 5 Estimation In this Section we propose a sieve based nonparametric estimator for the conditional moments of the distribution of Y given X in the case of Engel curves. Many studies have documented a variety of nonlinearities and substantial unobserved heterogeneity in shapes, see Blundell, Browning, and Crawford (2003) and Lewbel and Pendakur (2009) for instance, or Lewbel (2010) for a survey. It is therefore useful to provide an estimator that allows for the presence of measurement error of the specific kind implied by ependiture data, while not imposing functional form restrictions. Also as noted above, unlike previous studies, we are able to disentangle the variance components due to measurement error from those due to preference heterogeneity. The estimator we propose is based on applying the sieve GMM estimator of Ai and Chen (2003) to the conditional moments used for Theorem 2. For ease of eposition we will just consider estimation of the first two conditional moments, but given our identification results the etension 16

17 to higher moments is purely mechanical. The model is Y = Y +X S, X = X W, and X = m(q)+v. The goal is consistent estimation of the functions ω 1 ( ) and ω 2 ( ), where by construction: Y = ω 1 (X )+ǫ 1 and Y 2 = ω 2 (X )+ǫ 2 with E[ǫ 1 X ] = E[ǫ 2 X ] = 0 by definition of ω 1 ( ) and ω 2 ( ). The data consist of an i.i.d. sample of size N from the triple (Y,X,Q). Based on Theorems 1 and 2 we eploit the moment restrictions implied by equations (6) and (8) and define: ρ 0 (q i ;θ) = y i ω 1 (ẑ i σv)f(v)dv, (19) ρ 1 (q i ;θ) = y 2 i [ω 2 (ẑ i σv)+λ(ẑ i σv) 2] f(v)dv, (20) ρ 2 (q i ;θ) = i y i [(ẑi σv)ω 1 (ẑ i σv)+λ(ẑ i σv) 2] f(v)dv, (21) where q i = (y i, i,ẑ i ), i = 1,...,N, denotes observations and θ = (λ,σ,ω 1 ( ),ω 2 ( ),f( )), with f( ) being the probability density function of the first stage error term V. According to equation (5) ẑ i are the first stage fitted values of a nonparametric regression of the observed X on Q. Defining ρ(q i ;θ) = (ρ 0 (q i ;θ),ρ 1 (q i ;θ),ρ 2 (q i ;θ)), a consistent estimator is then obtained from: E[c(ẑ i ) ρ(q i ;θ)] = 0 (22) for a suitable vector of instruments c(ẑ i ). The computation of (22) is complicated by several factors. First, the parameter vector θ is infinite-dimensionalduetothepresenceoftheunknownfunctionsω 1 ( ), ω 2 ( )andf( ). Second,even if these functions were finitely parametrized, the computation of ρ i (q i ;θ) would involve integrals (19)-(21) which do not have a closed form solution. We address both of these issues by adopting a minimum distance sieve estimator along the lines of Ai and Chen (2003), replacing the space H = H 1 H 2 H f. with a finite-dimensional sieve 17

18 space H n = H 1n H 2n H fn which becomes dense in the original space H as n increases as in Grenander (1981). Computations involving integrals are simplified by a convenient choice of the sieve space H fn. We consider cosine polynomial and Hermite polynomial sieve spaces to approimate conditional moments (ω 1 ( ) and ω 2 ( )) and the density function f(v) respectively, that is: ω j ( ) f(v) N j β ij b ij ( ), j = 1,2, (23) i=0 N f α i h i ( ), (24) i=0 for some N 1, N 2, N f, and where the basis functions {b ij ( ),i = 0,1,...} and {h i ( ),i = 0,1,...} are given by: ( iπ( b ij ( ) a j ) ) = cos, h i ( ) = H i (v)φ(v) b j a j for some a j,b j, j = 1,2. The function φ( ) is the standard normal density function, while H i ( ) is the i-th order Hermite polynomial. Cosine polynomial sieves are chosen to approimate conditional moments since they are known for well approimating aperiodic functions on an interval (see Chen 2007 and Newey and Powell 2003). Hermite polynomial sieves, on the other hand, are well suited for approimating the density function f(v) for two reasons. First, standard restrictions for the approimating density to integrate to one and to be mean zero with unit variance are trivially imposed by setting α 0 = 1 and α 1 = α 2 = 0. Second, the fact that a Hermite polynomial is multiplied by the standard normal density allows us to easily compute the integrals in equations (19)-(21) along the lines of Newey (2001) and Wang and Hsiao (2011). Indeed, by substituting (23) and (24) in (19), (20) and (21) we obtain: N 1 N f ˆρ 0 (q i ;η) = y i β i1 α l b i1 (ẑ i σv)h l (v)φ(v)dv, i=0 l=0 N 2 N f [bi2 ˆρ 1 (q i ;η) = yi 2 β i2 α l (ẑ i σv)+λ(ẑ i σv) 2] H l (v)φ(v)dv, i=0 l=0 N 1 N f [(ẑi ˆρ 2 (q i ;η) = i y i β i1 α l σv)b i1 (ẑ i σv)+λ(ẑ i σv) 2] H l (v)φ(v)dv, i=0 l=0 18

19 where η = (λ,σ,α 0,...,α Nf,β 10,...,β 1N1,β 20,...,β 2N2 ). Thus the integrals involved in ˆρ(q i ;η) can be computed with an arbitrary degree of precision by averaging the value of the integrand function over randomly drawn observations from a standard normal density. Letting v j for j = 1,...,J be J random draws from a standard normal distribution, ˆρ 0 (q i ;η) is in this way computed as N f N 1 J ˆρ 0 (q i ;η) = y i J 1 β i1 α l b i1 (ẑ i σv j )H l (v j ). i=0 l=0 j=1 Similar epressions hold for ˆρ 1 (q i ;η) and ˆρ 2 (q i ;η). 6 It then follows from Theorems 1 and 2 and from Lemma 3.1 in Ai and Chen (2003) that a consistent estimator for η is given by arg min η 1 N N ˆρ(q i,η) c(z i )[Σ(q i )] 1 c(z i ) ˆρ(q i,η) i=1 with a positive definite matri Σ(q i ). We apply the two-step GMM procedure: (1) Obtain an initial estimate ˆη from the consistent estimator: arg min η 1 N N ˆρ(q i,η) c(z i )c(z i ) ˆρ(q i,η). i=1 (2) Improve the efficiency of the estimator η by applying the minimization: arg min η 1 N N 1c(zi ˆρ(q i,η) c(z i )[ˆΣ(qi )] ) ˆρ(q i,η). i=1 where ˆΣ(q i ) = {ˆσ jl (q i )} is obtained from ˆη as: ˆσ jl (q i ) = 1 N N c j (z i )c j (z i )ˆρ j (q i, ˆη)ˆρ l (q i, ˆη). i=1 Ai and Chen (2003) and Newey and Powell (2003) show that this is a consistent estimator for ˆη, and derive the asymptotically normal limiting distribution for the parametric components of θ. 6 While N 1, N 2 and N f need to increase with sample size and play the role of smoothing parameters, J only affects the degree of precision with which the integrals are evaluated and should be set as large as is computationally practical, analogous to the choice of the fineness of the grid in ordinary numerical integration. 19

20 Our primary interest is estimation of the functions ω 1 ( ) and ω 2 ( ). Ai and Chen (2003) show that, under suitable assumptions, the rate of convergence of infinite dimensional components of θ like these is faster than n 1/4. 6 Simulation Study A simulation study is employed to assess the finite sample performance of the estimator derived in Section 5. For simplicity we focus on the estimation of the conditional mean of Y. The simulation design is: Y = g(x )+U, U N(0,σ 2 U), X = 1+0.4Z V, Z N(5,1.5), V N(0,0.3 2 ) where σu 2 is set so that the R2 of the regression of Y on X is roughly Three different specifications for the conditional mean function g( ) are considered. The first is the standard Working (1943) and Leser (1963) specification of Engel curves, corresponding to budget shares linear in the logarithm of X. The others are a third order polynomial Engel curve and a Fourier function Engel curve. The choice of the parameters for each of the three specifications is: g 1 (X ) = X 0.5X log(x ) g 2 (X ) = 0.8X +0.02X X 3 g 3 (X ) = 4 2sin(2π(X 0)/4) +0.5cos(2π(X 0)/4) Data (Y,X ) are assumed to be contaminated by measurement errors, so what is observed is the couple (Y,X) given by Y = Y +X S, X = X W, with W = S + S +1, E[S] = E[ S] = 0. The variances of measurement errors S and S are chosen such that Var[logX ] Var[logW], so half of the variation in the observed logx is measurement 20

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