
on Econometrics 
By:  Xiaohong Chen (Institute for Fiscal Studies and Yale); Wei Biao Wu; Yanping Yi 
Abstract:  <p><p>This paper considers efficient estimation of copulabased semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant distributions and parametric copula functions; where the copulas capture all scalefree temporal dependence and tail dependence of the processes. The Markov models generated via tail dependent copulas may look highly persistent and are useful for financial and economic applications. We first show that Markov processes generated via Clayton, Gumbel and Student's t copulas (with tail dependence) are all geometric ergodic. We then propose a sieve maximum likelihood estimation (MLE) for the copula parameter, the invariant distribution and the conditional quantiles. We show that the sieve MLEs of any smooth functionals are rootn consistent, asymptotically normal and efficient; and that the sieve likelihood ratio statistics is chisquare distributed. We present Monte Carlo studies to compare the finite sample performance of the sieve MLE, the twostep estimator of Chen and Fan (2006), the correctly specified parametric MLE and the incorrectly specified parametric MLE. The simulation results indicate that our sieve MLEs perform very well; having much smaller biases and smaller variances than the twostep estimator for Markov models generated by Clayton, Gumbel and other copulas having strong tail dependence.</p></p> 
JEL:  C14 C22 
Date:  2009–03 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:06/09&r=ecm 
By:  Victor Chernozhukov (Institute for Fiscal Studies and Massachusetts Institute of Technology); Sokbae 'Simon' Lee (Institute for Fiscal Studies and University College London); Adam Rosen (Institute for Fiscal Studies and University College London) 
Abstract:  <p>We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set. Our approach is especially convenient in models comprised of a continuum of inequalities that are separable in parameters, and also applies to models with inequalities that are nonseparable in parameters. Since analog estimators for intersection bounds can be severely biased in finite samples, routinely underestimating the length of the identified set, we also offer a (downward/upward) median unbiased estimator of these (upper/lower) bounds as a natural byproduct of our inferential procedure. Furthermore, our method appears to be the first and currently only method for inference in nonparametric models with a continuum of inequalities. We develop asymptotic theory for our method based on the strong approximation of a sequence of studentized empirical processes by a sequence of Gaussian or other pivotal processes. We provide conditions for the use of nonparametric kernel and series estimators, including a novel result that establishes strong approximation for general series estimators, which may be of independent interest. We illustrate the usefulness of our method with Monte Carlo experiments and an empirical example.</p> 
Date:  2009–07 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:19/09&r=ecm 
By:  Pablo GuerronQuintana; Atsushi Inoue; Lutz Kilian 
Abstract:  The authors show that in weakly identified models (1) the posterior mode will not be a consistent estimator of the true parameter vector, (2) the posterior distribution will not be Gaussian even asymptotically, and (3) Bayesian credible sets and frequentist confidence sets will not coincide asymptotically. This means that Bayesian DSGE estimation should not be interpreted merely as a convenient device for obtaining asymptotically valid point estimates and confidence sets from the posterior distribution. As an alternative, the authors develop a new class of frequentist confidence sets for structural DSGE model parameters that remains asymptotically valid regardless of the strength of the identification. The proposed set correctly reflects the uncertainty about the structural parameters even when the likelihood is flat, it protects the researcher from spurious inference, and it is asymptotically invariant to the prior in the case of weak identification. 
Keywords:  Stochastic analysis ; Macroeconomics  Econometric models 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:fip:fedpwp:0913&r=ecm 
By:  Xiaohong Chen (Institute for Fiscal Studies and Yale); Demian Pouzo 
Abstract:  <p><p><p>This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (Θ) and unknown functions (h)of endogenous variables. We show that: (1) the penalized sieve minimum distance(PSMD) estimator (ˆΘ, ˆh) can simultaneously achieve rootn asymptotic normality of ˆΘ and nonparametric optimal convergence rate of ˆh, allowing for noncompact function parameter spaces; (2) a simple weighted bootstrap procedure consistently estimates the limiting distribution of the PSMD ˆΘ; (3) the semiparametric efficiency bound formula of Ai and Chen (2003) remains valid for conditional models with nonsmooth residuals, and the optimally weighted PSMD estimator achieves the bound; (4) the centered, profiled optimally weighted PSMD criterion is asymptotically chisquare distributed. We illustrate our theories using a partially linear quantile instrumental variables (IV) regression, a Monte Carlo study, and an empirical estimation of the shapeinvariant quantile IV Engel curves.</p> </p><p></p><p><p>This is an updated version of CWP09/08.</p></p> </p><p></p> 
Date:  2009–07 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:20/09&r=ecm 
By:  Victor Chernozhukov (Institute for Fiscal Studies and Massachusetts Institute of Technology); Roberto Rigobon; Thomas Stoker (Institute for Fiscal Studies and Massachusetts Institute of Technology) 
Abstract:  <p>We give semiparametric identification and estimation results for econometric models with a regressor that is endogenous, bound censored and selected,called a Tobin regressor. First, we show that true parameter value is set identified and characterize the identification sets. Second, we propose novel estimation and inference methods for this true value. These estimation and inference methods are of independent interest and apply to any problem where the true parameter value is point identified conditional on some nuisance parameter values that are setidentified. By fixing the nuisance parameter value in some suitable region, we can proceed with regular point and interval estimation. Then, we take the union over nuisance parameter values of the point and interval estimates to form the final set estimates and confidence set estimates. The initial point or interval estimates can be frequentist or Bayesian. The final set estimates are setconsistent for the true parameter value, and confidence set estimates have frequentist validity in the sense of covering this value with at least a prespecified probability in large samples. We apply our identification, estimation, and inference procedures to study the effects of changes in housing wealth on household consumption. Our set estimates fall in plausible ranges, significantly above low OLS estimates and below high IV estimates that do not account for the Tobin regressor structure.</p> 
Date:  2009–05 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:12/09&r=ecm 
By:  Mohitosh Kejriwal; Pierre Perron; Jing Zhou 
Abstract:  This paper considers the problem of testing for multiple structural changes in the persistence of a univariate time series. We propose supWald tests of the null hypothesis that the process has an autoregressive unit root against the alternative hypothesis that the process alternates between stationary and unit root regimes. Both nontrending and trending cases are analyzed. We derive the limit distributions of the tests under the null and establish their consistency under the relevant alternatives. The computation of the test statistics as well as asymptotic critical values is facilitated by the dynamic programming algorithm proposed in Perron and Qu (2006) which allows the minimization of the sum of squared residuals under the alternative hypothesis while imposing within and cross regime restrictions on the parameters. Finally, we present Monte Carlo evidence to show that the proposed tests perform quite well in finite samples relative to those available in the literature. 
Keywords:  structural change, persistence, Wald tests, unit root, parameter restrictions 
JEL:  C22 
Date:  2009–08 
URL:  http://d.repec.org/n?u=RePEc:pur:prukra:1223&r=ecm 
By:  Joel Horowitz (Institute for Fiscal Studies and Northwestern University); Sokbae 'Simon' Lee (Institute for Fiscal Studies and University College London) 
Abstract:  <p>This paper is concerned with developing uniform confidence bands for functions estimated nonparametrically with instrumental variables. We show that a sieve nonparametric instrumental variables estimator is pointwise asymptotically normally distributed. The asymptotic normality result holds in both mildly and severely illposed cases. We present an interpolation method to obtain a uniform confidence band and show that the bootstrap can be used to obtain the required critical values. Monte Carlo experiments illustrate the finitesample performance of the uniform confidence band.</p> 
Date:  2009–07 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:18/09&r=ecm 
By:  Manuel Arellano (Institute for Fiscal Studies and CEMFI); Stéphane Bonhomme 
Abstract:  <p><p><p>We study the identification of panel models with linear individualspecific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a tradeoff between heterogeneity and error dynamics. We show identification of the density of individual effects when errors follow an ARMA process under conditional independence. We discuss GMM estimation of moments of effects and errors, and introduce a simple density estimator of a slope effect in a special case. As an application we estimate the effect that a mother smokes during pregnancy on child's birth weight.</p></p></p> 
Date:  2009–08 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:22/09&r=ecm 
By:  LeYu Chen (Institute for Fiscal Studies and Academia Sinica) 
Abstract:  <p><p>This paper presents new identification results for the class of structural dynamic discrete choice models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where utility function of one choice in the model is parametric but the distribution of unobserved heterogeneities is nonparametric. The proposed identification method does not rely on the availability of terminal period data and hence can be applied to infinite horizon structural dynamic models. For identification we assume availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the structural dynamic discrete choice model is semiparametrically identified using the control function approach.</p> </p><p><p>This is a substantial revision of "Semiparametric identification of structural dynamic optimal stopping time models", CWP06/07.</p></p> 
Date:  2009–05 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:08/09&r=ecm 
By:  Steven T. Berry; Philip A. Haile 
Abstract:  We consider identification of nonparametric random utility models of multinomial choice using "micro data," i.e., observation of the characteristics and choices of individual consumers. Our model of preferences nests random coefficients discrete choice models widely used in practice with parametric functional form and distributional assumptions. However, the model is nonparametric and distribution free. It allows choicespecific unobservables, endogenous choice characteristics, unknown heteroskedasticity, and highdimensional correlated taste shocks. Under standard "large support" and instrumental variables assumptions, we show identifiability of the random utility model. We demonstrate robustness of these results to relaxation of the large support condition and show that when it is replaced with a weaker "common choice probability" condition, the demand structure is still identified. We show that key maintained hypotheses are testable. 
JEL:  C35 
Date:  2009–08 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:15276&r=ecm 
By:  Caner, Mehmet; Morrill, Melinda 
Abstract:  The instrumental variables strategy is commonly employed in empirical research. For correct inference using this econometric technique, the instruments must be perfectly exogenous and relevant. In fact, the standard tratio test statistic used in this context yields unreliable and often inaccurate results even when there is only a slight violation of the exclusion restriction. It is crucial to realize that to make reliable inferences on the structural parameters we need to know the true correlation between the structural error and the instruments. The main innovation in this paper is to identify an appropriate test in this context: a joint null hypothesis of the structural parameters with the correlation between the instruments and the structural error term. Since correlation cannot be estimated, we propose a test statistic involving a grid search over correlation values. To address inference under violations of exogeneity, significant contributions have been made in the recent literature by assuming some degree of nonexogeneity. We introduce a new approach by deriving a modified tstatistic that corrects for the bias associated with nonexogeneity of the instrument. A key advantage of our approach over that of the previous literature is that we do not need to make any assumptions about the degree of violation of exogeneity either as possible values or prior distributions. In particular, our method is not a form of sensitivity analysis. Since our modified test statistic is continuous and monotonic in correlation it is easy to conduct inference by a simple grid search. Even though the joint null may seem to be limiting in interpreting rejection, we can still make accurate inferences on the structural parameters because of a feature of the grid search over correlation values. The procedure for calculating the modified coefficients and statistics is illustrated with two empirical examples. 
Keywords:  Violation of exogeneity; Instrumental variables regression: Joint test 
JEL:  C20 
Date:  2009–08–14 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:16790&r=ecm 
By:  LeYu Chen (Institute for Fiscal Studies and Academia Sinica); Jerzy Szroeter 
Abstract:  <p><p>Econometric inequality hypotheses arise in diverse ways. Examples include concavity restrictions on technological and behavioural functions, monotonicity and dominance relations, onesided constraints on conditional moments in GMM estimation, bounds on parameters which are only partially identified, and orderings of predictive performance measures for competing models. In this paper we set forth four key properties which tests of multiple inequality constraints should ideally satisfy. These are (1) (asymptotic) exactness, (2) (asymptotic)similarity on the boundary, (3) absence of nuisance parameters from the asymptotic null distribution of the test statistic, (4) low computational complexity and boostrapping cost. We observe that the predominant tests currently used in econometrics do not appear to enjoy all these properties simultaneously. We therefore ask the question : Does there exist any nontrivial test which, as a mathematical fact, satisfies the first three properties and, by any reasonable measure, satisfies the fourth ? Remarkably the answer is affirmative. The paper demonstrates this constructively. We introduce a method of test construction called chaining which begins by writing multiple inequalities as a single equality using zeroone indicator functions. We then smooth the indicator functions. The approximate equality thus obtained is the basis of a wellbehaved test. This test may be considered as the baseline of a wider class of tests. A full asymptotic theory is provided for the baseline. Simulation results show that the finitesample performance of the test matches the theory quite well.</p></p> 
Date:  2009–06 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:13/09&r=ecm 
By:  Alexandre Belloni; Victor Chernozhukov (Institute for Fiscal Studies and Massachusetts Institute of Technology) 
Abstract:  <p>We consider median regression and, more generally, quantile regression in highdimensional sparse models. In these models the overall number of regressors p is very large, possibly larger than the sample size n, but only s of these regressors have nonzero impact on the conditional quantile of the response variable, where s grows slower than n. Since in this case the ordinary quantile regression is not consistent, we consider quantile regression penalized by the L1norm of coefficients (L1QR). First, we show that L1QR is consistent at the rate of the square root of (s/n) log p, which is close to the oracle rate of the square root of (s/n), achievable when the minimal true model is known. The overall number of regressors p affects the rate only through the log p factor, thus allowing nearly exponential growth in the number of zeroimpact regressors. The rate result holds under relatively weak conditions, requiring that s/n converges to zero at a superlogarithmic speed and that regularization parameter satisfies certain theoretical constraints. Second, we propose a pivotal, datadriven choice of the regularization parameter and show that it satisfies these theoretical constraints. Third, we show that L1QR correctly selects the true minimal model as a valid submodel, when the nonzero coefficients of the true model are well separated from zero. We also show that the number of nonzero coefficients in L1QR is of same stochastic order as s, the number of nonzero coefficients in the minimal true model. Fourth, we analyze the rate of convergence of a twostep estimator that applies ordinary quantile regression to the selected model. Fifth, we evaluate the performance of L1QR in a MonteCarlo experiment, and provide an application to the analysis of the international economic growth.</p> 
Date:  2009–05 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:10/09&r=ecm 
By:  Kim, Hyeongwoo; Durmaz, Nazif 
Abstract:  The least squares (LS) estimator suffers from signicant downward bias in autoregressive models that include an intercept. By construction, the LS estimator yields the best insample fit among a class of linear estimators notwithstanding its bias. Then, why do we need to correct for the bias? To answer this question, we evaluate the usefulness of the two popular bias correction methods, proposed by Hansen (1999) and So and Shin (1999), by comparing their outofsample forecast performances with that of the LS estimator. We find that biascorrected estimators overall outperform the LS estimator. Especially, Hansen's grid bootstrap estimator combined with a rolling window method performs the best. 
Keywords:  SmallSample Bias; Grid Bootstrap; Recursive Mean Adjustment; OutofSample Forecast; DieboldMariano Test 
JEL:  C53 
Date:  2009–05 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:16780&r=ecm 
By:  Massimiliano Caporin (Dipartimento di Scienze Economiche "Marco Fanno", Universita degli Studi di Padova); Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo) 
Abstract:  Large and very large portfolios of financial assets are routine for many individuals and organizations. The two most widely used models of conditional covariances and correlations are BEKK and DCC. BEKK suffers from the archetypal "curse of dimensionality" whereas DCC does not. This is a misleading interpretation of the suitability of the two models to be used in practice. The primary purposes of the paper are to define targeting as an aid in estimating matrices associated with large numbers of financial assets, analyze the similarities and dissimilarities between BEKK and DCC, both with and without targeting, on the basis of structural derivation, the analytical forms of the sufficient conditions for the existence of moments, and the sufficient conditions for consistency and asymptotic normality, and computational tractability for very large (that is, ultra high) numbers of financial assets, to present a consistent two step estimation method for the DCC model, and to determine whether BEKK or DCC should be preferred in practical applications. 
Date:  2009–08 
URL:  http://d.repec.org/n?u=RePEc:tky:fseres:2009cf638&r=ecm 
By:  Xiaohong Chen (Institute for Fiscal Studies and Yale); Lars Peter Hansen; Jose A. Scheinkman 
Abstract:  <p><p><p>We investigate a method for extracting nonlinear principal components. These principal components maximize variation subject to smoothness and orthogonality constraints; but we allow for a general class of constraints and densities, including densities without compact support and even densities with algebraic tails. We provide primitive sufficient conditions for the existence of these principal components. We also characterize the limiting behavior of the associated eigenvalues, the objects used to quantify the incremental importance of the principal components. By exploiting the theory of continuoustime, reversible Markov processes, we give a different interpretation of the principal components and the smoothness constraints. When the diffusion matrix is used to enforce smoothness, the principal components maximize longrun variation relative to the overall variation subject to orthogonality constraints. Moreover, the principal components behave as scalar autoregressions with heteroskedastic innovations. Finally, we explore implications for a more general class of stationary, multivariate diffusion processes.</p></p></p> 
Date:  2009–05 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:07/09&r=ecm 
By:  Victor Chernozhukov (Institute for Fiscal Studies and Massachusetts Institute of Technology); Ivan FernandezVal; Blaise Melly 
Abstract:  <p><p>In this paper we develop procedures for performing inference in regression models about how potential policy interventions affect the entire marginal distribution of an outcome of interest. These policy interventions consist of either changes in the distribution of covariates related to the outcome holding the conditional distribution of the outcome given covariates fixed, or changes in the conditional distribution of the outcome given covariates holding the marginal distribution of the covariates fixed. Under either of these assumptions, we obtain uniformly consistent estimates and functional central limit theorems for the counterfactual and status quo marginal distributions of the outcome as well as other functionvalued effects of the policy, including, for example, the effects of the policy on the marginal distribution function, quantile function, and other related functionals. We construct simultaneous confidence sets for these functions; these sets take into account the sampling variation in the estimation of the relationship between the outcome and covariates. Our procedures rely on, and our theory covers, all main regression approaches for modeling and estimating conditional distributions, focusing especially on classical, quantile, duration, and distribution regressions. Our procedures are general and accommodate both simple unitary changes in the values of a given covariate as well as changes in the distribution of the covariates or the conditional distribution of the outcome given covariates of general form. We apply the procedures to examine the effects of labor market institutions on the U.S. wage distribution.</p></p> 
Date:  2009–05 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:09/09&r=ecm 
By:  Stéphane Loisel (SAF  Laboratoire de Sciences Actuarielle et Financière  Université Claude Bernard  Lyon I : EA2429); Christian Mazza (Département de Mathématiques  Université de Fribourg); Didier Rullière (SAF  Laboratoire de Sciences Actuarielle et Financière  Université Claude Bernard  Lyon I : EA2429) 
Abstract:  The classical risk model is considered and a sensitivity analysis of finitetime ruin probabilities is carried out. We prove the weak convergence of a sequence of empirical finitetime ruin probabilities. Socalled partly shifted risk processes are introduced, and used to derive an explicit expression of the asymptotic variance of the considered estimator. This provides a clear representation of the influence function associated with finite time ruin probabilities, giving a useful tool to quantify estimation risk according to new regulations. 
Keywords:  Finitetime ruin probability; robustness; Solvency II; reliable ruin probability; asymptotic normality; influence function; partly shifted risk process; Estimation Risk Solvency Margin. (ERSM). 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:hal:journl:hal00168716_v1&r=ecm 
By:  Niels Haldrup (Aarhus University and CREATES); Frank S. Nielsen (Aarhus University and CREATES); Morten Ørregaard Nielsen (Queen's University and CREATES) 
Abstract:  A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the observed regime states as well as the possibility of fractional cointegration. The model is motivated by the dynamics of electricity prices where the transmission of power is subject to occasional congestion periods. For a system of bilateral prices noncongestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between a univariate price process under noncongestion and a bivariate price process under congestion. At the same time, it is an empirical regularity that electricity prices tend to show a high degree of long memory, and thus that prices may be fractionally cointegrated. Analysis of Nord Pool data shows that even though the prices are identical under noncongestion, the prices are not, in general, fractionally cointegrated in the congestion state. Hence, in most cases price convergence is a property following from regime switching rather than a conventional error correction mechanism. Finally, the suggested model is shown to deliver forecasts that are more precise compared to competing models. 
Keywords:  Cointegration, electricity prices, fractional integration, long memory, regime switching 
JEL:  C32 
Date:  2009–08 
URL:  http://d.repec.org/n?u=RePEc:qed:wpaper:1211&r=ecm 
By:  Yingyong Dong; Arthur Lewbel (Institute for Fiscal Studies and Boston College) 
Abstract:  <p><p>Suppose V and U are two independent mean zero random variables, where V has an asymmetric distribution with two mass points and U has a symmetric distribution. We show that the distributions of V and U are nonparametrically identified just from observing the sum V +U, and provide a rate root n estimator. We apply these results to the world income distribution to measure the extent of convergence over time, where the values V can take on correspond to country types, i.e., wealthy versus poor countries. We also extend our results to include covariates X, showing that we can nonparametrically identify and estimate cross section regression models of the form Y = g(X;D*)+U, where D* is an unobserved binary regressor.</p></p> 
Date:  2009–07 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:16/09&r=ecm 
By:  ChiaLin Chang (Department of Applied Economics, National Chung Hsing University); Philip Hans Franses (Econometric Institute, Erasmus School of Economics); Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo) 
Abstract:  A government's ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is nonreplicable. Governments typically provide nonreplicable forecasts (or, expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate. In this paper, we develop a methodology to evaluate nonreplicable forecasts. We argue that in order to do so, one needs to retrieve from the nonreplicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that it is the undocumented knowledge of the Taiwanese government that reduces forecast errors substantially. 
Date:  2009–08 
URL:  http://d.repec.org/n?u=RePEc:tky:fseres:2009cf637&r=ecm 
By:  Walter Beckert (Institute for Fiscal Studies and Birkbeck College London) 
Abstract:  <p><p>This paper provides a comprehensive econometric framework for the empirical analysis of buyer power. It encompasses the two main features of pricing schemes in businesstobusiness relationships: nonlinear price schedules and bargaining over rents. Disentangling them is critical to the empirical identification of buyer power. Testable predictions from the theoretical analysis are delineated, and a pragmatic empirical methodology is presented. It is readily implementable on the basis of transaction data, routinely collected by antitrust authorities. The empirical framework is illustrated using data from the UK brick industry. The paper emphasizes the importance of controlling for endogeneity of volumes and for heterogeneity across buyers and sellers.</p></p> 
Date:  2009–07 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:17/09&r=ecm 
By:  Christelis, Dimitris (University of Naples, Federico II); SanzdeGaldeano, Anna (Universitat Autònoma de Barcelona) 
Abstract:  We study smoking persistence in ten countries using data from the European Community Household Panel. Such persistence may be due to true state dependence but may also reflect individual unobserved heterogeneity. We distinguish between the two by using semiparametric dynamic panel data methods applied to both the decision to smoke or not and to the decision on the number of cigarettes smoked. Our model allows for correlation of the two timevarying error terms, i.e. for selectivity. We find that for both smoking decisions true state dependence is in general much smaller when unobserved individual heterogeneity is taken into account, and we also uncover large differences in true state dependence across countries. Finally, we find that taking into account heaping in the reported number of cigarettes smoked considerably improves the fit of our model. 
Keywords:  smoking, panel data, selectivity 
JEL:  C33 C34 D12 I10 I12 
Date:  2009–07 
URL:  http://d.repec.org/n?u=RePEc:iza:izadps:dp4336&r=ecm 
By:  Rainer Schnell 
Abstract:  TRecord linkage is used for preparing sampling frames, deduplication of lists and combining information on the same object from two different databases. If the identifiers of the same objects in two different databases have error free unique common identifiers like personal identification numbers (PID), record linkage is a simple file merge operation. If the identifiers contains errors, record linkage is a challenging task. In many applications, the files have widely different numbers of observations, for example a few thousand records of a sample survey and a few million records of an administrative database of social security numbers. Available software, privacy issues and future research topics are discussed. 
Keywords:  RecordLinkage, Datamining, Privacy preserving protocols 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:rsw:rswwps:rswwps84&r=ecm 
By:  Richard Blundell (Institute for Fiscal Studies and University College London); Joel Horowitz (Institute for Fiscal Studies and Northwestern University); Matthias Parey (Institute for Fiscal Studies) 
Abstract:  <p>This paper develops a new method for estimating the demand function for gasoline and the deadweight loss due to an increase in the gasoline tax. The method is also applicable to other goods. The method uses shape restrictions derived from economic theory to improve the precision of a nonparametric estimate of the demand function. Using data from the U.S. National Household Travel Survey, we show that the restrictions are consistent with the data on gasoline demand and remove the anomalous behavior of a standard nonparametric estimator. Our approach provides new insights about the price responsiveness of gasoline demand and the way responses vary across the income distribution. We reject constant elasticity models and find that price responses vary nonmonotonically with income. In particular, we find that low and highincome consumers are less responsive to changes in gasoline prices than are middleincome consumers.</p> 
Date:  2009–05 
URL:  http://d.repec.org/n?u=RePEc:ifs:cemmap:11/09&r=ecm 
By:  Frank A. cowell; Emmanuel Flachaire; Sanghamitra Bandyopadhyay 
Abstract:  Specific functional forms are often used in economic models of distributions; goodnessoffit measures are used to assess whether a functional form is appropriate in the light of realworld data. Standard approaches use a distance criterion based on the EDF, an aggregation of differences in observed and theoretical cumulative frequencies. However, an economic approach to the problem should involve a measure of the information loss from using a badlyfitting model. This would involve an aggregation of, for example, individual income discrepancies between model and data. We provide an axiomatisation of an approach and applications to illustrate its importance. 
Keywords:  Goodness of fit, Discrepancy, Income distribution, Inequality measurement 
JEL:  D63 C10 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:oxf:wpaper:444&r=ecm 