
on Econometrics 
By:  Victor Aguirregabiria; Arvind Magesan 
Abstract:  This paper extends the Euler Equation (EE) representation of dynamic decision problems to a general class of discrete choice models and shows that the advantages of this approach apply not only to the estimation of structural parameters but also to the computation of a solution and to the evaluation of counterfactual experiments. We use a choice probabilities representation of the discrete decision problem to derive marginal conditions of optimality with the same features as the standard EEs in continuous decision problems. These EEs imply a fixed point mapping in the space of conditional choice values, that we denote the Euler equationvalue (EEvalue) operator. We show that, in contrast to Euler equation operators in continuous decision models, this operator is a contraction. We present numerical examples that illustrate how solving the model by iterating in the EEvalue mapping implies substantial computational savings relative to iterating in the Bellman equation (that requires a much larger number of iterations) or in the policy function (that involves a costly valuation step). We define a sample version of the EEvalue operator and use it to construct a sequence of consistent estimators of the structural parameters, and to evaluate counterfactual experiments. The computational cost of evaluating this samplebased EEvalue operator increases linearly with sample size, and provides an unbiased (in finite samples) and consistent estimator the counterfactual. As such there is no curse of dimensionality in the consistent estimation of the model and in the evaluation of counterfactual experiments. We illustrate the computational gains of our methods using several Monte Carlo experiments. 
Keywords:  Dynamic programming discrete choice models; Euler equations; Policy iteration; Estimation; Approximation bias 
JEL:  C13 C35 C51 C61 
Date:  2016–05–24 
URL:  http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa562&r=ecm 
By:  Rosas, Francisco; Lence, Sergio H. 
Abstract:  The Neoclassical theory of production establishes a dual relationship between the profit value function of a competitive firm and its underlying production technology. This relationship, commonly referred to as duality theory, has been widely used in empirical work to estimate production parameters, such as elasticities and returns to scale, without the requirement of explicitly specifying the technology. We generate a pseudodataset by Monte Carlo simulations, which starting from known production parameters, yield a dataset with the main characteristics of U.S. agriculture in terms of unobserved firm heterogeneity, decisions under uncertainty, unexpected production and price shocks, endogenous prices, output and input aggregation, measurement error in variables, and omitted variables. Econometric estimation conducted with the mentioned pseudodata show that the initial production parameters are not precisely recovered and therefore the elasticities are inaccurately estimated. The deviation of the own and crossprice elasticities from their true values, given our parameter calibration, ranges between 6% and 229%, with an average of 71%. Also, ownprice elasticities are as imprecisely recovered as crossprice elasticities. Sensitivity analysis shows that results still hold for different sources and levels of noise, as well as sample size used in estimation. 
Keywords:  Duality theory, firm’s heterogeneity, measurement error, data aggregation, omitted variables, endogeneity, uncertainty, Monte Carlo simulations., Demand and Price Analysis, Production Economics, Q12, D22, D81, C18, 
Date:  2016–05 
URL:  http://d.repec.org/n?u=RePEc:ags:aaea16:236091&r=ecm 
By:  Tue Gorgens; Chirok Han; Sen Xue 
Abstract:  This paper establishes asymptotic distributions of the quadratic GMM estimator of the autoregressive parameter in simple linear dynamic panel data models with fixed effects under standard minimal assumptions. The number of time periods is assumed to be small. Focusing on settings where autoregressive parameter is uniquely identified, nonstandard convergence rates and limiting distributions arise in the wellknown random walk case, as well as in other previously unrecognized cases. The paper finds that the convergence rates are slow in the nonstandard cases, and the limiting distributions are a mixture of two nonnormal distributions. The findings are illustrated using Monte Carlo simulations. 
Keywords:  Dynamic panel data models, fixed effects, generalized method of moments, nonstandard limiting distributions 
JEL:  C23 
Date:  2016–05 
URL:  http://d.repec.org/n?u=RePEc:acb:cbeeco:2016635&r=ecm 
By:  Ryoko Ito 
Abstract:  We develop the splineDCS model and apply it to trade volume prediction, which remains a highly nontrivial task in highfrequency finance. Our application illustrates that the splineDCS is computationally practical and captures salient empirical features of the data such as the heavytailed distribution and intraday periodicity very well. We produce density forecasts of volume and compare the model's predictive performance with that of the stateoftheart volume forecasting model, named the componentMEM, of Brownlees et al. (2011). The splineDCS significantly outperforms the componentMEM in predicting intraday volume proportions. 
Keywords:  order slicing, price impact, robustness, score, VWAP trading 
JEL:  C22 C51 C53 C58 G12 
Date:  2016–01–24 
URL:  http://d.repec.org/n?u=RePEc:cam:camdae:1606&r=ecm 
By:  Bouscasse, H.; Joly, I.; Peyhardi, J. 
Abstract:  In this paper, we model mode choice with the new specification of generalized linear models proposed by Peyardi et al. (2015). In logit models used by economists, the link function can be decomposed into the reference ratio of probabilities and a cumulative distribution function (cdf). Alternative cdfs (Student, Cauchy, Gumbel, Gompertz, Laplace, Normal) can be used. These cdfs differ in their symmetry (symetric or asymetric distributions) and in their tails (heavy or thin tails), each allowing a different distribution of behaviors. We test the statistic and economic implications of changing the cdf. First, we investigate the goodnessoffit indicators (AIC, BIC, McFadden R2). Then, we compare estimated parameters in terms of sign and significativity. And finally, we look at behavioural outputs such as value of time and demand elasticities. We use a recent stated preferences survey conducted by the author in the RhôneAlpes Région (France). Its specificity is to specifically address the question of mode choice (rail, coach and car) in a regional context. Attributes include travel time, cost and comfort. We also investigate the cross effect of travel time and comfort. Comparisons between cdfs are made on the basis of three models, including only attributes variables or only individual variables or both. Our results show that the different cdfs provide quite similar results. But, in our estimations, the logistic cdf never ranks among the best options. In terms of significance and sign of coefficients, parameters' estimation are globally the same even if some special features can be noticed. Looking at time equivalence of comfort, we notice that in the model without individual variables, the cdf has a major influence on outputs. In particular, the Student cdf provides very consistent results while some other cdfs (e.g. Gompertz, Logistic, Normal) are extreme. 
Keywords:  LOGIT MODEL;REFERENCE MODELS;VALUE OF TIME;ELASTICITIES;MODE CHOICE 
JEL:  C18 C52 R40 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:gbl:wpaper:201604&r=ecm 
By:  Lesage, James P.; Vance, Colin; Chih, YaoYu 
Abstract:  We apply a heterogenous coefficient spatial autoregressive panel model from Aquaro, Bailey and Pesaran (2015) to explore competition/cooperation by Berlin fueling stations in setting prices for diesel and E5 fuel. Unlike the maximum likelihood estimation method set forth by Aquaro, Bailey and Pesaran (2015), we rely on a Markov Chain Monte Carlo (MCMC) estimation methodology. MCMC estimates as applied here with noninformative priors will produce estimates equal to those from maximum likelihood, a point we demonstrate with a Monte Carlo experiment. We explore stationlevel price markups using over 400 fueling stations located in and around Berlin, average daily diesel and E5 fuel prices, and refinery cost information covering more than 487 days. The heterogeneous coefficients spatial autoregressive panel data model uses the large sample of daily time periods to produce spatial autoregressive model estimates for each fueling station. These estimates provide information regarding the price reaction function of each station to neighboring stations. This is in contrast to conventional estimates of price reaction functions that average over the entire crosssectional sample of stations. We show how these estimates can be used to infer competition versus cooperation in price setting by individual stations. The empirical results reveal a mix of competitive and collusive price setting, with some evidence that stations located near others of the same brand tend toward collusion, while those located near rival brands tend toward competition. 
Abstract:  Wir nutzen ein räumliches autoregressives PanelDatenmodell von Aquaro, Bailey und Pesaran (2015), um Wettbewerbs bzw. Kooperationsverhalten bei der Preissetzung für Diesel und E5Benzin von Berliner Tankstellen zu untersuchen. Es wird das MarkovChainMonteCarloVerfahren (MCMCVerfahren) angewandt, welches in diesem Zusammenhang die gleichen Schätzungen wie die MaximumLikelihoodMethode von Aquaro, Bailey und Pesaran (2015) liefert. Wir nutzen Informationen über mehr als 400 Tankstellen in und um Berlin, Tagesdurchschnittspreise für Diesel und E5 und Raffineriekosten von mehr als 487 Tagen, um Preisaufschläge zu untersuchen. Das angewandte Modell schätzt die Preisreaktionsfunktion  anders als übliche Schätzungen  jeder Tankstelle auf umliegende Tankstellen. Wir zeigen, wie mit diesen Schätzungen auf das das Wettbewerbs/ Kooperationsverhalten bei der Preissetzung einzelner Tankstellen geschlossen werden kann. Die empirischen Ergebnisse zeigen eine Mischung aus wettbewerblicher und kooperativer Preissetzung mit Evidenz, dass Tankstellen mit Tankstellen derselben Marke in der Nähe zu Kooperation tendieren und Tankstellen mit Tankstellen konkurrierender Marken in der Umgebung zu Wettbewerb tendieren. 
Keywords:  spatial panel data models,Markov Chain Monte Carlo,spatial autoregressive model,observationlevel spatial interaction 
JEL:  C11 C23 L11 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:zbw:rwirep:617&r=ecm 
By:  Giorgia Marini (UniversitaÌ€ Sapienza di Roma  Dipartimento di Studi Giuridici, Filosofici ed Economici) 
Abstract:  This paper enlarges on Gutierrez's (2003) results on the power of panel cointegration tests. By a comparison of power of panel cointegration tests, we show how the choice of most powerful test depends on the values of the sample statistics. Countrybycountry and panel stationarity and cointegration tests are performed on a panel of 20 OECD countries over the period 19712004. Residualbased tests and a cointegration rank test in the system of health care expenditure and GDP are used to test cointegration. Asymptotic normal distribution of these tests allows a straightforward comparison: for some values of the sample statistics, residualbased and rank tests are not directly comparable as the power of the residualbased tests oscillates; for other values of the sample statistics, the rank test is more powerful than the residualbased tests. This suggests that a clearcut conclusion on the most powerful test cannot be reached a priori. 
Keywords:  Panel data, panel stationarity tests, panel cointegration tests, power of tests 
JEL:  C12 C22 C23 I10 
Date:  2016–05 
URL:  http://d.repec.org/n?u=RePEc:gfe:pfrp00:00021&r=ecm 
By:  Søren Johansen (University of Copenhagen and CREATES); Bent Nielsen (Nuffield College & Department of Economics, University of Oxford & Institute for New Economic Thinking at the Oxford Martin School) 
Abstract:  We show tightness of a general Mestimator for multiple linear regression in time series. The positive criterion function for the Mestimator is assumed lower semicontinuous and sufficiently large for large argument: Particular cases are the Huberskip and quantile regression. Tightness requires an assumption on the frequency of small regressors. We show that this is satisfied for a variety of deterministic and stochastic regressors, including stationary an random walks regressors. The results are obtained using a detailed analysis of the condition on the regressors combined with some recent martingale results. 
Keywords:  Mestimator, robust statistics, martingales, Huberskip, quantile estimation. 
JEL:  C22 
Date:  2016–05–28 
URL:  http://d.repec.org/n?u=RePEc:aah:create:201618&r=ecm 
By:  Palma, Marco; Li, Yajuan; Vedenov, Dmitry; Bessler, David 
Abstract:  The simulated choice probabilities in Mixed Logit are approximated numerically from a multidimensional integral with a mixing distribution from a multivariate density function of the random parameters. Theoretically the order in which the variables are estimated should not matter; however, due to the inherent simulation ‘noise’ the magnitude of the estimated coefficients differs depending on the arbitrarily selected order in which the random variables enter the estimation procedure. This problem is exacerbated with a low number of draws or if correlation among coefficients is allowed. If correlation among the random parameters is allowed the variable ordering effects arise from simulation noise and from the Cholesky factorization used to allow for correlation. Ignoring the potential ordering effects in simulated maximum likelihood estimation methods seriously compromises the ability for replicating the results and can inadvertently influence policy recommendations. The simulation noise is independent of the number of integrating dimensions for random draws, but it increases for Halton draws. Hence, better coverage is achieved with Halton draws for small integrating dimensions, but random draws provide better coverage for larger dimensions. 
Keywords:  Cholesky, Halton draws, Mixing distribution, Random draws, Random Parameters, Simulation., Research Methods/ Statistical Methods, C25, C63, 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:ags:aaea16:235990&r=ecm 
By:  Abi Morshed, Alaa (Tilburg University, Center For Economic Research); Andreou, E. (Tilburg University, Center For Economic Research); Boldea, Otilia (Tilburg University, Center For Economic Research) 
Abstract:  Structural break tests developed in the literature for regression models are sensitive to model misspecification. We show  analytically and through simulations  that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the conditional mean dynamics are misspecified. We also show that the sup Wald test for breaks in the unconditional mean and variance does not have the same size distortions, yet benefits from similar power to its conditional counterpart. Hence, we propose using it as an alternative and complementary test for breaks. While the conditional tests based on dynamic regression models detect breaks in the mean and variance of the US unemployment growth and interest rate growth series around the Great Moderation, the evidence for these breaks disappears when using the unconditional tests. Therefore, there is no evidence of longrun mean or volatility shifts in unemployment growth and interest rate growth. 
Keywords:  structural change; sup Wald test; dynamic misspecification 
JEL:  C01 C12 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:tiu:tiucen:3b21f21c2cef49d7bb9bad81f5ee71f2&r=ecm 
By:  Bryan S. Graham 
Abstract:  In social and economic networks linked agents often share additional links in common. There are two competing explanations for this phenomenon. First, agents may have a structural taste for transitive links  the returns to linking may be higher if two agents share links in common. Second, agents may assortatively match on unobserved attributes, a process called homophily. I study parameter identifiability in a simple model of dynamic network formation with both effects. Agents form, maintain, and sever links over time in order to maximize utility. The return to linking may be higher if agents share friends in common. A pairspecific utility component allows for arbitrary homophily on timeinvariant agent attributes. I derive conditions under which it is possible to detect the presence of a taste for transitivity in the presence of assortative matching on unobservables. I leave the joint distribution of the initial network and the pairspecific utility component, a very high dimensional object, unrestricted. The analysis is of the `fixed effects' type. The identification result is constructive, suggesting an analog estimator, whose single large network properties I characterize. 
JEL:  C1 C14 C23 C25 D85 
Date:  2016–04 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:22186&r=ecm 
By:  Davis, Katrina J; Burton, Michael; Kragt, Marit E 
Abstract:  Models to analyse discrete choice data that account for heterogeneity in error variance (scale) across respondents are increasingly common, e.g. heteroscedastic conditional logit or scale adjusted latent class models. In this paper we do not question the need to allow for scale heterogeneity. Rather, we examine the interpretation of results from these models. We provide five empirical examples using discrete choice experiments, analysed using conditional logit, heteroscedastic conditional logit, or scale adjusted latent class models. We show that analysts may incorrectly conclude that preferences are consistent across respondents even if they are not, or that classes of respondents may have (in)significant preferences for some or all attributes of the experiment, when they do not. We recommend that future studies employing scale heterogeneity models explicitly state scale factors for all samples, choice contexts, and/or latent scale classes, and report rescaled preference parameters for each of these groups. 
Keywords:  Discrete choice experiments, Heteroscedastic conditional logit models, Scale adjusted latent class models, Interpretation of preferences, Bestpractice reporting, Research Methods/ Statistical Methods, C10, C18, C51, Q51, 
Date:  2016–05–14 
URL:  http://d.repec.org/n?u=RePEc:ags:uwauwp:235373&r=ecm 
By:  Fève, Patrick; Guay, Alain 
Abstract:  This paper investigates the contribution of sentiments shocks to US fluctuations in a Structural VAR setup with long, medium and short run restrictions. Sentiments shocks are identified as shocks orthogonal to fundamentals that accounts for most of the variance of confidence. We assess our identification procedure from simulation experiments and show that it performs pretty well. From actual data, we obtain that, contrary to news shocks on total factor productivity, sentiments shocks explain very little of quantities and prices. Sentiments shocks mostly appear as an idiosyncratic component of confidence. These results are robust to various perturbations of the benchmark model. 
Keywords:  Sentiment Shocks, News Shocks, SVARs, Identifying Restrictions 
JEL:  C32 E32 
Date:  2016–05 
URL:  http://d.repec.org/n?u=RePEc:tse:wpaper:30484&r=ecm 
By:  Barfield, Ashley; Shonkwiler, J. Scott 
Abstract:  Revealed preference methods require survey data on past resource use, and numerous studies have found reported recreation frequency to be overestimated and concentrated on prototype (rounded and calendarbased) values. This paper develops an approach to treat extreme values and rounded responses in survey datasets and thereby improve model fit and resulting welfare estimates. We illustrate how, when modeling singlesite trip data, model fit can be improved by transitioning from a discrete to a continuous distribution at a cutpoint where response behavior begins to exhibit rounding. We feel this method will be useful for recreation demand research and may have broad applicability to the general analysis of count data. 
Keywords:  extreme responses, negative binomial distribution, recall bias, recreation, rounding, Environmental Economics and Policy, Research Methods/ Statistical Methods, 
Date:  2016–05 
URL:  http://d.repec.org/n?u=RePEc:ags:aaea16:235670&r=ecm 
By:  Shr, YauHuo; Ready, Richard 
Keywords:  Discrete choice analysis, Nonmarket Valuation, Environmental Economics and Policy, C52, 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:ags:aaea16:236174&r=ecm 
By:  Xu, Ning; Hong, Jian; Fisher, Timothy 
Abstract:  Model selection is difficult to analyse yet theoretically and empirically important, especially for highdimensional data analysis. Recently the least absolute shrinkage and selection operator (Lasso) has been applied in the statistical and econometric literature. Consis tency of Lasso has been established under various conditions, some of which are difficult to verify in practice. In this paper, we study model selection from the perspective of generalization ability, under the framework of structural risk minimization (SRM) and VapnikChervonenkis (VC) theory. The approach emphasizes the balance between the insample and outofsample fit, which can be achieved by using crossvalidation to select a penalty on model complexity. We show that an exact relationship exists between the generalization ability of a model and model selection consistency. By implementing SRM and the VC inequality, we show that Lasso is L2consistent for model selection under assumptions similar to those imposed on OLS. Furthermore, we derive a probabilistic bound for the distance between the penalized extremum estimator and the extremum estimator without penalty, which is dominated by overfitting. We also propose a new measurement of overfitting, GR2, based on generalization ability, that converges to zero if model selection is consistent. Using simulations, we demonstrate that the proposed CVLasso algorithm performs well in terms of model selection and overfitting control. 
Keywords:  Model selection, VC theory, generalization ability, Lasso, highdimensional data, structural risk minimization, cross validation. 
JEL:  C13 C52 C55 
Date:  2016–04–22 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:71670&r=ecm 
By:  Heckman, James J. (University of Chicago); Humphries, John Eric (University of Chicago); Veramendi, Gregory (Arizona State University) 
Abstract:  This paper estimates returns to education using a dynamic model of educational choice that synthesizes approaches in the structural dynamic discrete choice literature with approaches used in the reduced form treatment effect literature. It is an empirically robust middle ground between the two approaches which estimates economically interpretable and policyrelevant dynamic treatment effects that account for heterogeneity in cognitive and noncognitive skills and the continuation values of educational choices. Graduating college is not a wise choice for all. Ability bias is a major component of observed educational differentials. For some, there are substantial causal effects of education at all stages of schooling. 
Keywords:  education, earnings, health, rates of return, causal effects of education, cognitive skills, noncognitive skills 
JEL:  C32 C38 I12 I14 I21 
Date:  2016–05 
URL:  http://d.repec.org/n?u=RePEc:iza:izadps:dp9957&r=ecm 
By:  Gadat, Sébastien; Marteau, Clément; Maugis, Cathy 
Date:  2016–05 
URL:  http://d.repec.org/n?u=RePEc:tse:wpaper:30481&r=ecm 