nep-ecm New Economics Papers
on Econometrics
Issue of 2011‒11‒21
sixteen papers chosen by
Sune Karlsson
Orebro University

  1. A Permutation-based Combination of Sign Tests for Assessing Habitat Selection By Lorenzo Fattorini; Caterina Pisani; Francesco Riga; Marco Zaccaroni
  2. Goodness of Fit: an axiomatic approach By Frank A. Cowell; Russell Davidson; Emmanuel Flachaire
  3. Bayesian semiparametric GARCH models By Xibin Zhang; Maxwell L. King
  4. Extending the Hausman Test to Check for the presence of Outliers By Catherine Dehon; Marjorie Gassner; Vincenzo Verardi
  5. Partial Distributional Policy Effects By Rothe, Christoph
  6. Semiparametric Estimation with Generated Covariates By Mammen, Enno; Rothe, Christoph; Schienle, Melanie
  7. Lagged Duration Dependence in Mixed Proportional Hazard Models By Picchio, Matteo
  8. Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome Models, with Applications in Health Economics By John Mullahy
  9. Maximum Likelihood Characterization of Rotationally Symmetric Distributions of the Sphere By Christophe Ley; Mitia Duerinckx
  10. The Leverage Effect Puzzle: Disentangling Sources of Bias at High Frequency By Yacine Ait-Sahalia; Jianqing Fan; Yingying Li
  11. Semi-nonparametric indirect inference. By Blasques, Francisco Albergaria Amaral
  12. Exploring comparative effect heterogeneity with instrumental variables: prehospital intubation and mortality By H. Evans;; A. Basu;
  13. Levy Process Models for High Frequency Financial Data By George Tauchen
  14. On a Connection Between Stein Characterizations and Fisher Information By Christophe Ley; Yves-Caoimhin Swan
  15. From many series, one cycle: improved estimates of the business cycle from a multivariate unobserved components model By Charles A. Fleischman; John M. Roberts
  16. Long-run identifying restrictions on VARs within the AS-AD framework By Jean-Sébastien Pentecôte, University of Rennes 1 - CREM-CNRS

  1. By: Lorenzo Fattorini; Caterina Pisani; Francesco Riga; Marco Zaccaroni
    Abstract: The analysis of habitat use in radio-tagged animals is approached by comparing the portions of use vs the portions of availability observed for each habitat type. Since data are linearly dependent with singular variance-covariance matrices, standard multivariate statistical test cannot be applied. To overcome the problem, compositional data analysis is customary performed via log-ratio transform of sample observations. The procedure is criticized in this paper, emphasizing the many drawbacks which may arise from the use of compositional analysis. An alternative nonparametric solution is proposed in the framework of multiple testing. The habitat use is assessed separately for each habitat type by means of the sign test performed on the original observations. The resulting p-values are combined in an overall test statistic whose significance is determined permuting sample observations. The theoretical findings of the paper are checked by simulation studies. Applications to some case studies are considered.
    Keywords: compositional data analysis, Johnson’s second order selection, Johnson’s third order selection, Monte Carlo studies, multiple testing, random habitat use.
    JEL: C12
    Date: 2011–10
  2. By: Frank A. Cowell (STICERD - London School of Economics); Russell Davidson (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579); Emmanuel Flachaire (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579)
    Abstract: An axiomatic approach is used to develop a one-parameter family of measures of divergence between distributions. These measures can be used to perform goodness-of-fit tests with good statistical properties. Asymptotic theory shows that the test statistics have well-defined limiting distributions which are however analytically intractable. A parametric bootstrap procedure is proposed for implementation of the tests. The procedure is shown to work very well in a set of simulation experiments, and to compare favourably with other commonly used goodness-of-fit tests. By varying the parameter of the statistic, one can obtain information on how the distribution that generated a sample diverges from the target family of distributions when the true distribution does not belong to that family. An empirical application analyses a UK income data set.
    Keywords: Goodness of fit; axiomatic approach; measures of divergence; parametric bootstrap
    Date: 2011–11–08
  3. By: Xibin Zhang; Maxwell L. King
    Abstract: This paper aims to investigate a Bayesian sampling approach to parameter estimation in the semiparametric GARCH model with an unknown conditional error density, which we approximate by a mixture of Gaussian densities centered at individual errors and scaled by a common standard deviation. This mixture density has the form of a kernel density estimator of the errors with its bandwidth being the standard deviation. The proposed investigation is motivated by the lack of robustness in GARCH models with any parametric assumption of the error density for the purpose of error-density based inference such as value-at-risk (VaR) estimation. The contribution of the paper is to construct the likelihood and posterior of model and bandwidth parameters under the proposed mixture error density, and to forecast the one-step out-of-sample density of asset returns. The resulting VaR measure therefore would be distribution-free. Applying the semiparametric GARCH(1,1) model to daily stock-index returns in eight stock markets, we find that this semiparametric GARCH model is favoured against the GARCH(1,1) model with Student t errors for five indices, and that the GARCH model underestimates VaR compared to its semiparametric counterpart. We also investigate the use and benefit of localized bandwidths in the proposed mixture density of the errors.
    Keywords: Bayes factors, kernel-form error density, localized bandwidths, Markov chain Monte Carlo, value-at-risk
    JEL: C11 C14 C15 G15
    Date: 2011–11–03
  4. By: Catherine Dehon; Marjorie Gassner; Vincenzo Verardi
    Abstract: In this paper, we follow the same logic as in Hausman (1978) to create a testing procedure that checks for the presence of outliers by comparing a regression estimator that is robust to outliers (S-estimator), with another that is more e¢ cient but a¤ected by them. Some simulations are presented to illustrate the good behavior of the test for both its size and its power.
    Keywords: S-estimators; MM-estimators; Outliers; Linear regression; Generalized Method of Moments; Robustness
    JEL: C12 C21 H11
    Date: 2011–11
  5. By: Rothe, Christoph (Toulouse School of Economics)
    Abstract: In this paper, we propose a method to evaluate the effect of a counterfactual change in the unconditional distribution of a single covariate on the unconditional distribution of an outcome variable of interest. Both fixed and infinitesimal changes are considered. We show that such effects are point identified under general conditions if the covariate affected by the counterfactual change is continuously distributed, but are typically only partially identified if its distribution is discrete. For the latter case, we derive informative bounds making use of the available information. We also discuss estimation and inference.
    Keywords: counterfactual distribution, partial identification, nonseparable model
    JEL: C14 C31
    Date: 2011–10
  6. By: Mammen, Enno (University of Mannheim); Rothe, Christoph (Toulouse School of Economics); Schienle, Melanie (Humboldt University, Berlin)
    Abstract: In this paper, we study a general class of semiparametric optimization estimators of a vector-valued parameter. The criterion function depends on two types of infinite-dimensional nuisance parameters: a conditional expectation function that has been estimated nonparametrically using generated covariates, and another estimated function that is used to compute the generated covariates in the first place. We study the asymptotic properties of estimators in this class, which is a nonstandard problem due to the presence of generated covariates. We give conditions under which estimators are root-n consistent and asymptotically normal, and derive a general formula for the asymptotic variance.
    Keywords: semiparametric estimation, generated covariates, profiling, propensity score
    JEL: C14 C31
    Date: 2011–10
  7. By: Picchio, Matteo (Tilburg University)
    Abstract: We study the non-parametric identification of a mixed proportional hazard model with lagged duration dependence when data provide multiple outcomes per individual or stratum. We show that the information conveyed by the within strata variation can be exploited to non-parametrically identify lagged duration dependence in more general models than in the literature.
    Keywords: lagged duration dependence, mixed proportional hazard models, identification, multiple spells, parallel data
    JEL: C14 C41
    Date: 2011–10
  8. By: John Mullahy
    Abstract: Estimation of marginal or partial effects of covariates x on various conditional parameters or functionals is often the main target of applied microeconometric analysis. In the specific context of probit models such estimation is straightforward in univariate models, and Greene, 1996, 1998, has extended these results to cover the case of quadrant probability marginal effects in bivariate probit models. The purpose of this paper is to extend these results to the general multivariate probit context for arbitrary orthant probabilities and to demonstrate the applicability of such extensions in contexts of interest in health economics applications. The baseline results are extended to models that condition on subvectors of y, to count data structures that derive from the probability structure of y, to multivariate ordered probit data structures, and to multinomial probit models whose marginal effects turn out to be a special case of those of the multivariate probit model. Simulations reveal that analytical formulae versus fully numerical derivatives result in a reduction in computational time as well as an increase in accuracy.
    JEL: C35 I1
    Date: 2011–11
  9. By: Christophe Ley; Mitia Duerinckx
    Abstract: A classical characterization result, which can be traced back to Gauss, states that the maximum likelihood estimator (MLE) of the location parameter equals the sample mean for any possible univariate samples of any possible sizes n if and only if the samples are drawn from a Gaussian population. A similar result, in the two-dimensional case, is given in von Mises (1929) for the Fisher-von Mises-Langevin (FVML) distribution, the equivalent of the Gaussian law on the unit circle. Half a century later, Bingham and Mardia (1975) extend the result to FVML distributions on the unit sphere S<sup>k-1</sup> := fv 2 R<sup>k</sup> :v'v = 1g, k >= 2. In this paper, we present a general MLE characterization theorem for a large subclass of rotationally symmetric distributions on S<sup>k-1</sup>, k >= 2, including the FVML distribution.
    Keywords: Cauchy's finctional equation; characterization theorem; Fisher-von Mises-Langevin distribution; Maximum likelihood estimator; Rationally symmetric distributions on the sphere
    Date: 2011–11
  10. By: Yacine Ait-Sahalia; Jianqing Fan; Yingying Li
    Abstract: The leverage effect refers to the generally negative correlation between an asset return and its changes of volatility. A natural estimate consists in using the empirical correlation between the daily returns and the changes of daily volatility estimated from high-frequency data. The puzzle lies in the fact that such an intuitively natural estimate yields nearly zero correlation for most assets tested, despite the many economic reasons for expecting the estimated correlation to be negative. To better understand the sources of the puzzle, we analyze the different asymptotic biases that are involved in high frequency estimation of the leverage effect, including biases due to discretization errors, to smoothing errors in estimating spot volatilities, to estimation error, and to market microstructure noise. This decomposition enables us to propose novel bias correction methods for estimating the leverage effect.
    JEL: C22 G12
    Date: 2011–11
  11. By: Blasques, Francisco Albergaria Amaral (Maastricht University)
    Date: 2011
  12. By: H. Evans;; A. Basu;
    Abstract: We highlight the role of local instrumental variable (LIV) methods in exploring treatment effect heterogeneity using an empirical example of evaluating the use versus non-use of prehospital intubation (PHI) in patients with traumatic injury on inpatient mortality. We find evidence that the effect of PHI on inpatient mortality varies over levels of unobserved confounders giving rise to a phenomenon known as essential heterogeneity. Under essential heterogeneity, the traditional instrumental variable (IV) method, when using a continuous IV, estimates an effect that is an arbitrary weighted average of the casual effects for marginal groups of patients whose PHI receipt are directly influenced by the IV levels. Instead, the LIV methods estimate the distribution of treatment effects for every margin that is identified by data and allow for predictable aggregation to recover estimates for meaningful treatment effect parameters such as the Average Treatment Effect (ATE) and the Effect on the Treated (TT). LIV methods also allow exploring heterogeneity in treatment effects over levels of observed confounders. In the PHI analysis, we estimate an ATE of 0.074. We find strong evidence of positive self-selection in practice based on observed and unobserved characteristics, whereby patients who were most likely to be harmed by PHI were also less likely to receive PHI. However, the degree of positive self-selection mitigates in regions with higher rates of PHI use. We also explore factors associated with the prediction of significant harm by PHI. We provide clinical interpretation of results and discuss the importance of these methods in the context of comparative effectiveness research.
    Keywords: Instrumental variables; local IV methods; heterogeneity; prehospital intubation; mortality
    Date: 2011–08
  13. By: George Tauchen
    Abstract: In this paper we present parametric estimation of models for stock returns by describing price dynamic as the sum of two independent Levy components. The increments (moves) are viewed as discrete-time log price changes that follow an infinitely divisible distribution, i.e. stationary and independent price changes (zero drift) that follow a Levy-type distribution. We explore empirical plausibility of two parametric models: Jump-Diffusion (C-J) and pure jump model (TS-J). The first process describes dynamics of small frequent moves and is modeled by Brownian motion in C-J model and by tempered stable Levy process in TS-J model. The second process is responsible for big rare moves in asset prices and is modeled by compound Poisson process in both models. The estimation is performed via continuously updated GMM by matching the characteristic function implied by the model with the observed characteristic function. Using high frequency data on 13 stocks of different market capitalization for 2006-2008 sample period we find that C-J model performs well only for large cap stocks, while medium cap stock dynamics are captured by TS-J model. We also report evidence of positive relation between activity index of the process for stock returns and its frequency of trading.
    JEL: C51 C52 G12
    Date: 2011
  14. By: Christophe Ley; Yves-Caoimhin Swan
    Abstract: Abstract: We generalize the so-called density approach to Stein character- izations of probability distributions. We prove an elementary factorization property of the resulting Stein operator in terms of a generalized (standard- ized) score function. We use this result to connect Stein characterizations with information distances such as the generalized (standardized) Fisher information.
    Keywords: density approach; generalized (standardized) Fisher information; generalized (standardized) score functions; information functionals; probability metrics
    Date: 2011–11
  15. By: Charles A. Fleischman; John M. Roberts
    Abstract: We construct new estimates of potential output and the output gap using a multivariate approach that allows for an explicit role for measurement errors in the decomposition of real output. Because we include data on hours, output, employment, and the labor force, we are able to decompose our estimate of potential output into separate trends in labor productivity, labor-force participation, weekly hours, and the NAIRU. We find that labor-market variables—especially the unemployment rate—are the most informative individual indicators of the state of the business cycle. Conditional on including these measures, inflation is also very informative. Among measures of output, we find that although they add little to the identification for the cycle, the income-side measures of output are about as informative as the traditional product-side measures about the level of structural productivity and potential output. We also find that the output gap resulting from the recent financial crisis was very large, reaching -7 percent of output in the second half of 2009.
    Date: 2011
  16. By: Jean-Sébastien Pentecôte, University of Rennes 1 - CREM-CNRS
    Abstract: Bayoumi and Eichengreen’s (BE, 1994) article has been very influent in the empirics of the core-periphery view of fixed exchange rate agreements. They rely on the basic AS-AD macroeconomic model in order to identify supply and demand shocks through long-run restrictions in vector autoregressions. Doing this should enable one to assess the size of such disturbances and the asymmetry between countries. While reference is usually made to Blanchard and Quah (BQ, 1989), it is shown here how this factorization has been modified by BE and how the two resulting decomposition schemes can be linked. Contrary to BE’s premise, relaxing the assumption of shocks of equal size is not just a matter of scale. The empirical properties of the exchange regime are modified, especially as regards the correlation of shocks. Given the VAR setting used in the related studies, it is also established that zero-constraints on either instantaneous or long-run impulse responses provide identical results. An empirical assessment he euro currency area over 1996-2008 illustrate these points. The recorded evidence suggests that non-zero restrictions imply slope coefficients of the AS and AD curves close to values derived from New-Keynesian models.
    Keywords: Fixed exchange rates, core-periphery, long-run restrictions, structural VARs
    JEL: C32 E13 F33
    Date: 2011–11

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