nep-ecm New Economics Papers
on Econometrics
Issue of 2015‒02‒11
fifteen papers chosen by
Sune Karlsson
Örebro universitet

  1. GMM Estimation of Fixed Effects Dynamic Panel Data Models with Spatial Lag and Spatial Errors (Revised version of CentER DP 2011-134) By Cizek, P.; Jacobs, J.; Ligthart, J.E.; Vrijburg, H.
  2. The Finite Sample Performance of Semi- and Nonparametric Estimators for Treatment Effects and Policy Evaluation By Frölich, Markus; Huber, Martin; Wiesenfarth, Manuel
  3. Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches By Catalina Bolancé; Zuhair Bahraoui; Ramon Alemany
  4. Optimal Pseudo-Gaussian and Rank-based Tests of the Cointegration Rank in Semiparametric Error-correction Models By Hallin, M.; Werker, B.J.M.; van den Akker, R.
  5. Matching a distribution by matching quantiles estimation By Nikolaos Sgouropoulos; Qiwei Yao; Claudia Yastremiz
  6. Through the Looking Glass: Indirect Inference via Simple Equilibria By Calvet , Laurent; Czellar, Veronika
  7. The use of accuracy indicators to correct for survey measurement error By Damião Nóbrega Da Silva; Chris J. Skinner
  8. Monge-Kantorovich Depth, Quantiles, Ranks and Signs By Victor Chernozhukov; Alfred Galichon; Marc Hallin; Marc Henry
  9. Tests for Normality in Linear Panel Data Models By Javier Alejo; Antonio Galvao; Gabriel Montes-Rojas; Walter Sosa-Escudero
  10. Estimating parameters and structural change in CGE models using a Bayesian cross-entropy estimation approach By Go, Delfin S.; Lofgren, Hans; Mendez Ramos, Fabian; Robinson, Sherman
  11. Updating the option implied probability of default methodology By Vilsmeier, Johannes
  12. Archimedean-based Marshall-Olkin Distributions and Related Copula Functions By Sabrina Mulinacci
  13. A uniform law for convergence to the local times of linear fractional stable motions By James Duffy
  14. Best estimate reporting with asymmetric loss By Lillestøl, Jostein; Sinding-Larsen, Richard
  15. Asymptotics for parametric GARCH-in-Mean Models By Conrad, Christian; Mammen , Enno

  1. By: Cizek, P. (Tilburg University, Center For Economic Research); Jacobs, J.; Ligthart, J.E. (Tilburg University, Center For Economic Research); Vrijburg, H.
    Abstract: The three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian and Prucha (2007), which corrects for spatially correlated errors in static panel data models, is extended by introducing fixed effects, a spatial lag, and a one-period lag of the dependent variable as additional explanatory variables. Combining this approach with the dynamic panel-data GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and specifying moment conditions for various time lags, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. The proposed spatially corrected GMM estimates are based on a spatial lag and a transformation correcting for the spatial error correlation. We prove their consistency and asymptotic normality for a large number of spatial units and a fixed number of time periods. Feasible spatial correction based on estimated spatial error correlation is shown to lead to estimators that are asymptotically equivalent to the infeasible estimators based on a known spatial error<br/>correlation. Monte Carlo simulations show that the root mean squared error of spatially corrected GMM estimates is generally smaller than that of corresponding spatial GMM estimates in which spatial error correlation is ignored.
    Keywords: Dynamic panel models,; spatial lag,; spatial error,; GMM estimation
    JEL: C15 C21 C22 C23
    Date: 2015
  2. By: Frölich, Markus (University of Mannheim); Huber, Martin (University of Fribourg); Wiesenfarth, Manuel (German Cancer Research Center)
    Abstract: This paper investigates the finite sample performance of a comprehensive set of semi- and nonparametric estimators for treatment and policy evaluation. In contrast to previous simulation studies which mostly considered semiparametric approaches relying on parametric propensity score estimation, we also consider more flexible approaches based on semi- or nonparametric propensity scores, nonparametric regression, and direct covariate matching. In addition to (pair, radius, and kernel) matching, inverse probability weighting, regression, and doubly robust estimation, our studies also cover recently proposed estimators such as genetic matching, entropy balancing, and empirical likelihood estimation. We vary a range of features (sample size, selection into treatment, effect heterogeneity, and correct/misspecification) in our simulations and find that several nonparametric estimators by and large outperform commonly used treatment estimators using a parametric propensity score. Nonparametric regression, nonparametric doubly robust estimation, nonparametric IPW, and one-to-many covariate matching perform best.
    Keywords: treatment effects, policy evaluation, simulation, empirical Monte Carlo study, propensity score, semi- and nonparametric estimation
    JEL: C21
    Date: 2015–01
  3. By: Catalina Bolancé (Riskcenter-IREA, Department of Econometrics. University of Barcelona); Zuhair Bahraoui (Riskcenter-IREA, Department of Econometrics. University of Barcelona); Ramon Alemany (Riskcenter-IREA, Department of Econometrics. University of Barcelona)
    Abstract: We propose a new kernel estimation of the cumulative distribution function based on transformation and on bias reducing techniques. We derive the optimal bandwidth that minimises the asymptotic integrated mean squared error. The simulation results show that our proposed kernel estimation improves alternative approaches when the variable has an extreme value distribution with heavy tail and the sample size is small.
    Keywords: Transformed kernel estimation, cumulative distribution function, extreme value distribution.
    Date: 2015–01
  4. By: Hallin, M.; Werker, B.J.M. (Tilburg University, Center For Economic Research); van den Akker, R. (Tilburg University, Center For Economic Research)
    Abstract: This paper provides locally optimal pseudo-Gaussian and rank-based tests for the cointegration rank in linear cointegrated error-correction models with i.i.d. elliptical innovations. The proposed tests are asymptotically distribution-free, hence their validity does not depend on the actual distribution of the innovations. The proposed rank-based tests depend on the choice of scores, associated with a reference density that can freely be chosen. Under appropriate choices they are achieving the semiparametric efficiency bounds; when based on Gaussian scores, they moreover uniformly dominate their pseudo-Gaussian counterparts. Simulations show that the asymptotic analysis provides an accurate approximation to finite-sample behavior. The theoretical results are based on a complete picture of the asymptotic statistical structure of the model under consideration.
    Keywords: Cointegration model; Cointegration rank; Elliptical densities; erro-correction model; Lagrange multiplier test; Local Asymptotic Brownian Functional; Local Asymptotic Mixed Normality; Local Asymptotic Normality; Multivariate ranks; quasi-likelihood procedures
    JEL: C14 C32
    Date: 2015
  5. By: Nikolaos Sgouropoulos; Qiwei Yao; Claudia Yastremiz
    Abstract: Motivated by the problem of selecting representative portfolios for backtesting counterparty credit risks, we propose a matching quantiles estimation (MQE) method for matching a target distribution by that of a linear combination of a set of random variables. An iterative procedure based on the ordinary least squares estimation (OLS) is proposed to compute MQE. MQE can be easily modified by adding a LASSO penalty term if a sparse representation is desired, or by restricting the matching within certain range of quantiles to match a part of the target distribution. The convergence of the algorithm and the asymptotic properties of the estimation, both with or without LASSO, are established. A measure and an associated statistical test are proposed to assess the goodness-of-match. The finite sample properties are illustrated by simulation. An application in selecting a counterparty representative portfolio with a real data set is reported. The proposed MQE also finds applications in portfolio tracking, which demonstrates the usefulness of combining MQE with LASSO.
    Keywords: goodness\-of\-match; LASSO; ordinary least squares estimation; portfolio tracking; representative portfolio; sample quantile
    JEL: C1 E6
    Date: 2014–05–25
  6. By: Calvet , Laurent; Czellar, Veronika
    Abstract: This paper proposes an indirect inference (Gourieroux, Monfort and Renault, 1993; Smith, 1993) estimation method for a large class of dynamic equilibrium models. The authors' approach is based on the observation that the econometric structure of these systems naturally generates auxiliary equilibria that can serve as building blocks for estimation. They use this insight to develop an accurate estimator for the long-run risk model of Bansal and Yaron (2004). The authors demonstrate the accuracy of our method by Monte Carlo simulation and estimate the long-run risk model on U.S. data. They also illustrate the good performance of the methodology on an asset pricing model with investor learning.
    Keywords: Hidden Markov model; long-run risk; learning; value at risk; indirect inference; particle filters
    JEL: C01 C13 C15 C53 C58
    Date: 2013–11–10
  7. By: Damião Nóbrega Da Silva; Chris J. Skinner
    Abstract: An accuracy indicator is an observed variable which is related to the size of measurement error. Basic and extended models are introduced to represent the properties of a binary accuracy indicator. Under specified assumptions, it is shown that an accuracy indicator can identify a measurement error model. An approach to estimating a distribution function is presented together with methodology for variance estimation. The approach is applied to data on earnings from the British Household Panel Survey, where the accuracy indicator is whether or not a payslip is observed. A validation study provides justification for the modelling assumptions
    Keywords: accuracy indicator; finite population distribution function; measurement error; pseudo-maximum-likelihood
    JEL: C1
    Date: 2014–02–05
  8. By: Victor Chernozhukov; Alfred Galichon; Marc Hallin; Marc Henry
    Abstract: We propose new concepts of statistical depth, multivariate quantiles,ranks and signs, based on canonical transportation maps between a distributionof interest on IRd and a reference distribution on the d-dimensionalunit ball. The new depth concept, called Monge-Kantorovich depth, specializesto halfspace depth in the case of elliptical distributions, but, for more generaldistributions, differs from the latter in the ability for its contours to account fornon convex features of the distribution of interest. We propose empirical counterpartsto the population versions of those Monge-Kantorovich depth contours,quantiles, ranks and signs, and show their consistency by establishing a uniformconvergence property for transport maps, which is of independent interest.
    Keywords: statictical depth; vector quantiles; vector ranks; multivariate signs; optimal transport maps
    Date: 2015–01
  9. By: Javier Alejo (CONICET-CEDLAS-UNLP); Antonio Galvao (University of Iowa); Gabriel Montes-Rojas (CONICET-Universidad de San Andres); Walter Sosa-Escudero (Universidad de San Andres-CONICET)
    Abstract: A new Stata command, xtsktest, is proposed to explore non-normalities in linear panel data models. The tests explore skewness and excess kurtosis allowing researchers to identify departures away from gaussianity in both error components of a standard panel regression, sepa- rately or jointly. The tests are based on recent results by Galvao, Montes- Rojas, Sosa-Escudero and Wang (2013), and can be seen as extending the classical Bera-Jarque normality test for the case of panel data.
    JEL: J08 J24 J68 O15
    Date: 2015–02
  10. By: Go, Delfin S.; Lofgren, Hans; Mendez Ramos, Fabian; Robinson, Sherman
    Abstract: This paper uses a three-step Bayesian cross-entropy estimation approach in an environment of noisy and scarce data to estimate behavioral parameters for a computable general equilibrium model. The estimation also measures how labor-augmenting productivity and other structural parameters in the model may have shifted over time to contribute to the generation of historically observed changes in the economic arrangement. In this approach, the parameters in a computable general equilibrium model are treated as fixed but unobserved, represented as prior mean values with prior error mass functions. Estimation of the parameters involves using an information-theoretic Bayesian approach to exploit additional information in the form of new data from a series of social accounting matrices, which are assumed were measured with error. The estimation procedure is"efficient"in the sense that it uses all available information and makes no assumptions about unavailable information. As illustration, the methodology is applied to estimate the parameters of a computable general equilibrium model using alternative data sets for the Republic of Korea and Sub-Saharan Africa.
    Keywords: Economic Theory&Research,E-Business,Information Security&Privacy,Emerging Markets,Inequality
    Date: 2015–01–01
  11. By: Vilsmeier, Johannes
    Abstract: In this paper we 'update' the option implied probability of default (option iPoD) approach recently suggested in the literature. First, a numerically more stable objective function for the estimation of the risk neutral density is derived whose integrals can be solved analytically. Second, it is reasoned that the originally proposed approach for the estimation of the PoD produces arbitrary results and hence an alternative procedure is suggested that is based on the Lagrange multipliers. Based on numerical evaluations and an illustrative empirical application we conclude that the framework provides very promising results.
    Keywords: Option Implied Probability of Default,Risk Neutral Density,Cross Entropy
    JEL: C51 C52 C61 G12 G24 G32
    Date: 2014
  12. By: Sabrina Mulinacci
    Abstract: A new class of bivariate distributions is introduced that extends the Generalized Marshall-Olkin distributions of Li and Pellerey (2011). Their dependence structure is studied through the analysis of the copula functions that they induce. These copulas, that include as special cases the Generalized Marshall-Olkin copulas and the Scale Mixture of Marshall-Olkin copulas (see Li, 2009),are obtained through suitable distortions of bivariate Archimedean copulas: this induces asymmetry, and the corresponding Kendall's tau as well as the tail dependence parameters are studied.
    Date: 2015–02
  13. By: James Duffy (Institute for New Economic Thinking, Oxford Martin School, and Economics Department, University of Oxford)
    Abstract: We provide a uniform law for the weak convergence of additive functionals of partial sum processes to the local times of linear fractional stable motions, in a setting sufficiently general for statistical applications. Our results are fundamental to the analysis of the global properties of nonparametric estimators of nonlinear statistical models that involve such processes as covariates.
    Date: 2015–01–22
  14. By: Lillestøl, Jostein (Dept. of Business and Management Science, Norwegian School of Economics); Sinding-Larsen, Richard (Dept. of Geology and Mineral Resources Engineering, Norwegian University of Science and Technology)
    Abstract: This paper considers the problem of point prediction based on a predictive distribution, representing the uncertainty about the outcome. The issue explored is the reporting of a single characteristic, typically the mean, the median or the mode, in the context of a skewed distribution and asymmetric loss. Special attention is given to the two-piece normal distribution and asymmetric piecewise linear and quadratic loss. The practical context for the issue is the yearly reporting of remaining petroleum resources given by the authorities to stakeholders that may ask for just a single number.
    Keywords: Estimate reporting; asymmetric loss; point prediction; predictive distribution
    JEL: C00 C10 C13
    Date: 2015–01–30
  15. By: Conrad, Christian; Mammen , Enno
    Abstract: In this paper we develop an asymptotic theory for the parametric GARCH-in-Mean model. The asymptotics is based on a study of the volatility as a process of the model parameters. The proof makes use of stochastic recurrence equations for this random function and uses exponential inequalities to localize the problem. Our results show why the asymptotics for this specification is quite complex although it is a rather standard parametric model. Nevertheless, our theory does not yet treat all standard specifications of the mean function.
    Keywords: GARCH-in-Mean; stochastic recurrence equations; risk-return relationship
    Date: 2015–01–19

This nep-ecm issue is ©2015 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.