nep-ecm New Economics Papers
on Econometrics
Issue of 2007‒04‒14
eleven papers chosen by
Sune Karlsson
Orebro University

  1. Test of Unbiasedness of the Integrated Covariance Estimation in the Presence of Noise By Masato Ubukata; Kosuke Oya
  2. Heterogeneity in dynamic discrete choice models By Martin Browning; Jesus Carro
  3. Bias Corrections for Two-Step Fixed Effects Panel Data Estimators By Iván Fernández-Val; Francis Vella
  4. Estimating Euler Equations with Noisy Data: Two Exact GMM Estimators By Sule Alan; Orazio Attanasio; Martin Browning
  5. Automatic spectral density estimation for Random fileds on a lattice via bootstrap By Jose M. Vidal-Sanz
  6. Applications of Subsample, Hybrid, and Size-Correction Methods (joint with D.W.K. Andrews), 2005, this version April 2007 By Patrik Guggenberger
  7. Inferring the Latent Incidence of Inefficiency from DEA Estimates and Bayesian Priors By Daniel Friesner; Ron Mittelhammer; Robert Rosenman
  8. Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation By Jennifer L. Castle; David F. Hendry
  9. Modelling income processes with lots of heterogeneity By Martin Browning; Mette Ejrnaes; Javaier Alvarez
  10. Estimating Intertemporal Allocation Parameters using Simulated Expectation Errors By Sule Alan; Martin Browning
  11. Replication in Economics By Daniel S. Hamermesh

  1. By: Masato Ubukata (Graduate School of Economics, Osaka University); Kosuke Oya (Graduate School of Economics, Osaka University)
    Abstract: The cumulative covariance estimator in Hayashi and Yoshida (2005) which suits for non-synchronous observations possibly has a bias in the presence of the observational noise. We propose the test statistic to detect whether the observational noise causes a measurable bias in the estimator of Hayashi and Yoshida (2005). The test statistic proposed in this paper is asymptotically distributed as standard normal under null hypothesis. The finite sample performance of the test statistic is investigated through Monte Carlo simulation.
    Keywords: test statistic; integrated covariance; non-synchronous observation; observational noise; market microstructure noise
    JEL: C12 D49
    Date: 2007–04
  2. By: Martin Browning; Jesus Carro
    Abstract: We consider dynamic discrete choice models with heterogeneity in both the levels parameter and the state dependence parameter. We first analyse the purchase of full fat milk using a long consumer panel (T > 100) on many households. The large T nature of the panel allows us to consistently estimate the parameters of each household separately. This analysis indicates strongly that the levels and the state dependence parameter are heterogeneous and dependently distributed. This empirical analysis motivates the theoretical analysis which considers the estimation of dynamic discrete choice models on short panels, allowing for more heterogeneity than is usually accounted for. The theoretical analysis considers a simple two state, first order Markov chain model without covariates in which both transition probabilities are heterogeneous. Using such a model we are able to derive small sample analytical results for bias and mean squared error. We discuss the maximum likelihood approach, a novel bias corrected version of the latter and we also develop a new estimator that minimises the integrated mean square error, which we term MIMSE. The attractions of the latter estimator are that it is very easy to compute, it is always identified and it converges to maximum likelihood as T becomes large so that it inherits all of the desirable large sample properties of MLE. Our main finding is that in almost all short panel contexts the MIMSE significantly outperforms the other two estimators in terms of mean squared error.
    Keywords: Unobserved Heterogeneity, Heterogeneous Slopes, Fixed Effects, Binary Choice, Panel Data
    JEL: C23
    Date: 2006
  3. By: Iván Fernández-Val (Boston University); Francis Vella (Georgetown University and IZA)
    Abstract: This paper introduces bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These include limited dependent variable models with both unobserved individual effects and endogenous explanatory variables, and sample selection models with unobserved individual effects. Our two-step approach first estimates the reduced form by fixed effects procedures to obtain estimates of the time variant heterogeneity underlying the endogeneity/selection bias. We then estimate the primary equation by fixed effects including an appropriately constructed control function from the reduced form estimates as an additional explanatory variable. The fixed effects approach in this second step captures the time invariant heterogeneity while the control function accounts for the time varying heterogeneity. Since either or both steps might employ nonlinear fixed effects procedures it is necessary to bias adjust the estimates due to the incidental parameters problem. This problem is exacerbated by the two step nature of the procedure. As these two step approaches are not covered in the existing literature we derive the appropriate correction thereby extending the use of large-T bias adjustments to an important class of models. Simulation evidence indicates our approach works well in finite samples and an empirical example illustrates the applicability of our estimator.
    Keywords: panel data, two-step estimation, endogenous regressors, fixed effects, bias, union premium
    JEL: C23 J31 J51
    Date: 2007–03
  4. By: Sule Alan; Orazio Attanasio; Martin Browning
    Abstract: In this paper we exploit the specific structure of the Euler equation and develop two alternative GMM estimators that deal explicitly with measurement error. The first estimator assumes that the measurement error is lognormally distributed. The second estimator drops the distributional assumption and solves out for the unknown, but constant, conditional mean. Our Monte Carlo results suggest that both proposed estimators perform much better than conventional alternatives based on the exact Euler equation or its log-linear approximation, especially with short panels. The empirical application of the proposed estimators yields plausible estimates of the coefficient of relative risk aversion and discount rate.
    Keywords: Nonlinear Models, Measurement Error, Euler Equation
    JEL: C13 E21
    Date: 2006
  5. By: Jose M. Vidal-Sanz
    Abstract: This paper considers the nonparametric estimation of spectral densities for second order stationary random fields on a d-dimensional lattice. I discuss some drawbacks of standard methods, and propose modified estimator classes with improved bias convergence rate, emphasizing the use of kernel methods and the choice of an optimal smoothing number. I prove uniform consistency and study the uniform asymptotic distribution, when the optimal smoothing number is estimated from the sampled data.
    Date: 2007–03
  6. By: Patrik Guggenberger
  7. By: Daniel Friesner; Ron Mittelhammer; Robert Rosenman (School of Economic Sciences, Washington State University)
    Abstract: Data envelopment analysis (DEA) is among the most popular empirical tools for measuring cost and productive efficiency. Because DEA is a linear programming technique, establishing formal statistical properties for outcomes is difficult. We show that the incidence of inefficiency within a population of Decision Making Units (DMUs) is a latent variable, with DEA outcomes providing only noisy sample-based categorizations of inefficiency. We then use a Bayesian approach to infer an appropriate posterior distribution for the incidence of inefficient DMUs based on a random sample of DEA outcomes and a prior distribution on the incidence of inefficiency. The methodology applies to both finite and infinite populations, and to sampling DMUs with and without replacement, and accounts for the noise in the DEA characterization of inefficiency within a coherent Bayesian approach to the problem. The result is an appropriately up-scaled, noise-adjusted inference regarding the incidence of inefficiency in a population of DMUs.
    Keywords: Data Envelopment Analysis, latent inefficiency, Bayesian inference,Beta priors, posterior incidence of inefficiency
    JEL: C11
    Date: 2006–08
  8. By: Jennifer L. Castle; David F. Hendry
    Abstract: Structural models` inflation forecasts are often inferior to those of naive devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, so no gain results, helping interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.
    Keywords: Inflation Forecasting, Structural Breaks, Robust Forecasts, Time-disaggregation, Forecast-error Taxonomies
    JEL: C32 C53
    Date: 2007
  9. By: Martin Browning; Mette Ejrnaes; Javaier Alvarez
    Abstract: All empirical models of earnings processes in the literature assume a good deal of homogeneity. In contrast to this we model earnings processes allowing for lots of heterogeneity between agents. We also introduce an extension to the linear ARMA model that allows that the initial convergence to the long run may be different from that implied by the conventional ARMA model. This is particularly important for unit root tests which are actually tests of a composite of two independent hypotheses. We fit our models to a variety of statistics including most of those considered by previous investigators. We use a sample drawn from the PSID, and focus on white males with a high school degree. Despite this observable homogeneity we find much greater latent heterogeneity than previous investigators. We show that allowance for heterogeneity makes substantial differences to estimates of model parameters and to outcomes of interest. Additionally we find strong evidence against the hypothesis that any worker has a unit root.
    Keywords: Earnings, Heterogeneity, ARMA, Initial Conditions, Unit Root Tests
    JEL: J30 C23
    Date: 2006
  10. By: Sule Alan; Martin Browning
    Abstract: There is widespread agreement that given currently available data, we cannot accurately estimate the parameters of intertemporal allocation using GMM on Euler equations, whether they be exact or approximate. Our reading of this literature and our own results is that this is a small sample (strictly, short panel) problem. The alternative seems to be to move to full structural modelling. In the current state of the art this is cumbersome, fragile and unable to deal with significant heterogeneity. We present a novel structural estimation procedure that is based on simulating expectation errors; we refer to it as Simulated Residual Estimation (SRE). We develop variants of the basic procedure that allow us to account for measurement error in consumption, the `news` in interest rate realisations and for heterogeneity in discount factors.
    Keywords: Intertemporal Allocation, Expectation Errors, Life-Cycle Models
    JEL: D12 D91
    Date: 2006
  11. By: Daniel S. Hamermesh
    Abstract: This examination of the role and potential for replication in economics points out the paucity of both pure replication -- checking on others' published papers using their data -- and scientific replication -- using data representing different populations in one's own work or in a Comment. Several controversies in empirical economics illustrate how and how not to behave when replicating others' work. The incentives for replication facing editors, authors and potential replicators are examined. Recognising these incentives, I advance proposals aimed at journal editors that will increase the supply of replication studies, and I propose a way of generating more scientific replication that will make empirical economic research more credible.
    JEL: A14 B41 C59
    Date: 2007–04

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