nep-ecm New Economics Papers
on Econometrics
Issue of 2010‒01‒30
thirty-six papers chosen by
Sune Karlsson
Orebro University

  1. Asymptotics and Bootstrap for Transformed Panel Data Regressions By Liangjun Su; Zhenlin Yang
  2. Indirect Inference for Dynamic Panel Models By Christian Gouriéroux; Peter C. B. Phillips; Jun Yu
  3. X-Differencing and Dynamic Panel Model Estimation By Chirok Han; Peter C.B. Phillips; Donggyu Sul
  4. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics By Koop, Gary; Korobilis, Dimitris
  5. Instrumental Variable Quantile Estimation of Spatial Autoregressive Models By Liangjun Su; Zhenlin Yang
  6. Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance By Peter C. B. Phillips; Jun Yu
  7. Parameter estimation in nonlinear AR–GARCH models By Mika Meitz; Pentti Saikkonen
  8. Estimating Semiparametric Panel Data Models by Marginal Integration By Qian, Junhui; Wang, Le
  9. Optimal Estimation under Nonstandard Conditions By Werner Ploberger; Peter C.B. Phillips
  10. GMM estimation of Spatial Panels with Fixed Effects By Moscone, Francesco; Tosetti, Elisa
  11. Uniform Asymptotic Normality in Stationary and Unit Root Autoregression By Chirok Han; Peter C.B. Phillips; Donggyu Sul
  12. Estimating Nonlinearities in Spatial Autoregressive Models By Nicolas Debarsy; Vincenzo Verardi
  14. A Robust LM Test for Spatial Error Components By Zhenlin Yang
  15. Set Inference for Semiparametric Discrete Games By Kyoo il Kim
  16. A note on the geometric ergodicity of a nonlinear AR–ARCH model By Mika Meitz; Pentti Saikkonen
  17. On Joint Modelling and Testing for Local and Global Spatial Externalities By Zhenlin Yang
  18. Uniform Convergence Rate of the SNP Density Estimator and Testing for Similarity of Two Unknown Densities By Kyoo il Kim
  19. A framework for adaptive Monte-Carlo procedures By Bernard Lapeyre; J\'er\^ome Lelong
  20. Evaluating Value-at-Risk models via Quantile Regression By Wagner Piazza Gaglianone; Luiz Renato Lima; Oliver Linton; Daniel Smith
  21. Higher Order Bias Correcting Moment Equation for M-Estimation and its Higher Order Efficiency By Kyoo il Kim
  22. Convex Treatment Response and Treatment Selection By Stefan Boes
  23. Parallel hierarchical sampling:a general-purpose class of multiple-chains MCMC algorithms By Antonietta Mira; Fabio Rigat
  24. Semiparametric Estimation of Signaling Games By Kyoo il Kim
  25. Quasi-Experimental Identification and Estimation in the Regression Kink Design By David Card; David S. Lee; Zhuan Pei
  26. Two New Zealand Pioneer Econometricians By Peter C.B. Phillips
  27. Correcting the bias in the estimation of a dynamic ordered probit with fixed effects of self-assessed health status By Jesús M. Carro; Alejandra Traferri
  28. Bayesian Estimation and Model Selection in the Generalised Stochastic Unit Root Model By Roberto Leon-Gonzalez; Fuyu Yang
  29. Regime specific predictability in predictive regressions By Jesús Gonzalo; Jean-Ives Piterakis
  30. A forward demeaning transformation for a dynamic count panel data model By Yoshitsugu Kitazawa
  31. Rotation in Multiple Correspondence Analysis: a planar rotation iterative procedure By Jérome SARACCO (GREThA UMR CNRS 5113); Marie CHAVENT (IMB UMR CNRS 5251); Vanessa KUENTZ (IMB UMR CNRS 5251)
  32. Controlled diffusion processes with markovian switchings for modeling dynamical engineering systems By Héctor Cañada; Rosario Romera
  33. A double-hurdle count model for completed fertility data from the developing world By Alfonso Miranda
  34. An area-wide real-time database for the euro area. By Domenico Giannone; Jérôme Henry; Magdalena Lalik; Michele Modugno
  35. A Globally Consistent Framework for Reliability-based Trade Statistics Reconciliation in the Presence of an Entrepôt By Zhi Wang; Mark Gehlhar; Shunli Yao
  36. Incomplete Preferences in Choice Experiments: A note on avoidable noise and bias in welfare estimates By Mitesh Kataria; Jason F. Shogren

  1. By: Liangjun Su; Zhenlin Yang (Singapore Management University)
    Abstract: This paper investigates the asymptotic properties of quasi-maximum likelihood estimators for transformed random effects models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoscedasticity, and simple model structure. We develop a quasi maximum likelihood-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the parameter estimates, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance matrix. Monte Carlo results reveal that these estimates perform well in finite samples, and that the gains by using bootstrap procedure for inference can be enormous.
    Keywords: Asymptotics, Bootstrap, Quasi-MLE, Transformed panels, Variance-covariance matrix estimate
    JEL: C23 C15 C51
    Date: 2010–01
  2. By: Christian Gouriéroux; Peter C. B. Phillips; Jun Yu (Singapore Management University)
    Abstract: It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with .xed e¤ects is inconsistent under .xed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in practical applications when T is small and the autoregressive parameter is close to unity. The present paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference (Gouriéroux et al., 1993), shows unbiasedness and analyzes efficiency. The method is implemented in a simple linear dynamic panel model, but has wider applicability and can, for instance, be easily ex-tended to more complicated frameworks such as nonlinear models. Monte Carlo studies show that the proposed procedure achieves substantial bias reductions with only mild increases in variance, thereby substantially reducing root mean square errors. The method is compared with certain consistent estimators and bias-corrected ML estimators previously proposed in the literature and is shown to have superior .nite sample properties to GMM and the bias-corrected ML of Hahn and Kuersteiner (2002). Finite sample performance is compared with that of a recent estimator proposed by Han and Phillips (2005).
    Keywords: Autoregression, Bias reduction, Dynamic panel, Fixed e¤ects Indirect inference
    JEL: C33
    Date: 2010–01
  3. By: Chirok Han (Korea University); Peter C.B. Phillips (Cowles Foundation, Yale University); Donggyu Sul (University of Texas Dallas)
    Abstract: This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting "panel fully aggregated" estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, free from bias for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, GMM and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of the n/T ratio, which makes the approach appealing for practical work. In the time series AR(1) case (n = 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coefficient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for fixed and even small n. For panel data modeling purposes, a general-to-specific selection rule is suggested for choosing the lag parameter p and the procedure works in a standard manner, aiding practical implementation. The PFAE estimation method is also applicable to dynamic panel models with exogenous regressors. Some simulation results are reported giving comparisons with other dynamic panel estimation methods.
    Keywords: GMM, Panel full aggregation, Stacked and pooled least squares, Panel unit root, X-Differencing
    JEL: C22 C23
    Date: 2010–01
  4. By: Koop, Gary; Korobilis, Dimitris
    Abstract: Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus, over-parameterization problems may arise. Bayesian methods have become increasingly popular as a way of overcoming these problems. In this monograph, we discuss VARs, factor augmented VARs and time-varying parameter extensions and show how Bayesian inference proceeds. Apart from the simplest of VARs, Bayesian inference requires the use of Markov chain Monte Carlo methods developed for state space models and we describe these algorithms. The focus is on the empirical macroeconomist and we offer advice on how to use these models and methods in practice and include empirical illustrations. A website provides Matlab code for carrying out Bayesian inference in these models.
    Keywords: Empirical macroeconometrics; Bayesian estimation; MCMC; vector autoregressions; factor models; time-varying parameters
    JEL: C51 C53 C50 C52 E58 C12 C87 E52 C15 C11
    Date: 2009–09–27
  5. By: Liangjun Su; Zhenlin Yang (Singapore Management University)
    Abstract: We propose an instrumental variable quantile regression (IVQR) estimator for spatial autoregressive (SAR) models. Like the GMM estimators of Lin and Lee (2006) and Kelejian and Prucha (2006), the IVQR estimator is robust against heteroscedasticity. Unlike the GMM estimators, the IVQR estimator is also robust against outliers and requires weaker moment conditions. More importantly, it allows us to characterize the heterogeneous impact of variables on different points (quantiles) of a response distribution. We derive the limiting distribution of the new estimator. Simulation results show that the new estimator performs well in finite samples at various quantile points. In the special case of median restriction, it outperforms the conventional QML estimator without taking into account of heteroscedasticity in the errors; it also outperforms the GMM estimators with or without considering the heteroscedasticity.
    Keywords: Spatial Autoregressive Model, Quantile Regression, Instrumental Variable, Quasi Maximum Likelihood, GMM, Robustness
    JEL: C13 C21 C51
    Date: 2010–01
  6. By: Peter C. B. Phillips; Jun Yu (Singapore Management University)
    Abstract: This paper overviews maximum likelihood and Gaussian methods of estimating continuous time models used in finance. Since the exact likelihood can be constructed only in special cases, much attention has been devoted to the development of methods designed to approximate the likelihood. These approaches range from crude Euler-type approximations and higher order stochastic Taylor series expansions to more complex polynomial-based expansions and infill approximations to the likelihood based on a continuous time data record. The methods are discussed, their properties are outlined and their relative finite sample performance compared in a simulation experiment with the nonlinear CIR diffusion model, which is popular in empirical finance. Bias correction methods are also considered and particular attention is given to jackknife and indirect inference estimators. The latter retains the good asymptotic properties of ML estimation while removing finite sample bias. This method demonstrates superior performance in finite samples.
    Keywords: Maximum likelihood, Transition density, Discrete sampling, Continuous record, Realized volatility, Bias reduction, Jackknife, Indirect inference
    JEL: C22 C32
    Date: 2010–01
  7. By: Mika Meitz (Koc University); Pentti Saikkonen (University of Helsinki)
    Abstract: This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.
    Keywords: Nonlinear Autoregression, Generalized Autoregressive Conditional Heteroskedasticity, Nonlinear Time Series Models, Quasi-Maximum Likelihood Estimation, Strong Consistency, Asymptotic Normality
    JEL: C13 C22
    Date: 2010–01
  8. By: Qian, Junhui; Wang, Le
    Abstract: We propose a new methodology for estimating semiparametric panel data models, with a primary focus on the nonparametric component. We eliminate individual effects using first differencing transformation and estimate the unknown function by marginal integration. We extend our methodology to treat panel data models with both individual and time effects. And we characterize the asymptotic behavior of our estimators. Monte Carlo simulations show that our estimator behaves well in finite samples in both random effects and fixed effects settings.
    Keywords: Semiparametric Panel Data Model; Partially Linear; First Differencing; Marginal Integration
    JEL: C13 C14 C23
    Date: 2009–11–10
  9. By: Werner Ploberger (Washington University in St. Louis); Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: We analyze optimality properties of maximum likelihood (ML) and other estimators when the problem does not necessarily fall within the locally asymptotically normal (LAN) class, therefore covering cases that are excluded from conventional LAN theory such as unit root nonstationary time series. The classical Hájek-Le Cam optimality theory is adapted to cover this situation. We show that the expectation of certain monotone "bowl-shaped" functions of the squared estimation error are minimized by the ML estimator in locally asymptotically quadratic situations, which often occur in nonstationary time series analysis when the LAN property fails. Moreover, we demonstrate a direct connection between the (Bayesian property of) asymptotic normality of the posterior and the classical optimality properties of ML estimators.
    Keywords: Bayesian asymptotics, Asymptotic normality, Local asymptotic normality, Locally asymptotic quadratic, Optimality property of MLE, Weak convergence
    JEL: C22
    Date: 2010–01
  10. By: Moscone, Francesco; Tosetti, Elisa
    Abstract: In this paper we consider the estimation of a panel data regression model with spatial autoregressive disturbances, fixed effects and unknown heteroskedasticity. Following the work by Kelejian and Prucha (1999), Lee and Liu (2006a) and others, we adopt the Generalized Method of Moments (GMM) and consider as moments a set linear quadratic conditions in the disturbances. As in Lee and Liu (2006a), we assume that the inner matrices in the quadratic forms have zero diagonal elements to robustify moments against unknown heteroskedasticity. We derive the asymptotic distribution of the GMM estimator based on such conditions. Hence, we carry out some Monte Carlo experiment to investigate the small sample properties of GMM estimators based on various sets of moment conditions.
    Keywords: spatial econometrics; panel data; within estimator
    JEL: C15
    Date: 2010–01–19
  11. By: Chirok Han (Korea University); Peter C.B. Phillips (Cowles Foundation, Yale University); Donggyu Sul (University of Texas Dallas)
    Abstract: While differencing transformations can eliminate nonstationarity, they typically reduce signal strength and correspondingly reduce rates of convergence in unit root autoregressions. The present paper shows that aggregating moment conditions that are formulated in differences provides an orderly mechanism for preserving information and signal strength in autoregressions with some very desirable properties. In first order autoregression, a partially aggregated estimator based on moment conditions in differences is shown to have a limiting normal distribution which holds uniformly in the autoregressive coefficient rho including stationary and unit root cases. The rate of convergence is root of n when |rho| < 1 and the limit distribution is the same as the Gaussian maximum likelihood estimator (MLE), but when rho = 1 the rate of convergence to the normal distribution is within a slowly varying factor of n. A fully aggregated estimator is shown to have the same limit behavior in the stationary case and to have nonstandard limit distributions in unit root and near integrated cases which reduce both the bias and the variance of the MLE. This result shows that it is possible to improve on the asymptotic behavior of the MLE without using an artificial shrinkage technique or otherwise accelerating convergence at unity at the cost of performance in the neighborhood of unity.
    Keywords: Aggregating information, Asymptotic normality, Bias Reduction, Differencing, Efficiency, Full aggregation, Maximum likelihood estimation
    JEL: C22
    Date: 2010
  12. By: Nicolas Debarsy (CERPE - Centre de Recherches en Economie Régionale et Politique Economique - Université de Namur); Vincenzo Verardi (European Centre for Advanced Research in Economics and Statistics (ECARES) - Université Libre de Bruxelles, CRED - Centre de Recherche en Economie du Développement - Université de Namur)
    Abstract: In spatial autoregressive models, the functional form of autocorrelation is assumed to be linear. In this paper, we propose a simple semiparametric procedure, based on Yatchew's (1998) partial linear least squares, that relaxes this restriction. Simple simulations show that this model outperforms traditional SAR estimation when nonlinearities are present. We then apply the methodology on real data to test for the spatial pattern of voting for independent candidates in US presidential elections. We find that in some counties, votes for “third candidates” are non-linearly related to votes for “third candidates” in neighboring counties, which pleads for strategic behavior.
    Keywords: Spatial econometrics; semiparametric estimations
    Date: 2010–01–13
  13. By: Gabriele Fiorentini (Università di Firenze); Enrique Sentana (CEMFI)
    Abstract: We derive computationally simple score tests of serial correlation in the levels and squares of common and idiosyncratic factors in static factor models. The implicit orthogonality conditions resemble the orthogonality conditions resemble the orthogonality conditions of models with observed factors but the weighting matrices reflect their unobservability. We derive more powerful tests for elliptically symmetric distributions, which can be either parametrically or semiparametrically specified, and robustify the Gaussian tests against general nonnormality. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests, and compare them to other existing procedures. Finally, we apply our methods to monthly US stock returns.
    Keywords: ARCH, financial returns, Kalman filter, LM tests, predictability.
    JEL: C32 C13 C12 C14 C16
    Date: 2009–12
  14. By: Zhenlin Yang (Singapore Management University)
    Abstract: This paper presents a modified LM test of spatial error components, which is shown to be robust against distributional misspecifications and spatial layouts. The proposed test differs from the LM test of Anselin (2001) by a term in the denominators of the test statistics. This term disappears when either the errors are normal, or the variance of the diagonal elements of the product of spatial weights matrix and its transpose is zero or approaching to zero as sample size goes large. When neither is true, as is often the case in practice, the effect of this term can be significant even when sample size is large. As a result, there can be severe size distortions of the Anselin’s LM test, a phenomenon revealed by the Monte Carlo results of Anselin and Moreno (2003) and further confirmed by the Monte Carlo results presented in this paper. Our Monte Carlo results also show that the proposed test performs well in general.
    Keywords: Distributional misspecification, Robustness, Spatial layouts, Spatial error components, LM tests
    JEL: C23 C5
    Date: 2010–01
  15. By: Kyoo il Kim (Singapore Management University)
    Abstract: We consider estimation and inference of parameters in discrete games allowing for multiple equilibria, without using an equilibrium selection rule. We do a set inference while a game model can contain infinite dimensional parameters. Examples can include signaling games with discrete types where the type distribution is nonparametrically specified and entry-exit games with partially linear payoffs functions. A consistent set estimator and a con.dence interval of a function of parameters are provided in this paper. We note that achieving a consistent point estimation often requires an information reduction. Due to this less use of information, we may end up a point estimator with a larger variance and have a wider confidence interval than those of the set estimator using the full information in the model. This finding justifies the use of the set inference even though we can achieve a consistent point estimation. It is an interesting future research to compare these two alternatives- CI from the point estimation with the usage of less information vs. CI from the set estimation with the usage of the full information.
    Keywords: Semiparametric Estimation, Set Inference, InÂ…nite Dimensional Parameters, Inequality Moment Conditions, Signaling Game with Discrete Types
    JEL: C13 C14 C35 C62 C73
    Date: 2010–01
  16. By: Mika Meitz (Koc University); Pentti Saikkonen (University of Helsinki)
    Abstract: This note studies the geometric ergodicity of nonlinear autoregressive models with conditionally heteroskedastic errors. A nonlinear autoregression of order p (AR(p)) with the conditional variance specified as the conventional linear autoregressive conditional heteroskedasticity model of order q (ARCH(q)) is considered. Conditions under which the Markov chain representation of this nonlinear AR– ARCH model is geometrically ergodic and has moments of known order are provided. The obtained results complement those of Liebscher [Journal of Time Series Analysis, 26 (2005), 669–689] by showing how his approach based on the concept of the joint spectral radius of a set of matrices can be extended to establish geometric ergodicity in nonlinear autoregressions with conventional ARCH(q) errors.
    Keywords: Nonlinear Autoregression, Autoregressive Conditional Heteroskedasticity, Nonlinear Time Series Models, Geometric Ergodicity, Mixing, Strict Stationarity, Existence of Moments, Markov Models
    JEL: C10 C22
    Date: 2010–01
  17. By: Zhenlin Yang (Singapore Management University)
    Abstract: This paper concerns the joint modeling, estimation and testing for local and global spatial externalities. Spatial externalities have become in recent years a standard notion of economic research activities in relation to social interactions, spatial spillovers and dependence, etc., and have received an increasing attention by econometricians and applied researchers. While conceptually the principle underlying the spatial dependence is straightforward, the precise way in which this dependence should be included in a regression model is complex. Following the taxonomy of Anselin (2003, International Regional Science Review 26, 153-166), a general model is proposed, which takes into account jointly local and global externalities in both modelled and unmodelled effects. The proposed model encompasses all the models discussed in Anselin (2003). Robust methods of estimation and testing are developed based on Gaussian quasi-likelihood. Large and small sample properties of the proposed methods are investigated.
    Keywords: Asymptotic property, Finite sample property, Quasi-likelihood, Spatial regression models, Robustness, Tests of spatial externalities
    JEL: C1 C2 C5
    Date: 2010–01
  18. By: Kyoo il Kim (Singapore Management University)
    Abstract: This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.
    Keywords: SNP Density Estimator, Uniform Convergence Rate, Comparison of Two Densities
    JEL: C12 C14 C16
    Date: 2010–01
  19. By: Bernard Lapeyre (CERMICS); J\'er\^ome Lelong (LJK)
    Abstract: Adaptive Monte Carlo methods are powerful variance reduction techniques. In this work, we propose a mathematical setting which greatly relaxes the assumptions needed by for the adaptive importance sampling techniques presented by Arouna in 2003. We establish the convergence and asymptotic normality of the adaptive Monte Carlo estimator under local assumptions which are easily verifiable in practice. We present one way of approximating the optimal importance sampling parameter using a randomly truncated stochastic algorithm. Finally, we apply this technique to the valuation of financial derivatives and our numerical experiments show that the computational time needed to achieve a given accuracy is divided by a factor up to 5.
    Date: 2010–01
  20. By: Wagner Piazza Gaglianone; Luiz Renato Lima; Oliver Linton; Daniel Smith
    Abstract: This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables, such as whether or not there was an exception, sacrifices too much information. However, most of the specification tests (also called backtests) available in the literature, such as Christoffersen (1998) and Engle and Maganelli (2004) are based on such variables. In this paper we propose a new backtest that does not rely solely on binary variables. It is shown that the new backtest provides a sufficient condition to assess the finite sample performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Our theoretical findings are corroborated through a Monte Carlo simulation and an empirical exercise with daily S&P500 time series.
    Keywords: Value-at-Risk, Backtesting, Quantile Regression
    JEL: C12 C14 C52 G11
    Date: 2009–05
  21. By: Kyoo il Kim (Singapore Management University)
    Abstract: This paper studies an alternative bias correction for the M-estimator, which is obtained by correcting the moment equation in the spirit of Firth (1993). In particular, this paper compares the stochastic expansions of the analytically bias-corrected estimator and the alternative estimator and finds that the third-order stochastic expansions of these two estimators are identical. This implies that at least in terms of the third order stochastic expansion, we cannot improve on the simple one-step bias correction by using the bias correction of moment equations. Though the result in this paper is for a .xed number of parameters, our intuition may extend to the analytical bias correction of the panel data models with individual speci.c e¤ects. Noting the M-estimation can nest many kinds of estimators including IV, 2SLS, MLE, GMM, and GEL, our .nding is a rather strong result.
    Keywords: Third-order Stochastic Expansion, Bias Correction, M-estimation
    JEL: C10
    Date: 2010–01
  22. By: Stefan Boes (Socioeconomic Institute, University of Zurich)
    Abstract: This paper analyzes the identifying power of weak convexity assumptions in treatment effect models with endogenous selection. The counterfactual distributions are constrained either in terms of the response function, or conditional on the realized treatment, and sharp bounds on the potential outcome distributions are derived. The methods are applied to bound the effect of education on smoking.
    Keywords: nonparametric bounds, causality, endogeneity, instrumental variables
    JEL: C14 C30 I12
    Date: 2010–01
  23. By: Antonietta Mira (Department of Economics, University of Insubria, Italy); Fabio Rigat (Department of Statistics and Centre for analytical Science, University of Warwick, UK)
    Abstract: This paper introduces the Parallel Hierarchical Sampler (PHS), a class of Markov chain Monte Carlo algorithms using several interacting chains having the same target distribution but different mixing properties. Unlike any single-chain MCMC algorithm, upon reaching stationarity one of the PHS chains, which we call the “mother” chain, attains exact Monte Carlo sampling of the target distribution of interest. We empirically show that this translates in a dramatic improvement in the sampler’s performance with respect to single-chain MCMC algorithms. Convergence of the PHS joint transition kernel is proved and its relationships with single-chain samplers, Parallel Tempering (PT) and variable augmentation algorithms are discussed. We then provide two illustrative examples comparing the accuracy of PHS with
    Date: 2009–09
  24. By: Kyoo il Kim (Singapore Management University)
    Abstract: This paper studies an econometric modeling of a signaling game with two players where one player has one of two types. In particular, we develop an estimation strategy that identi…es the payo¤s structure and the distribution of types from data of observed actions. We can achieve uniqueness of equilibrium using a re…nement, which enables us to identify the parameters of interest. In the game, we consider non-strategic public signals about the types. Because the mixing distribution of these signals is nonparametrically specifi…ed, we propose to estimate the model using a sieve conditional MLE. We achieve the consistency and the asymptotic normality of the structural parameters estimates. As an alternative, we allow for the possibility of multiple equilibria, without using an equilibrium selection rule. As a consequence, we adopt a set inference allowing for multiplicity of equilibria.
    Keywords: Semiparametric Estimation, Signaling Game, Set Inference, InÂ…nite Dimensional Parameters, Sieve Simultaneous Conditional MLE
    JEL: C13 C14 C35 C62 C73
    Date: 2010–01
  25. By: David Card (UC Berkeley and NBER); David S. Lee (Princeton University and NBER); Zhuan Pei (Princeton University)
    Abstract: We consider nonparametic identification of the average marginal effect of a continuous endogenous regressor in a generalized nonseparable model when the regressor of interest is a known, deterministic, but kiniked function of an observed continuous assignment variable. This design arises in many institutional settings where a policy variable of interest (such as weekly unemployment benefits) is mechanically related to an observed but potentially endogenous variable (like previous earnings). We characterize a broad class of models in which a "Regression Kink Design" (RKD) provides valid inferences for the underlying marginal effects. Importantly, this class includes cases where the assignment variable is endogenously chose. Under suitable conditions we show that the RKD estimand identifies the "treatment on the treated" parameter (Florens et al., 2009) or the "local average response" (altonji and Matzkin, 2005) that is identified in an ideal randomized experiment. As in a regression discontinuity design, the required indentification assumption implies strong and readilt testable predictions for the pattern of predetermined covariates around the kink point. Standard local linear regression techniques can be easily adapted to obtain "nonparametris" RKD estimates. We illustrate the RKD approach by examining the effect of unemployment insurance benefits on the duration of benefit claims, using rich microdata from the state of Washington.
    Keywords: Unemployment benefits, Washington State, unemployment insurance, regression kink design
    JEL: D50 C01 E24 J08 J64
    Date: 2009–11
  26. By: Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: Two distinguished New Zealanders pioneered some of the foundations of modern econometrics. Alec Aitken, one of the most famous and well-documented mental arithmeticians of all time, contributed the matrix formulation and projection geometry of linear regression, generalized least squares (GLS) estimation, algorithms for Hodrick Prescott (HP) style data smoothing (six decades before their use in economics), and statistical estimation theory leading to the Cramér Rao bound. Rex Bergstrom constructed and estimated by limited information maximum likelihood (LIML) the largest empirical structural model in the early 1950s, opened up the field of exact distribution theory, developed cyclical growth models in economic theory, and spent nearly 40 years of his life developing the theory of continuous time econometric modeling and its empirical application. We provide an overview of their lives, discuss some of their accomplishments, and develop some new econometric theory that connects with their foundational work.
    Keywords: Aitken, Cramér Rao bound, HP filter, Minimum variance unbiased estimation, Projection, GLS; Bergstrom, Continuous time, Exact distribution, LIML, UK economy; Pioneers of econometrics
    JEL: B16 C00
    Date: 2010–01
  27. By: Jesús M. Carro; Alejandra Traferri
    Abstract: This paper considers the estimation of a dynamic ordered probit with fixed effects, with an application to self-assessed health status. The estimation of nonlinear panel data models with fixed effects by MLE is known to be biased when T is not very large. The problem is specially severe in our model because of the dynamics and because it contains two fixed effects: one in the linear index equation, interpreted as unobserved health status, and another one in the cut points, interpreted as heterogeneity in reporting behavior. The contributions of this paper are twofold. Firstly this paper contributes to the recent literature on bias correction in nonlinear panel data models by applying and studying the finite sample properties of two of the existing proposals to the ordered probit case. The most direct and easily applicable correction to our model is not the best one and still has important biases in our sample sizes. Secondly, we contribute to the literature that study the determinants of Self-Assesed Health measures by applying the previous analysis on estimation methods to the British Household Panel Survey.
    Keywords: Dynamic ordered probit, Self-assessed health, Reporting bias, Panel data, Unobserved heterogeneity, Incidental parameters, Bias correction
    JEL: C23 C25 I19
    Date: 2009–06
  28. By: Roberto Leon-Gonzalez; Fuyu Yang
    Abstract: We develop Bayesian techniques for estimation and model comparison in a novel Generalised Stochastic Unit Root (GSTUR) model. This allows us to investigate the presence of a deterministic time trend in economic series, while allowing the degree of persistence to change over time. In particular the model allows for shifts from stationarity I(0) to nonstationarity I(1) or vice versa. The empirical analysis demonstrates that the GSTUR model provides new insights on the properties of some macroeconomic time series such as stock market indices, in ation and ex- change rates.
    Keywords: Stochastic Unit Root, MCMC, Bayesian
    JEL: C11 C32
    Date: 2010–01
  29. By: Jesús Gonzalo; Jean-Ives Piterakis
    Abstract: Predictive regressions are linear specifications linking a noisy variable such as stock returns to past values of a more persistent regressor such as valuation ratios, interest rates etc with the aim of assessing the presence or absence of predictability. Key complications that arise when conducting such inferences are the potential presence of endogeneity, the poor adequacy of the asymptotic approximations amongst numerous others. In this paper we develop an inference theory for uncovering the presence of predictability in such models when the strength or direction of predictability, if present, may alternate across different economically meaningful episodes. This allows us to uncover economically interesting scenarios whereby the predictive power of some variable may kick in solely during particular regimes or alternate in strength and direction (e.g. recessions versus expansions, periods of high versus low stock market valuation, periods of high versus low term spreads etc). The limiting distributions of our test statistics are free of nuisance parameters and some are readily tabulated in the literature. Finally our empirical application reconsiders the literature on Dividend Yield based stock return predictability and contrary to the existing literature documents a strong presence of predictability that is countercyclical, occurring solely during bad economic times.
    Keywords: Endogeneity, Persistence, Return predictability, Threshold models
    Date: 2010–12
  30. By: Yoshitsugu Kitazawa (Faculty of Economics, Kyushu Sangyo University)
    Abstract: In this note, a forward demeaning transformation is proposed for the linear feedback model with the explanatory variables being strictly exogenous on count panel data. This transformation is analogous to that proposed by Arellano and Bover (1995) for the ordinary dynamic panel data model.
    Keywords: forward demeaning; linear feedback model; strictly exogenous explanatory variables; count panel data
    JEL: C23
    Date: 2010–01
  31. By: Jérome SARACCO (GREThA UMR CNRS 5113); Marie CHAVENT (IMB UMR CNRS 5251); Vanessa KUENTZ (IMB UMR CNRS 5251)
    Abstract: Multiple Correspondence Analysis (MCA) is a well-known multivariate method for statistical description of categorical data (see for instance Greenacre and Blasius, 2006). Similarly to what is done in Principal Component Analysis (PCA) and Factor Analysis, the MCA solution can be rotated to increase the components simplicity. The idea behind a rotation is to find subsets of variables which coincide more clearly with the rotated components. This implies that maximizing components simplicity can help in factor interpretation and in variables clustering. In PCA, the probably most famous rotation criterion is the varimax one introduced by Kaiser (1958). Besides, Kiers (1991) proposed a rotation criterion in his method named PCAMIX developed for the analysis of both numerical and categorical data, and including PCA and MCA as special cases. In case of only categorical data, this criterion is a varimax-based one relying on the correlation ratio between the categorical variables and the MCA numerical components. The optimization of this criterion is then reached by the algorithm of De Leeuw and Pruzansky (1978). In this paper, we give the analytic expression of the optimal angle of planar rotation for this criterion. If more than two principal components are to be retained, similarly to what is done by Kaiser (1958) for PCA, this planar solution is computed in a practical algorithm applying successive pairwise planar rotations for optimizing the rotation criterion. A simulation study is used to illustrate the analytic expression of the angle for planar rotation. The proposed procedure is also applied on a real data set to show the possible benefits of using rotation in MCA.
    Keywords: categorical data, multiple correspondence analysis, correlation ratio, rotation, varimax criterion
    JEL: C49 C69
    Date: 2010
  32. By: Héctor Cañada; Rosario Romera
    Abstract: A modeling approach to treat noisy engineering systems is presented. We deal with controlled systems that evolve in a continuous-time over finite time intervals, but also in continuous interaction with environments of intrinsic variability. We face the complexity of these systems by introducing a methodology based on Stochastic Differential Equations (SDE) models. We focus on specific type of complexity derived from unpredictable abrupt and/or structural changes. In this paper an approach based on controlled Stochastic Differential Equations with Markovian Switchings (SDEMS) is proposed. Technical conditions for the existence and uniqueness of the solution of these models are provided. We treat with nonlinear SDEMS that does not have closed solutions. Then, a numerical approximation to the exact solution based on the Euler- Maruyama Method (EM) is proposed. Convergence in strong sense and stability are provided. Promising applications for selected industrial biochemical systems are showed.
    Keywords: markov chains, stochastic dynamical systems, numerical approaches for SDE
    Date: 2009–06
  33. By: Alfonso Miranda (Depatment of Quantitative Social Science - Institute of Education, University of London.)
    Abstract: This paper reports a study on the socio-economic determinants of completed fertility in Mexico. An innovative Poisson Double-Hurdle count model is developed for the analysis. This methodological approach allows low and high order parities to be determined by two different data generating mechanisms, and explicitly accounts for potential endogenous switching between regimes. Unobserved heterogeneity is properly controlled. Special attention is given to study how socio-economic characteristics such as religion and ethnic group affect the likelihood of transition from low to high order parities. Findings indicate that education and Catholicism are associated with reductions in the likelihood of transition from parities lower than four to high order parities. Being an indigenous language speaker, in contrast, increases the odds of a large family.
    Keywords: Completed fertility, count data models, double-hurdle model
    JEL: J13 J15 C25
    Date: 2010–01–19
  34. By: Domenico Giannone (Université Libre de Bruxelles, ECARES CP 144, B-1050 Bruxelles, Belgium.); Jérôme Henry (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); Magdalena Lalik (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); Michele Modugno (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: This paper describes how we constructed a real-time database for the euro area covering more than 200 series regularly published in the European Central Bank Monthly Bulletin, as made available ahead of publication to the Governing Council members before their first meeting of the month. We describe the database in details and study the properties of the euro area real-time data flow and data revisions, also providing comparisons with the United States and Japan. We finally illustrate how such revisions can contribute to the uncertainty surrounding key macroeconomic ratios and the NAIRU. JEL Classification: C01, C82, E24, E58.
    Keywords: real-time; euro area; revisions; database.
    Date: 2010–01
  35. By: Zhi Wang; Mark Gehlhar; Shunli Yao (China Center for Economic Research)
    Abstract: This paper develops a mathematicla programming model to reconcile trade statistics subject to a set of global consistency conditions in the presence of an entrepot. Initial data reliability serves a key function for governing the magnitude of adjustment. Through a two-stage optimization procedure, the adjusted trade statistics are achived as solutions to a system of simultaneous equations that minimize a quadratic penalty function. As an empirical illustration, the model is applied to reconcile the 2004 trade statistics reported by China, Hong Kong, and their major trading partners, initialized with detailed estimates of bilateral trade flows, re-export markups, cif/fob ratios and data reliability indexes.
    Keywords: trade statistics reconciliation, entrepot trade, data reliability, global consistency
    JEL: F1 C61 C81
    Date: 2010–01
  36. By: Mitesh Kataria (Max Planck Institute of Economics, Strategic Interaction Group, Jena); Jason F. Shogren (University of Wyoming, Department of Economics and Finance, Laramie)
    Abstract: How does a choice experiment (CE) model derived under standard preference axioms perform for respondents with incomplete preferences? Using simulated data, we show how such miss-specification results in unnecessary noise and bias in welfare estimates, and can be avoided.
    Keywords: Choice experiment, Ordered Logit, Bias, Preference Axioms
    JEL: D61 Q51
    Date: 2010–01–15

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