nep-ecm New Economics Papers
on Econometrics
Issue of 2006‒09‒11
thirteen papers chosen by
Sune Karlsson
Orebro University

  1. Semiparametric Regression with Kernel Error Model By Ao Yuan; Jan G. De Gooijer
  2. Near-Optimal Unit Root Tests with Stationary Covariates with Better Finite Sample Size By Elena Pesavento
  4. Stability of nonlinear AR-GARCH models By Meitz, Mika; Saikkonen, Pentti
  5. Identification and Nonparametric Estimation of a Transformed Additively Separable Model By David Jacho-Chavez; Arthur Lewbel; Oliver Linton
  6. The Unobserved Heterogeneity Distribution in Duration Analysis By Jaap H. Abbring; Gerard J. van den Berg
  7. Forecasting Realized Volatility by Decomposition By Markku Lanne
  8. Forecasting Emerging Market Indicators: Brazil and Russia By Victor Bystrov
  9. The Event-History Approach to Program Evaluation By Jaap H. Abbring
  10. A reappraisal of the evidence on PPP: a systematic investigation into MA roots in panel unit root tests and their implications By Fischer, Christoph; Porath, Daniel
  11. Bayesian Estimation of Unknown Heteroscedastic Variances By Hiroaki Chigira; Tsunemasa Shiba
  12. Derivation of Design Weights : The Case of the German Socio-Economic Panel (SOEP) By Martin Spieß
  13. Modeling Heterogeneity By Arthur Lewbel

  1. By: Ao Yuan (Howard University); Jan G. De Gooijer (Faculty of Economics and Econometrics, Universiteit van Amsterdam)
    Abstract: We propose and study a class of regression models, in which the mean function is specified parametrically as in the existing regression methods, but the residual distribution is modeled nonparametrically by a kernel estimator, without imposing any assumption on its distribution. This specification is different from the existing semiparametric regression models. The asymptotic properties of such likelihood and the maximum likelihood estimate (MLE) under this semiparametric model are studied. We show that under some regularity conditions, the MLE under this model is consistent (as compared to the possibly pseudo consistency of the parameter estimation under the existing parametric regression model), and is asymptotically normal with rate sqrt{n} and efficient. The nonparametric pseudo-likelihood ratio has the Wilks property as the true likelihood ratio does. Simulated examples are presented to evaluate the accuracy of the proposed semiparametric MLE method.
    Keywords: information bound; kernel density estimator; maximum likelihood estimate; nonlinear regression; semiparametric model; U-statistic; Wilks property
    JEL: C14
  2. By: Elena Pesavento
    Abstract: Numerous tests for integration and cointegration have been proposed in the literature. Since Elliott, Rothemberg and Stock (1996) the search for tests with better power has moved in the direction of finding tests with some optimality properties both in univariate and multivariate models. Although the optimal tests constructed so far have asymptotic power that is indistinguishable from the power envelope, it is well known that they can have severe size distortions in finite samples. This paper proposes a simple and powerful test that can be used to test for unit root or for no cointegration when the cointegration vector is known. Although this test is not optimal in the sense of Elliott and Jansson (2003), it has better finite sample size properties while having asymptotic power curves that are indistinguishable from the power curves of optimal tests. Similarly to Hansen (1995), Elliott and Jansson (2003), Zivot (2000), and Elliott, Jansson and Pesavento (2005) the proposed test achieves higher power by using additional information contained in covariates correlated with the variable being tested. The test is constructed by applying Hansen’s test to variables that are detrended under the alternative in a regression augmented with leads and lags of the stationary covariates. Using local to unity parametrization, the asymptotic distribution of the test under the null and the local alternative is analytically computed.
    Keywords: Unit Root Test, GLS detrending.
    JEL: C32
    Date: 2006
  3. By: Manuel Arellano; Jinyong Hahn (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: The purpose of this paper is to review recently development methods of estimation of nonlinear fixed effects panel data models with reduced bias properties. We begin by describing fixed effects estimators and the incidental parameters problem. Next the explain how to construct analytical bias correction of estimators, followed by bias correction of estimators, followed by bias correction of the moment equation, and bias corrections for the concentrated likelihood. We then turn to discuss other approaches leading to bias correction based on orthogonalization and their extensions. The remaining sections consider quasi maximum likelihood estimation for dynamic models, the estimation of marginal effects, and automatic methods based on simulation.
    Keywords: Asymptotic corrections, bias reduction, fixed effects, modifies likelihood, nonlinear models, panel data, simulation methods.
    JEL: C23
    Date: 2005–10
  4. By: Meitz, Mika (Dept. of Economic Statistics, Stockholm School of Economics); Saikkonen, Pentti (Dept. of Mathematics and Statistics, University of Helsinki)
    Abstract: This paper studies the stability of nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. Conditions under which the model is stable in the sense that its Markov chain representation is geometrically ergodic are provided. This implies the existence of an initial distribution such that the process is strictly stationary and beta-mixing. Conditions under which the stationary distribution has finite moments are also given. The results cover several nonlinear specifications recently proposed for both the conditional mean and conditional variance.
    Keywords: -
    JEL: C22
    Date: 2006–06–01
  5. By: David Jacho-Chavez (Indiana University); Arthur Lewbel (Boston College); Oliver Linton (London School of Economics)
    Abstract: Let r(x,z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses identification and consistent estimation of the unknown functions H, M, G and F, where r(x, z) = H[M (x, z)] and M(x,z) = G(x) + F(z). An estimation algorithm is proposed for each of the model's unknown components when r(x, z) represents a conditional mean function. The resulting estimators use marginal integration, and are shown to have a limiting Normal distribution with a faster rate of convergence than unrestricted nonparametric alternatives. Their small sample performance is studied in a Monte Carlo experiment. We empirically apply our results to nonparametrically estimate and test generalized homothetic production functions in four industries within the Chinese economy.
    Keywords: Partly separable models; Nonparametric regression; Dimension reduction; Generalized homothetic function; Production function.
    JEL: C13 C14 C21 D24
    Date: 2006–09–04
  6. By: Jaap H. Abbring (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam); Gerard J. van den Berg (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)
    Abstract: In a large class of hazard models with proportional unobserved heterogeneity, the distribution of the heterogeneity among survivors converges to a gamma distribution. This convergence is often rapid. We derive this result as a general result for exponential mixtures and explore its implications for the specification and empirical analysis of univariate and multivariate duration models.
    Keywords: duration analysis; exponential mixture; gamma distribution; limit distribution; mixed proportional hazard
    JEL: C41 C14
    Date: 2006–07–05
  7. By: Markku Lanne
    Abstract: Forecasts of the realized volatility of the exchange rate returns of the Euro against the U.S. Dollar obtained directly and through decomposition are compared. Decomposing the realized volatility into its continuous sample path and jump components and modeling and forecasting them separately instead of directly forecasting the realized volatility is shown to lead to improved out-of-sample forecasts. Moreover, gains in forecast accuracy are robust with respect to the details of the decomposition.
    Keywords: Mixture model, Jump, Realized volatility, Gamma distribution
    JEL: C22 C52 C53 G15
    Date: 2006
  8. By: Victor Bystrov
    Abstract: The adoption of inflation targeting in emerging market economies makesaccurate forecasting of inflation and output growth in these economies of primary importance. Since only short spans of data are available for such markets, autoregressive and small-scale vector autoregressive models can be suggested as forecasting tools. However,these models include only a few economic time series from the whole variety of data available to forecasters. Therefore dynamic factor models, extracting information from a large number of time series, can be suggested as a reasonable alternative. In this paper two approaches are evaluated on the basis of data available for Brazil and Russia. The results allow us to suggest that the forecasting performance of the models considered depends on the statistical properties of the series to be forecast, which are affected by structural changes and changes in operating regime. This interaction between the statistical properties of the series and the forecasting performance of models requires more detailed investigation.
    Keywords: forecasting, emerging markets, factor models
    JEL: C53 C32 E37
    Date: 2006
  9. By: Jaap H. Abbring (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)
    Abstract: This paper studies the event-history approach to microeconometric program evaluation. We present a mixed semi-Markov event-history model, discuss its application to program evaluation, and analyze its empirical content. The results of this paper provide fundamental insights in what can be learned from longitudinal micro data about, for example, the effects of training programs for the unemployed on their unemployment durations and subsequent job stability. They can guide the choice of particular models and methods for the empirical analysis of such effects.
    Keywords: event-history analysis; identifiability; mixed semi-Markov model; program evaluation
    JEL: C14 C31 C41
    Date: 2006–07–03
  10. By: Fischer, Christoph; Porath, Daniel
    Abstract: Panel unit root tests of real exchange rates – as opposed to univariate tests – usually reject non-stationarity. These tests, however, could be biased if the real exchange rate contained MA roots. Indeed, two independent arguments claim that the real exchange rate, being a sum of a stationary and a non-stationary component, is possibly an ARIMA (1, 1, 1) process. Monte Carlo simulations show, how systematic changes in the parameters of the components, of the test equation and of the correlation matrix affect the size of first and second generation panel unit root tests. Two components of the real exchange rate, the real exchange rate of a single good and a weighted sum of relative prices, are constructed from the data for a panel of countries. Computation of the relevant parameters reveals that panel unit root tests of the real exchange rate are severely oversized, usually much more so than simple ADF tests. Thus, the evidence for PPP from panel unit root tests may be merely due to extreme size biases.
    Keywords: panel unit root test, purchasing power parity, real exchange rate, Monte Carlo simulation
    JEL: C33 F31
    Date: 2006
  11. By: Hiroaki Chigira; Tsunemasa Shiba
    Abstract: We propose a Bayesian procedure to estimate possibly heteroscedastic variances of the regression error term, without assuming any structure on them. What we propose in this paper, may be construed as a Conditional Bayesian procedure that is conditioned upon the HCCM obtained from the OLS estimation of the original regression model. After we obtain the Eicker-White HCCM, we set up a Bayesian model and use an MCMC to simulate posterior pdf's of heteroscedastic variances whose structures are unknown. In addition to the numerical examples, we present an empirical investigation on the stock prices of Japanese pharmaceutical and biomedical companies.
    Keywords: Eicker-White HCCM, orthogonal regressors, conditional Bayesian, MCMC, stock return variance estimation
    Date: 2006–09
  12. By: Martin Spieß
    Abstract: Design-based estimators of totals, means or proportions in finite populations generally are functions of weighted sums. If each element selected into the sample is also observed, then for the calculation of the pi-estimator these weights are just the inverse inclusion probabilities of the elements. However, if e.g. nonresponse or attrition over time occurs, calculation of these weights also includes modeling of nonresponse and/or attrition mechanisms. Since models of these mechanisms are disputable, 'pure' design weights can be the basis for calculating alternative weights by a different modeling e.g. of nonresponse and/or attrition mechanisms. In the case of complex sampling schemes, however, it is often not possible to derive the exact inclusion probabilities. In that case, weights may be derived based on approximations and/or simplifying assumptions. In this paper, after describing the selection schemes of the subsamples A, B, C, D and E of the German Socio-Economic Panel (SOEP), approximate design weights are derived which enable users of the SOEP to calculate their own weights if desired.
    Keywords: Design-based inference; Approximate design weights; Complex surveys; SOEP
    Date: 2005
  13. By: Arthur Lewbel (Boston College)
    Abstract: My goal here is to provide some synthesis of recent results regarding unobserved heterogeneity in nonlinear and semiparametric models, using as a context Matzkin (2005a) and Browning and Carro (2005), which were the papers presented in the Modeling Heterogeneity session of the 2005 Econometric Society World Meetings in London. These papers themselves consist of enormously heterogeneous content, ranging from high theory to Danish milk, which I will attempt to homogenize. The overall theme of this literature is that, in models of individual economic agents, errors at least partly reflect unexplained heterogeneity in behavior, and hence in tastes, technologies, etc.,. Economic theory can imply restrictions on the structure of these errors, and in particular can generate nonadditive or nonseparable errors, which has profound implications for model specification, identification, estimation, and policy analysis.
    Keywords: unobserved heterogeneity, nonlinear models, semiparametric models
    Date: 2006–09–04

This nep-ecm issue is ©2006 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.