Showing papers in "Journal of Econometrics in 1998"

TL;DR: In this paper, two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator are presented. But both estimators require restrictions on the initial conditions process.

TL;DR: In this article, the authors discuss various approaches to modeling consumer heterogeneity and evaluate the utility of these approaches for marketing applications, and discuss the benefits of different approaches for different types of consumer heterogeneity.

TL;DR: The authors describe and apply choice models, including generalizations of logit called mixed logits, that do not exhibit the restrictive "independence from irrelevant alternatives" property and can approximate any substitution pattern.

TL;DR: In this article, a long memory stochastic volatility (LMSV) model is proposed, which is constructed by incorporating an ARFIMA process in a standard Stochastic Volatility scheme.

TL;DR: In this article, a new Bayesian approach for models with multiple change points is proposed, where a latent discrete state variable is specified to evolve according to a discrete-time discrete-state Markov process with transition probabilities constrained so that the state variable can either stay at the current value or jump to the next higher value.

TL;DR: In this paper, a modified maximum likelihood estimator that corrects for misclassification is proposed, which combines the maximum rank correlation estimator of Han (1987) (Journal of Econometrics 35, 303-316) with isotonic regression.

TL;DR: In this article, the authors review the literature on stated preference elicitation methods and introduce the concept of testing data generation process invariance across SP and revealed preference (RP) choice data sources.

TL;DR: The authors developed tests for spatial-error correlation and methods of estimation in the presence of such correlation for discrete-choice models, which are based on the notion of a generalized residual, are a set of orthogonality conditions that should be satisfied under the null.

TL;DR: In this paper, the Monte Carlo maximum likelihood (MCMCMC) method is used to estimate stochastic volatility (SV) models, which can be expressed as a linear state space model with log chi-square disturbances and decompose it into a Gaussian part, constructed by the Kalman filter, and a remainder function whose expectation is evaluated by simulation.

TL;DR: In this paper, it was shown that impulse responses and forecast error decompositions are inconsistent at long horizons in unrestricted VARs with some unit roots and that they do not converge to the optimal predictors over long forecast horizons.

TL;DR: In this paper, a two-stage method of moments (TSM) estimator and a nonlinear weighted least-squares estimator are proposed for the fully parametric case.

TL;DR: In this paper, a simple consistent test is considered and a bootstrap method is proposed for testing a parametric regression functional form, which gives a more accurate approximation to the null distribution of the test than the asymptotic normal theory result.

TL;DR: In this article, the authors extend the Markov switching model and use the information contained in leading indicator data to forecast transition probabilities, which can then be used to calculate expected durations.

TL;DR: It is illustrated that the robustness of conclusions drawn from SVAR exercises are questionable, and the problem of identification failure in structural VAR models is examined.

TL;DR: In this article, it is shown that if the break occurs early in the series, routine application of standard Dickey-Fuller tests can lead to a very serious problem of spurious rejection of the unit root null hypothesis.

TL;DR: The authors analyzes the robustness of the two most commonly used cointegration tests: the single equation based test of Engle and Granger (EG) and the system-based test of Johansen.

TL;DR: In this article, the authors discuss methods for reducing the bias of consistent estimators that are biased in finite samples, and apply them to two problems: estimating the autoregressive parameter in an AR(1) model with a constant term, and estimating a logic model.

TL;DR: This paper explored approaches for modeling measurement error in marketing research, including random, method and measure specific sources of error, and a three-facet multiplicative model is addressed wherein latent variables underlying a phenomenon under investigation are shown to interact with multiple methods and occasions of measurement.

TL;DR: The MBB covariance estimator is shown to provide heteroskedasticity and autocorrelation consistent (HAC) standard errors for least squares (LS) and quantile regression (QR) coefficient estimators.

TL;DR: In this article, the authors show how to bound the asymptotic bias of estimates using weights and imputations using the National Longitudinal Survey of Youth (NLSS) dataset.

TL;DR: The authors proposed an integrated consideration set-brand choice model that is capable of accounting for the heterogeneity in consideration set and in the parameters of the brand choice model, which is estimated by an approximation free Markov chain Monte Carlo sampling procedure and applied to a scanner panel data.

TL;DR: In this paper, a new class of rank estimators of scaled coefficients in semiparametric monotonic linear index models is presented, which require no subjective bandwidth choices and have attractive computational properties.

TL;DR: In this article, the authors consider situations in which the researcher's goal is not detecting the presence (absence) of unit roots or their location (i.e., cointegrating relations), but testing some economic hypotheses expressed as coefficient restrictions of VAR models.

TL;DR: In this article, the authors use unit-root techniques to address market response in evolving markets, with a focus on their response to price promotions, and examine four consumer product categories for which high-quality scanner records are available.

TL;DR: In this article, the authors examined the impact of a line extension on price competition between two national yogurt manufacturers and found that the extending firm gains price-setting power, and the positioning of the line extension grows the combined sales, and raises the average weekly contributions for both the expanding firm and the rival firm.

TL;DR: In this paper, the problem of posterior simulation and model choice for Poisson panel data models with multiple random effects has been studied and efficient algorithms based on Markov chain Monte Carlo methods for sampling the posterior distribution are developed.

TL;DR: In this article, the elderly's choice among health plans using data from a 1988 study of the Minneapolis-St. Paul Medicare health plan market is modeled using an extended heterogeneous logit model that incorporates information about attribute importance and allows for heterogeneity in tastes for observed and unobserved attributes.

TL;DR: In this paper, a generalized method of moments test for common nonlinear components in multiple time series is presented in terms of the canonical correlations between the multiple series and a judicially chosen set of test regressors, and its performance in small samples is evaluated using Monte Carlo simulations.

TL;DR: In this paper, the authors examine the dynamic effects of sales promotions and demonstrate that these dynamic econometric models provide greater managerial relevance than static models can, and also demonstrate that the dynamic models provide more managerial relevance.

TL;DR: In this paper, the authors show how to identify nonparametrically scalar stationary diffusions from discrete-time data by recovering the local evolution of the diffusion from two objects that can be inferred directly from discrete time data: the stationary density and a conveniently chosen eigenvalue eigenfunction pair of the conditional expectation operator.