Showing papers in "Journal of Econometrics in 1995"

TL;DR: In this paper, a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables is presented. But the authors do not consider models with predetermined variables that have constant correlation with the effects.

TL;DR: In this paper, the authors show how to estimate VAR's formulated in levels and test general restrictions on the parameter matrices even if the processes may be integrated or cointegrated of an arbitrary order.

TL;DR: In panel data four procedures are widely used: pooling, aggregating, averaging group estimates, and cross-section regression as discussed by the authors, and the theoretical results on the properties of these procedures are illustrated by UK labour demand functions for 38 industries over 30 years.

TL;DR: In this article, the bias of the least square dummy variable (LSDV) estimator has been analyzed in the context of panel data with lagged dependent explanatory variables, and it has been shown that the conventional estimation procedures are asymptotically valid only when the number of observations in the time dimension (T) gets large.

TL;DR: In this article, a decentralized economy will have difficulty in fully exploiting the growth opportunities of GPT's: arms-length market transactions between the GPT and its users may result in "too little, too late" innovation.

TL;DR: In this article, the authors consider a dynamic model for panel data and show that there are more moment conditions than are currently exploited in the literature, and also show that exogenous regressors generate a larger number of relevant moment conditions in dynamic model than they would in a static model.

TL;DR: In this paper, the authors proposed new methods for testing and correcting for sample selection bias in panel data models, which allow the unobserved effects in both the regression and selection equations to be correlated with the observed variables; the error distribution in the regression equation is unspecified; arbitrary serial dependence in the idiosyncratic errors of both equations is allowed; all idiosyncratic error can be heterogeneously distributed.

TL;DR: The authors used a newly available dataset on the RD and showed that how strong and robust the RD is depends on whether the control for potential industry and firm effects is considered, i.e., whether the RD can be robust to potential industry effects.

TL;DR: An analysis of a large panel data set on Israeli industrial firms finds that most of the growth in aggregate productivity comes from productivity changes within firms rather than from entry, exit, or differential growth.

TL;DR: In this article, it was shown that the Breusch and Pagan statistic does not enjoy the same desirable asymptotic (in n) properties as the distribution-free distribution, even under the hypothesis of no cross-sectional correlations.

TL;DR: In this paper, the identification problem for simultaneous equations is solved by the well-known rank condition, which gives a necessary and sufficient condition for the parameters to be uniquely or statistically identified by linear restrictions.

TL;DR: The exponential generalized beta distribution (EGB) as discussed by the authors is a five-parameter beta distribution which nests the generalized beta and gamma distributions and includes more than thirty distributions as limiting or special cases.

TL;DR: In this paper, a Monte Carlo study examines several estimation procedures of the asymptotic covariance matrix in quantile and censored quantile regression models: design matrix bootstrap, error bootstrapping, order statistic, sigma bootstrap and heteroskedastic kernel.

TL;DR: In this article, the authors explore relationships between industry performance measures and investments in high-tech office and information technology capital for two-digit manufacturing industries from 1968 through 1986, and find limited evidence of a positive relationship between profitability and the share of hightech capital in the total physical capital stock.

TL;DR: The authors compare the performance of univariate homoskedastic, GARCH, autoregressive, and nonparametric models for conditional variances, using five bilateral weekly exchange rates for the dollar, 1973-1989.

TL;DR: In this article, the authors analyzed the long-run foreign transmission effects in a multivariate time-series model of Danish and German prices, exchange rates and interest rates, and found that the vector process is I(2), but that a linear transformation of the prices and the nomical exchange rate removes the I (2) trend from the data.

TL;DR: In this paper, the authors developed consistent estimators for nonlinear regression specifications when errors in variables are present, and they applied their methodology to estimation of Engel curves on household data, finding that the "Lesser-Working" specification of budget shares regressed on the log of income or expenditure should be generalized to higher-order terms in log income.

TL;DR: In this article, the authors consider the use of Markov chain Monte Carlo methods to analyze hierarchical versions of Zellner's SUR model and propose an efficient algorithm to estimate a Markov time-varying parameter SUR model.

TL;DR: In this article, the authors propose inferential procedures for error correction models in structural form, with particular attention paid to the issues of exogeneity of conditioning variables and identification of cointegration parameters as well as short run parameters.

TL;DR: In this article, the authors derived three LM statistics for an error component model with first-order serially correlated errors, and the corresponding LM statistic is the same whether the alternative is AR(1) or MA(1).

TL;DR: A framework for analyzing forecast errors in a panel data setting to test for forecast rationality when forecast errors are simultaneously correlated across individuals, across target years, and across forecast horizons using Generalized Method of Moments estimation is developed.

TL;DR: In this paper, the authors construct a number of quality-adjusted price indexes for personal computers in the US marketplace over the 1989-1992 time period, incorporating simultaneously the time, age, and vintage effects of computer models and then develop a corresponding specification test procedure.

TL;DR: It is argued that it is better to use model selection procedures rather than formal hypothesis testing when deciding on model specification, because testing favors the null hypothesis, typically uses an arbitrary choice of significance level, and researchers using the same data can end up with different final models.

TL;DR: Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities and develops a new method for estimation of structural economic models.

TL;DR: This paper examined changes in the increments (returns) to education and experience at different points of the wage distribution and examined within-group wage inequality, showing that inequality increased differentially for all skill groups.

TL;DR: This paper showed that even when a linear regression model has first-order autoregressive errors, it is possible for auto-gressive least squares estimation (e.g., Cochrane-Orcutt) to yield inconsistent estimates.

TL;DR: In this paper, the asymptotic local power of unit root tests with the same data span is shown to be independent of sampling frequency, and a measure of the power trade-off between sampling frequency and time span for distinct alternatives is derived using an approximate slopes approach.

TL;DR: The roots of the traditional textbook approach to econometric modeling are traced to two older statistical traditions, Fisher's experimental design paradigm and Gauss's ‘theory of errors’, and it is argued that this approach is ill-suited for nonexperimental (observational) data.

TL;DR: This paper proposes a new variational characterization of the closely related set of state price densities, based on minimization of the Kullback-Leibler Information Criterion, which automatically satisfies an important positivity constraint.