On Lindley-like mean correction in the improved estimation of linear regression models

The risks of estimators incorporating the correction to the sample mean in the spirit of Stein and Lindley are approximated in the case of small disturbances.     

On the asymptotic bias and mean squared error of an improved estimator for coefficients in linear regression

An estimator for regression coefficients of Kadiyala (1984) is considered. It is proved that the estimator is asymptotically unbiased. The asymptotic weak mean squared error of the estimator is also derived and it is proved that, under certain conditions, the estimator dominates a general class of estimators given by Vinod and Ullah (1981).     

A locally linear estimation of regression discontinuity

In this paper, we propose a locally linear estimation of a regression discontinuity model. The proposed estimator is applicable to evaluation of the effectiveness of the program treatment, and it improves upon the existing literature by providing not just the treatment effect at discontinuity but also insight of the treatment effect on those near discontinuity. Under some familiar conditions, we establish the consistency and asymptotic normality of the proposed estimator. We also provide an easy to compute consistent covariance matrix.     

Testing for normality in linear regression models using regression and scale equivariant estimators

In this paper we provide a general solution to the problem of controlling the probability of a type I error in normality tests for the disturbances in linear regressions when using robust-regression residuals. We show that many classes of well-known robust regression estimators belong to the class of regression and scale equivariant estimators. It is these equivariance properties that are used to reduce the nuisance parameter space under the null, from which we develop Monte Carlo and Maximized Monte Carlo tests for the null of disturbance normality. Finally, we illustrate in a simulation experiment the potential power gains from using robust-regression residuals in testing this null hypothesis.     

对云南林业发展面临的十大矛盾之思考

本文从分析云南林业发展所面临的十大矛盾的实质及其内在运动规律入手，提出如何处理这十大矛盾的具体方针和对策。     

Econometrics and inconsistencies in the national accounts

Economic data are typically inconsistent with national accounting identities, contain measurement errors and are sometimes unavailable. A generalized conditioned least square procedure for the adjustment of data is proposed to deal with these problems. It is shown how the resulting data may be used for FIML estimation of the parameters of dynamic models (and the data themselves) with the aid of the Kalman-Bucy filter. An illustrative application of the proposed techniques to UK data is reported.     

Consistency ofs 2 in the linear regression model with correlated errors

We give a simple sufficient condition for consistency of the standard OLS-based estimate of the disturbance variance in the linear regression model with autocorrelated disturbances.Research supported by Deutsche Forschungsgemeinschaft (DFG). We are grateful to B. M. Poetscher for a generous supply of counterexamples.     

Robust Bayesian analysis of a parameter change in linear regression

Robust Bayesian analyses in a conjugate normal framework have been developed by Learner (1978) and Polasek and Pötzelberger (1987). Fixing the prior mean and varying the prior covariance Matrix yields a so-called feasible ellipsoid for the posterior mean and robust HPD regions, also called HiFi-regions. This paper considers the application of this approach to gain robust Bayesian inference in case of a parameter change in regression models.     

Explanations of the inconsistencies in survey respondents’ forecasts

A comparison of the point forecasts and the probability distributions of inflation and output growth made by individual respondents to the US Survey of Professional Forecasters indicates that the two sets of forecasts are sometimes inconsistent. We evaluate a number of possible explanations, and find that not all forecasters update their histogram forecasts as new information arrives. This is supported by the finding that the point forecasts are more accurate than the histograms in terms of first-moment prediction.     

A more general class of biased estimators of the co-efficients in linear regression

In a recent paper, Ullah and Ullah (1978) proposed a class of biased estimators, namely double k-class (k₁, k₂) for the coefficients in a linear regression model. Even though, this set of estimators contains James and Stein (1961) as a special case, in its present form, it does not contain the ridge type estimators. The aim of this note is to extend Ullah and Ullah set of estimators and then establish a relationship with the various operational ridge estimators. The conditions under which the extended set of estimators dominates the ordinary least squares estimator are analyzed.     

Inadmissibility of the iterative Stein-rule estimator of the disturbance variance in a linear regression

In this paper, we consider the estimator of the disturbance variance in a linear regression when the Stein-rule estimator is used in place of the OLS estimator (the iterative Stein-rule estimator of the disturbance variance). It is shown that the iterative Stein-rule estimator of the disturbance variance is dominated by the usual estimator of the disturbance variance based on the OLS estimator under the squared error loss criterion, if the number of regressors is greater than or equal to five.     

On the use of robust regression in econometrics

A particular robust regression estimator has gained popularity among applied econometricians. We show that this estimator is inconsistent for the parameters of the conditional mean when the errors are skewed and heteroskedastic, and conclude that therefore its use cannot be generally recommended.     

A Bayesian note on competing correlation structures in the dynamic linear regression model

A Bayesian posterior odds approach is used to distinguish between different error correlation structures in dynamic linear regression models. Recent classical results are provided with a Bayesian interpretation, and a small empirical example illustrates the approach.     

A modification of the CUSUM test in the linear regression model with lagged dependent variables

We consider testing for structural change in a dynamic linear regression model, and show that the well known CUSUM test, which has been initially devised only for the standard static model, can easily be modified such as to remain asymptotically valid also in this nonstandard situation.     

Transformations for an exact goodness-of-fit test of structural change in the linear regression model

This paper considers testing for structural change of unknown form in the linear regression model as a problem of testing for goodness-of-fit. Transformations of recursive (or other LUS) residuals that reduce the problem to one of testing independently distributed uniform variables are presented. Exact empirical distribution function tests can then be applied without having to estimate unknown Parameters. The tests are illustrated by their application to a money demand model.     

On the use of better measured proxies in regression analysis

Improving the measurement of mismeasured regressors may cause the inconsistency in least-square coefficient estimators to rise. An explanation for this result is provided and a more comprehensive summary of the data that is free of this problem is proposed.     

Bootstrap LM tests for higher-order spatial effects in spatial linear regression models

This paper first extends the methodology of Yang (J Econom 185:33–59, 2015) to allow for non-normality and/or unknown heteroskedasticity in obtaining asymptotically refined critical values for the LM-type tests through bootstrap. Bootstrap refinements in critical values require the LM test statistics to be asymptotically pivotal under the null hypothesis, and for this we provide a set of general methods for constructing LM and robust LM tests. We then give detailed treatments for two general higher-order spatial linear regression models: namely the \(\mathtt{SARAR}(p,q)\) model and the \(\mathtt{MESS}(p,q)\) model, by providing a complete set of non-normality robust LM and bootstrap LM tests for higher-order spatial effects, and a complete set of LM and bootstrap LM tests robust against both unknown heteroskedasticity and non-normality. Monte Carlo experiments are run, and results show an excellent performance of the bootstrap LM-type tests.     

The asymptotic efficiency of semiparametric estimators for censored linear regression models

This paper presents numerical comparisons of the asymptotic mean square estimation errors of semiparametric generalized least squares (SGLS), quantite, symmetrically censored least squares (SCLS), and tobit maximum likelihood estimators of the slope parameters of censored linear regression models with one explanatory variable. The results indicate that the SCLS estimator is less efficient than the other two semiparametric estimators. The SGLS estimator is more efficient than quantile estimators when the tails of the distribution of the random component of the model are not too thick and the probability of censoring is not too large. The most efficient semiparametric estimators usually have smaller mean square estimation errors than does the tobit estimator when the random component of the model is not normally distributed and the sample size is 500–1,000 or more.     

On Liu estimators for the logit regression model

This paper introduces a shrinkage estimator for the logit model which is a generalization of the estimator proposed by Liu (1993) for the linear regression. This new estimation method is suggested since the mean squared error (MSE) of the commonly used maximum likelihood (ML) method becomes inflated when the explanatory variables of the regression model are highly correlated. Using MSE, the optimal value of the shrinkage parameter is derived and some methods of estimating it are proposed. It is shown by means of Monte Carlo simulations that the estimated MSE and mean absolute error (MAE) are lower for the proposed Liu estimator than those of the ML in the presence of multicollinearity. Finally the benefit of the Lie estimator is shown in an empirical application where different economic factors are used to explain the probability that municipalities have net increase of inhabitants.     

Asymptotic distribution of least squares estimator and a test statistic in linear regression models

This paper presents a fairly general and sufficiently weak condition on the observation matrix for the asymptotic normality of the least squares estimator of coefficient vector in a linear regression model. The asymptotic distribution of a statistic for testing the nullity of coefficient vector is also considered.