共查询到20条相似文献,搜索用时 15 毫秒
1.
In this paper, we suggest a blockwise bootstrap wavelet to estimate the regression function in the nonparametric regression
models with weakly dependent processes for both designs of fixed and random. We obtain the asymptotic orders of the biases
and variances of the estimators and establish the asymptotic normality for a modified version of the estimators. We also introduce
a principle to select the length of data block. These results show that the blockwise bootstrap wavelet is valid for general
weakly dependent processes such as α-mixing, φ-mixing and ρ-mixing random variables. 相似文献
2.
Baba Thiam 《Statistica Neerlandica》2019,73(1):63-77
In this paper, we studied an alternative estimator of the regression function when the covariates are observed with error. It is based on the minimization of the relative mean squared error. We obtain expressions for its asymptotic bias and variance together with an asymptotic normality result. Our technique is illustrated on simulation studies. Numerical results suggest that the studied estimator can lead to tangible improvements in prediction over the usual kernel deconvolution regression estimator, particularly in the presence of several outliers in the dataset. 相似文献
3.
The most popular econometric models in the panel data literature are the class of linear panel data models with unobserved individual- and/or time-specific effects. The consistency of parameter estimators and the validity of their economic interpretations as marginal effects depend crucially on the correct functional form specification of the linear panel data model. In this paper, a new class of residual-based tests is proposed for checking the validity of dynamic panel data models with both large cross-sectional units and time series dimensions. The individual and time effects can be fixed or random, and panel data can be balanced or unbalanced. The tests can detect a wide range of model misspecifications in the conditional mean of a dynamic panel data model, including functional form and lag misspecification. They check a large number of lags so that they can capture misspecification at any lag order asymptotically. No common alternative is assumed, thus allowing for heterogeneity in the degrees and directions of functional form misspecification across individuals. Thanks to the use of panel data with large and , the proposed nonparametric tests have an asymptotic normal distribution under the null hypothesis without requiring the smoothing parameters to grow with the sample sizes. This suggests better nonparametric asymptotic approximation for the panel data than for time series or cross sectional data. This is confirmed in a simulation study. We apply the new tests to test linear specification of cross-country growth equations and found significant nonlinearities in mean for OECD countries’ growth equation for annual and quintannual panel data. 相似文献
4.
We propose a class of nonparametric tests for testing non-stochasticity of the regression parameterβ in the regression modely
i
=βx
i
+ɛ
i
,i=1, ...,n. We prove that the test statistics are asymptotically normally distributed both underH
0 and under contiguous alternatives. The asymptotic relative efficiencies (in the Pitman sense) with respect to the best parametric
test have also been computed and they are quite high. Some simulation studies are carried out to illustrate the results.
Research was supported by the University Grants Commission, India. 相似文献
5.
We study a “direct test” of Chu and White (1992) proposed for detecting changes in the trend of a linear regression model. The power of this test strongly depends on a suitable estimation of the variance of the error variables involved. We discuss various types of variance estimators and derive their asymptotic properties under the null-hypothesis of “no change” as well as under the alternative of “a change in linear trend”. A small simulation study illustrates the estimators' finite sample behaviour. 相似文献
6.
This paper is concerned with inference about a function g that is identified by a conditional quantile restriction involving instrumental variables. The paper presents a test of the hypothesis that g belongs to a finite-dimensional parametric family against a nonparametric alternative. The test is not subject to the ill-posed inverse problem of nonparametric instrumental variable estimation. Under mild conditions, the test is consistent against any alternative model. In large samples, its power is arbitrarily close to 1 uniformly over a class of alternatives whose distance from the null hypothesis is proportional to n−1/2, where n is the sample size. Monte Carlo simulations illustrate the finite-sample performance of the test. 相似文献
7.
B.B. Van Der Genugten 《Statistica Neerlandica》1983,37(3):127-141
Abstract In the linear regression model the generalized least squares (GLS) method is only applicable if the covariance matrix of the errors is known but for a scalar factor. Otherwise an estimator for this matrix has to be used. Then we speak of the estimated generalized least squares (EGLS) method. In this paper the asymptotic behaviour of both methods is compared. Results are applied to some standard models commonly used in econometrics 相似文献
8.
In this paper we introduce a linear programming estimator (LPE) for the slope parameter in a constrained linear regression model with a single regressor. The LPE is interesting because it can be superconsistent in the presence of an endogenous regressor and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the LPE. In the first case, the regressor is assumed to be fixed in repeated samples. In the second, the regressor is stochastic and potentially endogenous. For both cases the strong consistency and exact finite-sample distribution of the LPE is established. Conditions under which the LPE is consistent in the presence of serially correlated, heteroskedastic errors are also given. Finally, we describe how the LPE can be extended to the case with multiple regressors and conjecture that the extended estimator is consistent under conditions analogous to the ones given herein. Finite-sample properties of the LPE and extended LPE in comparison to the LSE and instrumental variable estimator (IVE) are investigated in a simulation study. One advantage of the LPE is that it does not require an instrument. 相似文献
9.
We propose a consistent test for a linear functional form against a nonparametric alternative in a fixed effects panel data model. We show that the test has a limiting standard normal distribution under the null hypothesis, and show that the test is a consistent test. We also establish the asymptotic validity of a bootstrap procedure which is used to better approximate the finite sample null distribution of the test statistic. Simulation results show that the proposed test performs well for panel data with a large number of cross-sectional units and a finite number of observations across time. 相似文献
10.
Maxwell L. King 《Journal of econometrics》1983,21(1):35-51
This paper presents a new approach to hypotheses testing problems which are non-nested in the classical sense and which concern the covariance matrix of the disturbance vector of the linear regression model. In particular, the application of the approach to testing for AR(1) disturbances against MA(1) disturbances is explored in some detail. Practical difficulties are discussed and selected upper bounds for the test's five percent significance points are tabulated. The small sample power of four versions of the new test are compared empirically and a clear conclusion is made in regard to the best overall test. 相似文献
11.
Classical estimation techniques for linear models either are inconsistent, or perform rather poorly, under α-stable error densities; most of them are not even rate-optimal. In this paper, we propose an original one-step R-estimation method and investigate its asymptotic performances under stable densities. Contrary to traditional least squares, the proposed R-estimators remain root-n consistent (the optimal rate) under the whole family of stable distributions, irrespective of their asymmetry and tail index. While parametric stable-likelihood estimation, due to the absence of a closed form for stable densities, is quite cumbersome, our method allows us to construct estimators reaching the parametric efficiency bounds associated with any prescribed values (α0,b0) of the tail index α and skewness parameter b, while preserving root-n consistency under any (α,b) as well as under usual light-tailed densities. The method furthermore avoids all forms of multidimensional argmin computation. Simulations confirm its excellent finite-sample performances. 相似文献
12.
Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach 总被引:1,自引:1,他引:1
The explanation of productivity differentials is very important to identify the economic conditions that create inefficiency
and to improve managerial performance. In the literature two main approaches have been developed: one-stage approaches and
two-stage approaches. Daraio and Simar (2005, J Prod Anal 24(1):93–121) propose a fully nonparametric methodology based on
conditional FDH and conditional order-m frontiers without any convexity assumption on the technology. However, convexity has always been assumed in mainstream production
theory and general equilibrium. In addition, in many empirical applications, the convexity assumption can be reasonable and
sometimes natural. Lead by these considerations, in this paper we propose a unifying approach to introduce external-environmental
variables in nonparametric frontier models for convex and nonconvex technologies. Extending earlier contributions by Daraio
and Simar (2005, J Prod Anal 24(1):93–121) as well as Cazals et al. (2002, J Econometrics 106:1–25), we introduce a conditional
DEA estimator, i.e., an estimator of production frontier of DEA type conditioned to some external-environmental variables
which are neither inputs nor outputs under the control of the producer. A robust version of this conditional estimator is
proposed too. These various measures of efficiency provide also indicators of convexity which we illustrate using simulated
and real data.
Cinzia Daraio received Research support from the Italian Ministry of Education Research on Innovation Systems Project (iRis)
“The reorganization of the public system of research for the technological transfer: governance, tools and interventions”
and from the Italian Ministry of Educational Research Project (MIUR 40% 2004) “System spillovers on the competitiveness of
Italian economy: quantitative analysis for sectoral policies” which are acknowledged.
Léopold Simar received Research support from the “Interuniversity Attraction Pole”, Phase V (No. P5/24) from the Belgian Government
(Belgian Science Policy) is acknowledged. 相似文献
13.
Bhaskar Bhattacharya 《Metrika》1998,47(1):71-84
For a (k×k) square contingency table with ordered categories, letX(Y) denote the row (column) number. The conditional symmetry model is given byP(X=i, Y=j|X<Y)=P(X=j, Y=i |X>Y), ∀i<j. In this paper, we study the likelihood ratio tests of conditional symmetry in a square contingency table against two particular classes of one-sided alternatives. We obtain the maximum likelihood estimators under each alternative. The asymptotic null distributions of the likelihood ratio statistics are shown to have chi-bar square type distributions. A simulation study is performed by comparing the powers of different tests. The theory developed is illustrated by using the famous eye vision data from Stuart (1953). 相似文献
14.
Postulating a linear regression of a variable of interest on an auxiliary variable with values of the latter known for all
units of a survey population, we consider appropriate ways of choosing a sample and estimating the regression parameters.
Recalling Thomsen’s (1978) results on non-existence of ‘design-cum-model’ based minimum variance unbiased estimators of regression
coefficients we apply Brewer’s (1979) ‘asymptotic’ analysis to derive ‘asymptotic-design-cummodel’ based optimal estimators
assuming large population and sample sizes. A variance estimation procedure is also proposed. 相似文献
15.
The asymptotic behavior of S-estimators in a random design linear model with long-range-dependent Gaussian errors is considered.
It turns out that the S-estimators of regression parameter and error variance are strongly consistent under mild conditions.
Furthermore, the asymptotic distribution of the S-estimator of regression parameter is normal if the design vectors are i.i.d.
and is non-normal if the design vectors are long-range dependent Gaussian vectors. We also show that the asymptotic distribution
of S-estimator of the error variance is non-normal since the errors are long-range dependent.
Supported by National Natural Science Foundation of China (Grant No. 10571159) and Specialized Research Fund for the Doctor
Program of Higher Education (Grant No. 2002335090). 相似文献
16.
This article is concerned with the inference on seemingly unrelated non‐parametric regression models with serially correlated errors. Based on an initial estimator of the mean functions, we first construct an efficient estimator of the autoregressive parameters of the errors. Then, by applying an undersmoothing technique, and taking both of the contemporaneous correlation among equations and serial correlation into account, we propose an efficient two‐stage local polynomial estimation for the unknown mean functions. It is shown that the resulting estimator has the same bias as those estimators which neglect the contemporaneous and/or serial correlation and smaller asymptotic variance. The asymptotic normality of the resulting estimator is also established. In addition, we develop a wild block bootstrap test for the goodness‐of‐fit of models. The finite sample performance of our procedures is investigated in a simulation study whose results come out very supportive, and a real data set is analysed to illustrate the usefulness of our procedures. 相似文献
17.
The Invariant Quadratic Estimators, the Maximum Likelihood Estimator (MLE) and Restricted Maximum Likelihood Estimator (REML) of variances in an orthogonal Finite Discrete Spectrum Linear Regression Model (FDSLRM) are derived and the problems of unbiasedness and consistency of these estimators are investigated.Acknowledgement. The research was supported by the grants 1/0272/03, 1/0264/03 and 2/4026/04 of the Slovak Scientific Grant Agency VEGA. 相似文献
18.
Martina Hančová 《Metrika》2008,67(3):265-276
The method of “natural” estimation of variances in a general (orthogonal or nonorthogonal) finite discrete spectrum linear
regression model of time series is suggested. Using geometrical language of the theory of projectors a form and properties
of the estimators are investigated. Obtained results show that in describing the first and second moment properties of the
new estimators the central role plays a matrix known in linear algebra as the Schur complement. Illustrative examples with
particular regressors demonstrate direct applications of the results. 相似文献
19.
Jie ZhangAuthor Vitae Lyn C. Thomas Author Vitae 《International Journal of Forecasting》2012,28(1):204
Estimating the recovery rate and recovery amount has become important in consumer credit due to the new Basel Accord regulation and the increase in the number of defaulters as a result of the recession. We compare linear regression and survival analysis models for modelling recovery rates and recovery amounts, in order to predict the loss given default (LGD) for unsecured consumer loans or credit cards. We also look at the advantages and disadvantages of using single and mixture distribution models for estimating these quantities. 相似文献
20.
Estimation of a linear regression under microaggregation with the response variable as a sorting variable 总被引:1,自引:0,他引:1
Microaggregation is a popular statistical disclosure control technique for continuous data. The basic principle of microaggregation is to group the observations in a data set and to replace them by their corresponding group means. However, while reducing the disclosure risk of data files, the technique also affects the results of statistical analyses. The paper deals with the impact of microaggregation on a multiple linear regression in continuous variables. We show that parameter estimates are biased if the dependent variable is used to form the groups. Using this result, we develop a consistent estimator that removes the aggregation bias, and derive its asymptotic covariance matrix. 相似文献