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3 edition of Alternative maximum likelihood procedures for regression with autocorrelated disturbances found in the catalog.

Alternative maximum likelihood procedures for regression with autocorrelated disturbances

Charles M. Beach

Alternative maximum likelihood procedures for regression with autocorrelated disturbances

by Charles M. Beach

  • 385 Want to read
  • 7 Currently reading

Published by Institute for Economic Research, Queen"s University in Kingston, Ont .
Written in English

    Subjects:
  • Regression analysis.,
  • Autocorrelation (Statistics)

  • Edition Notes

    Bibliography: leaf 12.

    StatementCharles M. Beach and James G. MacKinnon.
    SeriesDiscussion paper - Institute for Economic Research, Queen"s University ; no. 211, Discussion paper (Queen"s University (Kingston, Ont.). Institute for Economic Research) ;, no. 211.
    ContributionsMacKinnon, James G., joint author.
    Classifications
    LC ClassificationsHB139 .B42
    The Physical Object
    Pagination12 leaves ;
    Number of Pages12
    ID Numbers
    Open LibraryOL4196057M
    LC Control Number80474865

    of the conditional mean and variance for models with autocorrelated disturbances and as is done in other SAS regression procedures. The following statements regress Y on TIME using ordinary least squares: The maximum likelihood estimates are shown in Figure Figure also shows. iterates efficiently to a global optimum with second derivatives. The likelihood function and treatment of the initial observation are described completely in Davidson and MacKinnon (). References: Beach, Charles M. and James G. MacKinnon, "A Maximum Likelihood Procedure for Regression with Autocorrelated Errors," Econometr , pp.

    Course Description: This course is about the underlying theory and application of maximum likelihood (ML) procedures to social science will be strong emphasis on the statistical theory of maximum likelihood, particularly during the first five weeks or so when we develop principles of specification, estimation, inference, measures of fit, and properties of the ML model. PRESENCE OF SPATIALLY AUTOCORRELATED ERROR TERMS Robin A. Dubin* Abstract-Spatial autocorrelation occurs when population members are related through their geographic location. This paper presents a maximum likelihood procedure for simulta-neously estimating the parameters of the correlation function and the regression coefficients.

    In this article, we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy (ME) approach. Unlike standard procedures that require equating the score function of the maximum likelihood problem at zero, we propose an alternative strategy where the score is instead used as an external informative constraint to the maximization of the convex. This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on.


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Alternative maximum likelihood procedures for regression with autocorrelated disturbances by Charles M. Beach Download PDF EPUB FB2

The widely used Cochrane-Orcutt and Hildreth-Lu procedures for estimating the parameters of a linear regression model with first-order serial correlation typically ignore the first observation.

An alternative maximum likelihood procedure is recommended in this paper. This procedure is preferable to conventional ones on theoretical grounds, and sampling experiments suggest that it may yield. A Monte Carlo simulation is used to study the quality of forecasts obtained from regression models with various degrees of autocorrelation present in the disturbances.

The methods used to estimate the model parameters include least squares, full maximum likelihood, Prais‐Winsten, Cochrane‐Orcutt and Bayesian by: 4.

A Maximum Likelihood Procedure for Regression With Autocorrelated Errors The jackknife procedure is applied in two alternative ways: first to the regression itself, and second to the residuals. Alternative Maximum Likelihood Procedures for Regression with Autocorrelated Disturbances.

January An alternative maximum likelihood procedure is recommended in this : Christian Heumann. Abstract. Although it has been shown [9] that the maximum likelihood (M.L.) estimator of the autocorrelation coefficient in linear models with autoregressive disturbances is asymptotically unbiased, several Monte Carlo studies [8], [6], [15] suggest that finite sample bias is Cited by: 7.

For regression models with first-order autocorrelated disturbances, the traditional prescription of econometricians is to correct for serial correlation by using appropriate estimation techniques such as the Cochrane-Orcutt, Hildreth-Lu, or Prais-Winsten procedures.

the Prais-Winsten approach which is a full maximum likelihood approach with. This estimate of the parameters can be obtained using the least squares or the maximum likelihood method.

detecting the presence of autocorrelation and alternative consistent methods of estimating linear models with autocorrelated disturbance terms have been proposed. A maximum likelihood procedure for regression with autocorrelated.

Marginal-likelihood score-based tests of regression disturbances in the presence of nuisance parameters. "A Maximum Likelihood Procedure for Regression with Autocorrelated Errors," Econometrica, Econometric Society "Locally Optimal Testing When a Nuisance Parameter Is Present Only under the Alternative," The Review of.

The regression model with autocorrelated disturbances is as follows: In these equations, y t are the dependent values, x t is a column vector of regressor variables, is a column vector of structural parameters, and is normally and independently distributed with a mean of 0 and a variance of 2.

Alternative estimators of p Many previous Monte Carlo studies have used the following estimate of p: T T,!' = u=ur- t' Y u2 (10) r=z t=z This estimator is consistent but unlike (9a) or (9b) it does not minimize the sum-of-squared-errors for either CO or PW.

A maximum likelihood procedure for regression with autocorrelated errors. Regression forecasts when disturbances are autocorrelated Regression forecasts when disturbances are autocorrelated Dielman, Terry E.

Texas Christian University, U.S.A. ABSTRACT A Monte Carlo simulation is used to study the quality of forecasts obtained from regression models with various degrees of autocorrelation present in the disturbances.

A maximum likelihood procedure for regression with autocorrelated errors. Academy of Administration at the President of the Republic of Belarus, ().

Autoregressive transformations, trended independent variables and autocorrelated disturbance terms. Greene book Novem PART II Generalized Regression Model and Equation Systems The values that appear off the diagonal depend on the model used for the disturbance.

In most cases, consistent with the notion of a fading memory, the values decline as we move away from the diagonal. Hilderth, C. and J. Lu (), “Demand Relations with Autocorrelated Disturbances,” Technical Bulletin (Michigan State University, Agriculture Experiment Station).

Google Scholar Jarque, C.M. and A.K. Bera (), “A Test for Normality of Observations and Regression Residuals,” International Statistical Review, – “Efficient Estimation of a System of Regression Equations When Disturbances Are Both Serially and Contemporaneously Correlated.” Journal of the American Statistical Association –9.

Google Scholar. Phillips, G. “An Alternative Approach to Obtaining Nagar-Type Moment Approximations in Simultaneous Equation Models.”. “The Small Sample Efficiency of Two-Step Estimators in Regression Models with Autoregressive Disturbances.” Discussion Paper No. University of British Columbia. Harvey, A.

C., and Phillips, G. “Maximum Likelihood Estimation of Regression Models with Autoregressive–Moving Average Disturbances.” Biometrika – Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay.

Informally, it is the similarity between observations as a function of the time lag between them. The analysis of autocorrelation is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying. procedures for autocorrelation of disturbances in nonlinear regression models.

Using a quasi-maximum likelihood procedure, Kobayashi () proposed an extended iterated Cochrane-Orcutt estimator for the nonlinear case.

The criterion is a bias-corrected test. Maximum Likelihood Estimation of Singular Equation Systems with Autoregressive Disturbances Working Paper, Economics Department, Queen's University View citations (2) See also Journal Article in International Economic Review () Alternative Maximum Likelihood Procedures for Regression with Autocorrelated Disturbances.

illustrating regression with autocorrelated errors, and the series Y shown in Figure is used in the following introductory examples.

Ordinary Least Squares Regression To use the AUTOREG procedure, specify the input data set in th e PROC AUTOREG statement and specify the regression model in a MODEL statement. The COUNTREG procedure uses maximum likelihood estimation.

PROC COUNTREG supports the following models for count data: 1. Poisson regression, 2. negative binomial regression with quadratic (NEGBIN2) and linear (NEGBIN1) variance functions, 3.

zero-inflated Poisson (ZIP) model, 4. zero-inflated negative binomial (ZINB) model.Beach, C. M., and MacKinnon, J. G. (). “A Maximum Likelihood Procedure for Regression with Autocorrelated Errors.” Demand Relations with Autocorrelated Disturbances.

Technical ReportMichigan State University Agricultural Experiment Station. (). “Testing the Null Hypothesis of Stationarity against the Alternative of a.nonlinear models whenever full information maximum likelihood estimation is possible.

separate families of hypotheses to single-equation linear regression models both with autocorrelated and nonautocorrelated disturbances. In that paper, the that alternative procedures are .