Download or read book Estimating the Autocorrelated Error Model with Trended Data Further Results written by Rolla Edward Park and published by . This book was released on 1979 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Monte Carlo study is made of the small sample properties of various estimators of the linear regression model with first-order autocorrelated errors. When independent variables are trended, estimators using T transformed observations (Prais-Winsten) are much more efficient than those using T-1 (Cochrane-Orcutt). The best of the feasible estimators is iterated Prais-Winsten using a sum-of-squared-error minimizing estimate of the autocorrelation coefficient rho. None of the feasible estimators performs well in hypothesis testing; all seriously underestimate standard errors, making estimated coefficients appear to be much more significant than they actually are. (Author).

Download or read book Estimating the Autocorrelated Error Model With Trended Data written by Rand Corporation and published by . This book was released on 1976 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimating the Autocorrelated Error Model with Trended Data written by Rolla Edward Park and published by . This book was released on 1978 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Monte Carlo study is made of the small sample properties of various estimators of the linear regression model with first-order autocorrelated errors. When independent variables are trended, estimators using T transformed observations (Prais-Winsten) are much more efficient than those using T-1 (Cochrane-Orcutt). The best of the feasible estimators is iterated Prais-Winsten using a sum-of-squared-error minimizing estimate of the autocorrelation coefficient rho. None of the feasible estimators performs well in hypothesis testing; all seriously underestimate standard errors, making estimated coefficients appear to be much more significant than they actually are. (Author).

Download or read book Estimating Deterministic Trends in the Presence of Serially Correlated Errors written by Eugene Canjels and published by . This book was released on 1994 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies the problems of estimation and inference in the linear trend model: yt=̉+þt+ut, where ut follows an autoregressive process with largest root þ, and þ is the parameter of interest. We contrast asymptotic results for the cases þþþ

Download or read book Estimation of Autocorrelated Error Components written by Lung-Fei Lee and published by . This book was released on 1979 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Measurement Error in Nonlinear Models written by Raymond J. Carroll and published by CRC Press. This book was released on 1995-07-06 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.

Download or read book Statistical Inference in Autoregressive Models written by B. Ramanjineyulu and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, an attempt has been made by developing some inferential methods for autoregressive models by using Internally studentized residuals.In the Applied regression analysis, the autoregressive models, moving average models and combined autoregressive and moving average models have a wide number applications. The study on autoregressive process/models is considered to be essential to both the theoretical and applied statisticians.The first order and higher order autoregressive models for regressed variable and errors have been described by giving auto covariance functions.Further, an autoregressive dynamic model without constant term has been specified and in the presence of lagged dependent variable, a modified durbin's h-statistic for testing the hypthesis of no auto correlation has been developed for first order autoregressive error process, Instrumental variable method of estimation has been proposed to estimate the parameters of first order autoregressive errors model with lagged dependent variable as regressor and hence obtained estimates for autocorrelation co-efficients based an Internally studentized residual

Download or read book Convenient Methods for Estimation of Linear Regression Models with MA 1 Errors written by Glenn M. MacDonald and published by Kingston, Ont. : Institute for Economic Research, Queen's University. This book was released on 1983 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimating the Autocorrelated Error Model with Small Sample Size written by R. A. (Richard Arthur) Holmes and published by Burnaby, B.C. : Simon Fraser University, Faculty of Business Administration. This book was released on 1983 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Measurement Error in Nonlinear Models written by Raymond J. Carroll and published by CRC Press. This book was released on 2006-06-21 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex

Download or read book Practical Considerations When Estimating in the Presence of Autocorrelation written by Sanjiv Jaggia and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: When correcting for autocorrelation, most econometrics texts suggest using a quasi-differencing procedure. A number of issues arise. First, it is found that popular two-step procedures do not sufficiently correct for autocorrelation in certain instances. Second, while it is true that most regression packages implement an iterative procedure, the methodology itself is not conveyed in a straightforward manner to students of econometrics. In addition, for autocorrelated errors, the importance of the correction factor in simple forecasting is often overlooked. Finally, regression packages report r squared that is not comparable to that from the Ordinary Least Squares (OLS) estimation. This paper succinctly outlines the procedure for performing iterative procedures, explicitly accounts for autocorrelation among errors when generating forecasts, and identifies the necessary transformations for making proper comparisons relating to r squared.

Download or read book On the Strong Consistency of Estimators for Certain Distributed Lag Models with Autocorrelated Errors and Applications to Nonlinear Estimation for Models with Autoregressive Error Processes written by Phoebus J. Dhrymes and published by . This book was released on 1971 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation and Testing in Quantitative Linear Models with Autocorrelated Errors written by Gülhan Alpargu and published by . This book was released on 2001 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The efficiency of estimation procedures and the validity of testing procedures in simple and multiple quantitative linear models with autocorrelated errors have been studied in this thesis. The importance of the nature of the explanatory variable(s), fixed and trended versus purely random or following a first-order autoregressive [AR(1)] process, has been emphasized in Monte Carlo studies. The estimation procedures were compared on the basis of different measures of efficiency, relative to OLS or GLS, depending on the context. The estimation procedures studied include the Ordinary Least Squares (OLS), Generalized Least Squares (GLS), estimated GLS, Maximum Likelihood (ML), Restricted Maximum Likelihood (REML), First Differences (FD) and original First-Difference Ratios (FDR). The derived testing procedures were compared on the basis of a condition of strict validity as well as a criterion taking the variability of empirical significance levels into account." --

Download or read book Estimation of Rational Lag Models with Autocorrelated Errors written by Armstrong, John B and published by . This book was released on 1981 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The effect of autocorrelated errors on various least squares estimators written by Dun-Mow Hong and published by . This book was released on 1971 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bias in the Ordinary Least Squares Estimator in the Dynamic Linear Regression Model with Autocorrelated Disturbances written by B. A. Inder and published by . This book was released on 1987 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Kernel Regression Estimation with Autocorrelated Errors written by Jeffrey D. Hart and published by . This book was released on 1990 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: An approach to bandwidth selection for kernel regression is developed for the situation where errors are correlated according to a first-order autoregressive process. The parameters of the error process are estimated by a robust nonlinear regression on the periodogram of the differenced data. The estimated parameters are substituted into cross-validation criteria which include risk estimation and generalized cross-validation procedures. These are minimized to estimate the optimal bandwidth. The methodology is applied to the trend in United States wheat yields from 1908 to 1971. A small simulation study was performed for two purposes. First, properties of the estimates of the error process parameters were examined. Next, the performances of the criteria for choosing the bandwidth were studied.