Assessing Model Misspecification in Stochastic Linear Regression Analysis/strong>
Authors:-Research Scholar Siddamsetty Upendra, Research Scholar R. Abbaiah
Abstract-This paper studies misspecification tests for stochastic linear regression models, including the Durbin-Watson test, Ramsey’s regression specification error test, Lagrange’s multiplier test, and UTTS’ rainbow test. Specification errors arise when there are deviations from the underlying assumptions of a stochastic linear regression model, impacting associated inferences. Specifically, errors may occur in specifying the error vector ( ) and the data matrix ( X ). Common causes of specification errors involve including irrelevant independent variables or excluding relevant ones in the stochastic linear regression model. Previous research by Ivan Krivy et al. (2000) presented two stochastic algorithms for estimating parameters in nonlinear regression models. In a 1984 paper, Russell Davidson et al. developed a computational procedure for a variety of model specification tests. Ludger Ruschendorf et al. (1993) constructed nonlinear regression representations of general stochastic processes, focusing on specific representations for Markov chains and certain m-dependent sequences. This study contributes to the understanding of misspecification in stochastic linear regression models, utilizing a range of tests to identify errors in model assumptions and parameter estimation. The insights gained from these tests can enhance the accuracy and reliability of regression model inferences.