Results 1 
5 of
5
Least Absolute Deviation Estimation of Linear Econometric Models: A Literature Review
 SSRN
, 1965
"... This paper is an attempt to survey the literature on LAD estimation of single as well as multiequation linear econometric models ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
This paper is an attempt to survey the literature on LAD estimation of single as well as multiequation linear econometric models
The Error Term in the History of Time Series Econometrics.” Econometric Theory
, 2001
"... We argue that many methodological confusions in timeseries econometrics may be seen as arising out of ambivalence or confusion about the error terms. Relationships between macroeconomic time series are inexact and, inevitably, the early econometricians found that any estimated relationship would on ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
We argue that many methodological confusions in timeseries econometrics may be seen as arising out of ambivalence or confusion about the error terms. Relationships between macroeconomic time series are inexact and, inevitably, the early econometricians found that any estimated relationship would only fit with errors. Slutsky interpreted these errors as shocks that constitute the motive force behind business cycles. Frisch tried to dissect further the errors into two parts: stimuli, which are analogous to shocks, and nuisance aberrations. However, he failed to provide a statistical framework to make this distinction operational. Haavelmo, and subsequent researchers at the Cowles Commission, saw errors in equations as providing the statistical foundations for econometric models, and required that they conform to a priori distributional assumptions specified in structural models of the general equilibrium type, later known as simultaneousequations models (SEM). Since theoretical models were at that time mostly static, the structural modelling strategy relegated the dynamics in timeseries data frequently to nuisance, atheoretical complications. Revival of the shock interpretation in theoretical models came about through the rational expectations movement and development of the VAR (Vector AutoRegression) modelling approach. The socalled LSE (London School of Economics) dynamic specification approach decomposes the dynamics of modelled variable into three parts: shortrun shocks, disequilibrium shocks and innovative residuals, with only the first two of these sustaining an economic interpretation.
Least Absolute Deviation Estimation of MultiEquation Linear Econometric Models  A Study Based on Monte Carlo Experiments
 Doctoral Dissertation, Department of Economics, NEHU, Shillong ( unpublished
, 2004
"... Introduction: Ideal properties of the Ordinary Least Squares (OLS or L 2 norm) estimator of singleequation linear econometric model y = Xa + e, while e obeys GaussMarkov conditions, are well known. Additionally, if e is normally distributed, OLS estimator of a is also the Maximum Likelihood (ML) e ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Introduction: Ideal properties of the Ordinary Least Squares (OLS or L 2 norm) estimator of singleequation linear econometric model y = Xa + e, while e obeys GaussMarkov conditions, are well known. Additionally, if e is normally distributed, OLS estimator of a is also the Maximum Likelihood (ML) estimator. However, when e is nonnormally distributed, hyperkurtic or infested with sizeable outliers, OLS estimator fails to perform. It has been observed that in such cases Least Absolute Deviation (LAD or L 1 norm) estimator performs very well. Sporadic errors in X (where sample X is true X + #, and # is a sparse matrix with nonzero elements substantial in size) also vitiate OLS estimation. There too, LAD performs well. The real world data often consist of disturbances that are nonnormally distributed, some of which permit the variate to take on nonnegative values only. It has been known since Pareto, that distribution of income bears testimony to distribution of the error term with in
WORKING Assessing the Rothstein Test: Does It Really Show Teacher ValueAdded Models Are Biased?
, 2012
"... In a provocative and influential paper, Jesse Rothstein (2010) finds that standard valueadded models (VAMs) suggest implausible future teacher effects on past student achievement, a finding that obviously cannot be viewed as causal. This is the basis of a falsification test (the Rothstein falsifica ..."
Abstract
 Add to MetaCart
In a provocative and influential paper, Jesse Rothstein (2010) finds that standard valueadded models (VAMs) suggest implausible future teacher effects on past student achievement, a finding that obviously cannot be viewed as causal. This is the basis of a falsification test (the Rothstein falsification test) that appears to indicate bias in VAM estimates of current teacher contributions to student learning. Rothstein’s finding is significant because there is considerable interest in using VAM teacher effect estimates for highstakes teacher personnel policies, and the results of the Rothstein test cast considerable doubt on the notion that VAMs can be used fairly for this purpose. However, in this paper, we illustrate—theoretically and through simulations—plausible conditions under which the Rothstein falsification test rejects VAMs even when there is no bias in estimated teacher effects, and even when students are randomly assigned conditional on the covariates in the model. On the whole, our findings show that the “Rothstein falsification test ” is not definitive in showing bias, which suggests a much more encouraging picture for those wishing to use VAM teacher effect estimates for policy purposes. Mathematica Policy Research I.
Does it Really Show Teacher ValueAdded Models are Biased?
"... Mathematica Policy ResearchContents Acknowledgements......................................................................................................................................................................... ii Abstract................................................................... ..."
Abstract
 Add to MetaCart
Mathematica Policy ResearchContents Acknowledgements......................................................................................................................................................................... ii Abstract.............................................................................................................................................................................................. iii