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Empirical Determination of the Tolerable Sample Size for Ols Estimator in the Presence of Multicollinearity (ρ)
, 2014
"... This paper investigates the tolerable sample size needed for Ordinary Least Square (OLS) Estimator to be used when there is presence of Multicollinearity among the exogenous variables of a linear regression model. A regression model with constant term (β0) and two independent variables (with β1 an ..."
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This paper investigates the tolerable sample size needed for Ordinary Least Square (OLS) Estimator to be used when there is presence of Multicollinearity among the exogenous variables of a linear regression model. A regression model with constant term (β0) and two independent variables (with β1
A SecondOrder Approximation to the Bias of OLS Estimates in Bivariate VAR Models
, 1995
"... ABSTRACT: Two terms of a secondorder approximation to the bias of the multivariate OLS estimate are derived using the same technique as in Nicholls and Pope (1988). The resulting secondorder bias approximation is then tested against firstorder alternatives on two bivariate Monte Carlo simulated ..."
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ABSTRACT: Two terms of a secondorder approximation to the bias of the multivariate OLS estimate are derived using the same technique as in Nicholls and Pope (1988). The resulting secondorder bias approximation is then tested against firstorder alternatives on two bivariate Monte Carlo simulated
Determinants of longterm growth: a Bayesian Averaging of Classical Estimates (BACE) approach
, 2003
"... This paper examines the robustness and joint interaction of explanatory variables in crosscountry economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible combina ..."
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Cited by 374 (3 self)
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This paper examines the robustness and joint interaction of explanatory variables in crosscountry economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible
How important is methodology for the estimates of the determinants of happiness
 Economic Journal
, 2004
"... Psychologists and sociologists usually interpret happiness scores as cardinal and comparable across respondents, and thus run OLS regressions on happiness and changes in happiness. Economists usually assume only ordinality and have mainly used ordered latent response models, thereby not taking satis ..."
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Cited by 406 (14 self)
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Psychologists and sociologists usually interpret happiness scores as cardinal and comparable across respondents, and thus run OLS regressions on happiness and changes in happiness. Economists usually assume only ordinality and have mainly used ordered latent response models, thereby not taking
An autoregressive distributed lag modelling approach to cointegration analysis
 Cambridge University
, 1999
"... This paper examines the use of autoregressive distributed lag (ARDL) models for the analysis of longrun relations when the underlying variables are I(1). It shows that after appropriate augmentation of the order of the ARDL model, the OLS estimators of the shortrun parameters are p Tconsistent wi ..."
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Cited by 393 (6 self)
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This paper examines the use of autoregressive distributed lag (ARDL) models for the analysis of longrun relations when the underlying variables are I(1). It shows that after appropriate augmentation of the order of the ARDL model, the OLS estimators of the shortrun parameters are p T
Rank1/2: A Simple Way to Improve the OLS Estimation of Tail Exponents
, 2006
"... A popular way to estimate a Pareto exponent is to run an OLS regression: log (Rank) = c − b log (Size), and take b as an estimate of the Pareto exponent. Unfortunately, this procedure is strongly biased in small samples. We provide a simple practical remedy for this bias, and argue that, if one want ..."
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Cited by 61 (8 self)
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A popular way to estimate a Pareto exponent is to run an OLS regression: log (Rank) = c − b log (Size), and take b as an estimate of the Pareto exponent. Unfortunately, this procedure is strongly biased in small samples. We provide a simple practical remedy for this bias, and argue that, if one
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
, 2004
"... This paper presents a new approach to estimation and inference in panel data models with a multifactor error structure where the unobserved common factors are (possibly) correlated with exogenously given individualspecific regressors, and the factor loadings differ over the cross section units. The ..."
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Cited by 383 (44 self)
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. The estimation procedure has the advantage that it can be computed by OLS applied to an auxiliary regression where the observed regressors are augmented by (weighted) cross sectional averages of the dependent variable and the individual specific regressors. Two different but related problems are addressed: one
The Efficiency of OLS Estimator in the Linear Regression Model With Spatially Correlated Errors
"... : Bounds for the efficiency of ordinary least squares relative to generalized least squares estimator in the linear regression model with firstorder spatial error process are given. Key words: Ordinary least squares, Generalized least squares, Efficiency, Spatial error process, Spatial correlation. ..."
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: Bounds for the efficiency of ordinary least squares relative to generalized least squares estimator in the linear regression model with firstorder spatial error process are given. Key words: Ordinary least squares, Generalized least squares, Efficiency, Spatial error process, Spatial correlation
Integrated Modified OLS Estimation and Fixedb Inference for Cointegrating Regressions
, 2011
"... and ..."
Reexamining 50yearold OLS estimates of the Klein–Goldberger model
"... This paper examines the ordinary least squares estimates of the Klein–Goldberger model by Fox (Journal of Political Economy, 64, 1956, 128). Because Klein and Goldberger published the data set with the model, it is possible to reexamine Fox’s results years later, and investigate the accuracy with w ..."
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This paper examines the ordinary least squares estimates of the Klein–Goldberger model by Fox (Journal of Political Economy, 64, 1956, 128). Because Klein and Goldberger published the data set with the model, it is possible to reexamine Fox’s results years later, and investigate the accuracy
Results 11  20
of
1,826