Estimation of Parameters of Heteroscedastic Error Models Under Various Hypothesized Error Structures

Adjibolosoo, Senyo

doi:10.2307/2348976

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…A prerequisite is the knowledge of the underlying error structure or the possibility to estimate it consistently. For example, Adjibolosoo (1993) studies various hypothesized error structures and compares the estimation results in a Monte Carlo study. Note that for a homoscedastic error before the expenditure‐share weighting, such a weighting will induce heteroscedasticity and inefficient estimation (Solon et al ., 2015).…”

Section: Formulas and Decompositionsmentioning

confidence: 99%

Price Measurement Using Scanner Data: Time‐Product Dummy Versus Time Dummy Hedonic Indexes

Haan

Hendriks

Scholz

2020

Review of Income and Wealth

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This paper compares two model-based multilateral price indexes: the time-product dummy (TPD) index and the time dummy hedonic (TDH) index, both estimated by expenditure-share weighted least squares regression. The TPD model can be viewed as the saturated version of the underlying TDH model, and we argue that the regression residuals are "distorted toward zero" due to overfitting. We decompose the ratio of the two indexes in terms of average regression residuals of the new and disappearing items. The decomposition aims to explain the conditions under which the TPD index suffers from qualitychange bias or, more generally, lack-of-matching bias. An example using scanner data on packaged men's T-shirts illustrates our framework.

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Section: Formulas and Decompositionsmentioning

confidence: 99%

Price Measurement Using Scanner Data: Time‐Product Dummy Versus Time Dummy Hedonic Indexes

Haan

Hendriks

Scholz

2020

Review of Income and Wealth

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“…Independently, studies had been carried out on Bayesian estimation with heteroscedasticity disturbance error, Oloyede et al (2013), Tanizaki (2003), Chib (1993), Chib and Greenberg (1994) where it was affirmed that the presence of such disturbance error in the data or model renders inferences of the parameter estimates invalid. Adjibolosoo (1993) investigated the efficiency of estimators under various heteroscedastic error structures and concluded that an exponential family of heteroscedastic error structures would bring about efficient parameter estimates for improved statistical inference. Both heteroscedasticity and autocorrelated disturbance errors violated the assumption of ordinary least squares and render it inefficient and inconsistent.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Restricted Stein-rule Least Squares with non-spherical Disturbances

Oloyede

2023

JOSMA

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Bayesian restricted stein-rule least squares is a novelty estimator introduced to obtain parameters of the family of restricted least squares with intertwined heteroscedastic and autocorrelated disturbance errors. Errors in regression modelling set to measure the quality of the data and or model of which intertwining of most prominent errors disturbances play a significant role in determining the quality of the aforementioned. Efforts were geared towards the comparison of the performances and relative efficiency of the small sample property of Bayesian and classical family of restricted stein-rule least squares. The sample size was set at 25 to capture the intertwined disturbance errors, and the iteration of the Monte Carlo simulation was set at 10000 for both classical and Bayesian paradigms. and for both Autoregressive of order one (AR(1)) and Moving average of order one (MA(1)) processes respectively were set asymmetrically as -0.8,-0.5,-0.3, 0,0.3,0.5 and 0.8 while as a scale of heteroscedasticity was set at 0-homoscedasticity, -0.3-mild,-0.5-moderate and (0.7,0.9)-severe. The outcome of the study pointed out that Bayesian estimation (both posterior mean and Bayes estimates) for both restricted and restricted stein-rule estimators outperformed classical restricted and restricted stein-rule estimators in both performances and relative efficiency. It is therefore recommended to make use of the Bayesian framework when encountering similar disturbances in a small sample size.

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Estimation of Parameters of Heteroscedastic Error Models Under Various Hypothesized Error Structures

Cited by 2 publications

References 16 publications

Price Measurement Using Scanner Data: Time‐Product Dummy Versus Time Dummy Hedonic Indexes

Price Measurement Using Scanner Data: Time‐Product Dummy Versus Time Dummy Hedonic Indexes

Bayesian Restricted Stein-rule Least Squares with non-spherical Disturbances

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