Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market

Magnus, Jan R.; Wan, Alan T. K.; Zhang, Xinyu

doi:10.1016/j.csda.2010.09.023

Cited by 44 publications

(39 citation statements)

References 32 publications

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“…Simulation studies will need to provide further insights; see Magnus et al (2009). A major advantage of WALS is that it is based on a transparent treatment of ignorance, while BMA will always depend on subjective (and possibly sensitive) choices of the hyperparameters, such as the specification of g i in Section 2.4.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

A comparison of two model averaging techniques with an application to growth empirics

Magnus

Powell

Prüfer

2010

Journal of Econometrics

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Please cite this article as: Magnus, J.R., Powell, O., Prüfer, P., A comparison of two model averaging techniques with an application to growth empirics. Journal of Econometrics (2009Econometrics ( ), doi:10.1016Econometrics ( /j.jeconom.2009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTA comparison of two model averaging techniques with an application to growth empirics Abstract: Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) -currently one of the standard methods used in growth empirics -with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present. 1 A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTJEL Classification: C51, C52, C13, C11

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Section: Accepted M Manuscriptmentioning

confidence: 99%

A comparison of two model averaging techniques with an application to growth empirics

Magnus

Powell

Prüfer

2010

Journal of Econometrics

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300

283

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“…The data set consists of 560 transactions of the housing estate ‘South Horizon’ located in the South of Hong Kong, recorded by Centaline Property Agency Ltd. from January 2004 to October 2007. The model from Magnus, Wan and Zhang (2011) is adopted to analyze this data set:

\begin{matrix} left \\ {italicLPRICE}_{t} = β_{1} + β_{2} {italicLAREA}_{t} + β_{3} {italicLFLOOR}_{t} + β_{4} {italicGARV}_{t} + β_{5} {italicINDV}_{t} \\ + β_{6} {italicSEAVF}_{t} + β_{7} {italicSEAVS}_{t} + β_{8} {italicSEAVM}_{t} + β_{9} {italicMONV}_{t} \\ + β_{10} {italicSTRI}_{t} + β_{11} {italicSTRN}_{t} + β_{12} {italicUNLUCK}_{t} + e_{t} \end{matrix}

for t = 1, … , 560, where LPRICE is the natural logarithm of the sales price per square foot, and the twelve regressors, including the constant term, are shown in Table 1. As in Magnus, Wan and Zhang (2011), we treated the first six variables as focus regressors and the other six variables as auxiliary regressors, and so we combine 2 6 = 64 models.…”

Section: Empirical Examplementioning

confidence: 99%

Model averaging based on Kullback-Leibler distance

Zhang¹,

Zou²,

Carroll³

2015

STAT SINICA

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This paper proposes a model averaging method based on Kullback-Leibler distance under a homoscedastic normal error term. The resulting model average estimator is proved to be asymptotically optimal. When combining least squares estimators, the model average estimator is shown to have the same large sample properties as the Mallows model average (MMA) estimator developed by Hansen (2007). We show via simulations that, in terms of mean squared prediction error and mean squared parameter estimation error, the proposed model average estimator is more efficient than the MMA estimator and the estimator based on model selection using the corrected Akaike information criterion in small sample situations. A modified version of the new model average estimator is further suggested for the case of heteroscedastic random errors. The method is applied to a data set from the Hong Kong real estate market.

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“…It is worth noticing that this model averaging technique can also be generalized to non-spherical errors (see Magnus et al 2011). Thus, the assumption of homoskedastic and serially uncorrelated regression errors is not crucial for WALS.…”

Section: Weighted Average Least Squaresmentioning

confidence: 99%

Bayesian Model Averaging and Weighted Average Least Squares: Equivariance, Stability, and Numerical Issues

Luca

Magnus

2011

SSRN Journal

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This article is concerned with the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals which implement, respectively, the exact Bayesian Model Averaging (BMA) estimator and the Weighted Average Least Squares (WALS) estimator developed by Magnus et al. (2010). Unlike standard pretest estimators which are based on some preliminary diagnostic test, these model averaging estimators provide a coherent way of making inference on the regression parameters of interest by taking into account the uncertainty due to both the estimation and the model selection steps. Special emphasis is given to a number practical issues that users are likely to face in applied work: equivariance to certain transformations of the explanatory variables, stability, accuracy, computing speed and out-of-memory problems. Performances of our bma and wals commands are illustrated using simulated data and empirical applications from the literature on model averaging estimation.

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Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market

Cited by 44 publications

References 32 publications

A comparison of two model averaging techniques with an application to growth empirics

A comparison of two model averaging techniques with an application to growth empirics

Model averaging based on Kullback-Leibler distance

Bayesian Model Averaging and Weighted Average Least Squares: Equivariance, Stability, and Numerical Issues

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