A regression model of the heteroscedastic error variance

We introduce a regression model of the heteroscedastic error variance. A repetitive use of the least squares method is shown to provide the best linear unbiased estimator of the parameter vector of the model.     

Efficiency of the OLSE for regressions on two-dimensional grids with sinusoidal regressors and spatially correlated errors

For spatial regressions with sinusoidal surfaces, the ordinary least squares estimator (OLSE) is shown to be asymptotically as efficient as the generalized least squares estimator (GLSE) in that the covariance matrices of the two estimators have the same nontrivial limit under the same normalization.     

Motivational antecedents of leader-member exchange differentiation: Evidence from South Korea

Researchers have focused on the group-level leader-member exchange (LMX) differentiation in order to develop a more comprehensive view of within-group variability of LMX. Although the group-level LMX differentiation has been thought to have significant impacts on both individual and group outcomes, we know little about what makes leaders have differential relationships with their members. Drawing on both motives theory and interpersonal interaction theory, we examined the antecedents of LMX differentiation in a sample of 94 group leaders and 553 members of design companies in South Korea. Results indicated that whereas leaders high in affiliation motives had less differential relationships, leaders high in power motives had more differential relationships. We also found that the negative relationship between leaders’ affiliation motives and LMX differentiation was stronger when members’ affiliation and power motives were higher. Theoretical and practical implications are discussed.     

Nonstationary nonlinear heteroskedasticity in regression

This paper considers the regression with errors having nonstationary nonlinear heteroskedasticity. For both the usual stationary regression and the nonstationary cointegrating regression, we develop the asymptotic theories for the least squares methods in the presence of conditional heterogeneity given as a nonlinear function of an integrated process. In particular, we show that the nonstationarity of volatility in the regression errors may induce spuriousness of the underlying regression, if excessive nonstationary volatility is present in the errors. Mild nonstationary volatilities do not render the underlying regression spurious, but their presence makes the least squares estimator asymptotically biased and inefficient and the usual chi-square test invalid.     

Why Do Landlords Include Utilities in Rent? Evidence from the 2000 Housing Discrimination Study (HDS) and the 2002 American Housing Survey (AHS)

This study assesses the decision to include utilities in the rent and the effect of this decision on rents. We utilize individual house-level data from the 2000 Housing Discrimination Study (HDS) and 2002 American Housing Survey (AHS), and determine that the cost of meter installation is not the only reason that utility costs are included in rent. We find evidence that landlords include utilities in the rent to attract more customers. Additionally, our findings demonstrate that utility inclusion behavior differs significantly between old and new building owners. For the hedonic rent estimation, we find that the decision to include the cost of utilities in the rent raises rent.     

Nonstationary nonlinear heteroskedasticity

In this paper, we consider time series with the conditional heteroskedasticities that are given by nonlinear functions of integrated processes. Such time series are said to have nonlinear nonstationary heteroskedasticity (NNH), and the functions generating conditional heterogeneity are called heterogeneity generating functions (HGF's). Various statistical properties of time series with NNH are investigated for a wide class of HGF's. For NNH models with a variety of HGF's, volatility clustering and leptokurtosis, which are common features of ARCH type models, are manifest. In particular, it is shown that the sample autocorrelations of their squared processes vanish only very slowly, or do not even vanish at all, in the limit. Volatility clustering is therefore well expected. The NNH models with certain types of HGF's indeed have sample characteristics that are very similar to those of ARCH type models. Moreover, the sample kurtosis of the NNH model either diverges or has a stable limiting distribution with support truncated on the left by the kurtosis of the innovations. This would well explain the presence of leptokurtosis in many observed time series data. To illustrate the empirical relevancy of our model, we analyze the spreads between the forward and spot rates of USD/DM exchange rates. It is found that the conditional variances of the spreads can be well modelled as a nonlinear function of the levels of the spot rates.