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This paper investigates the impact of energy efficiency along the unconditional distribution of residential property prices in Portugal. Using a dataset of more than 256,000 residential property sales from 2009 to 2013, a period that covers an economic depression, unconditional quantile regression analysis reveals that the responsiveness to energy efficiency improvements is different not only as we move from low- to high-priced residential units but also for apartments and houses. While the former show a downward trend in the magnitude of energy efficiency coefficient estimates, the opposite occurs for houses. The latter market segment exhibits clear market discounts at the lowest quantiles of the price distribution, something that is not observable thought conditional mean and quantile regression analysis. Results suggest the existence of a different responsiveness to energy efficiency improvements in the Lisbon region when compared to the rest of the country and that the impact of the Energy Performance Certificate label increases throughout time across all price quantiles. As a by-product of this paper, an unconditional quantile price index shows that the impact of the crisis was not the same across the different market segments, with price decreases being more severe for low- than high-priced properties. info:eu-repo/semantics/publishedVersion

Using a unique dataset containing information of around 256 thousand residential property sales, this paper discloses a clear sales premium for most energy efficient dwellings, which is more pronounced for apartments (13%) than for houses (5 to 6%). Cross-country comparisons support the finding that energy efficiency price premiums are higher in the Portuguese residential market than in central and northern European markets. Results emphasize the relevance of data issues in hedonic regression models. They illustrate how the use of appraisal prices, explanatory variables with measurement errors, and the omission of variables associated with the quality of the properties, may seriously bias energy efficiency partial effect estimates. These findings provide valuable information not only to policy-makers, but also to researchers interested in this area. info:eu-repo/semantics/publishedVersion

A test for heteroskedasticity within the context of classical linear regression can be based on the difference between Wald statistics in heteroskedasticity-robust and nonrobust forms. The resulting statistic is asymptotically distributed under the null hypothesis of homoskedasticity as chi-squared with one degree of freedom. The power of this test is sensitive to the choice of parametric restriction on which the Wald statistics are based, so the supremum of a range of individual test statistics is proposed. Two versions of a supremum-based test are considered: the first version, easier to implement, does not have a known asymptotic null distribution, so the bootstrap is employed in order to assess its behaviour and enable meaningful conclusions from its use in applied work. The second version has a known asymptotic distribution and, in some cases, is asymptotically pivotal under the null. A small simulation study illustrates the implementation and finite-sample performance of both versions of the test.

This paper provides an integrated approach for estimating parametric models from endogenous stratified samples. We discuss several alternative ways of removing the bias of the moment indicators usually employed under random sampling for estimating the parameters of the structural model and the proportion of the strata in the population. Those alternatives give rise to a bunch of moment-based estimators which are appropriate for both cases where the marginal strata probabilities are known and unknown. The derivation of our estimators is very simple and intuitive and incorporates as particular cases most of the likelihood-based estimators existing in the literature.

Empirical likelihood (EL) is appropriate to estimate moment condition models when a random sample from the target population is available. However, many economic surveys are subject to some form of stratification, in which case direct application of EL will produce inconsistent estimators. In this paper we propose a two-step EL (TSEL) estimator to deal with stratified samples in models defined by unconditional moment restrictions in presence of some aggregate information, which may consist, for example, of the mean and the variance of the variable of interest and/or the explanatory variables. A Monte Carlo simulation study reveals promising results for many versions of the TSEL estimator.

In this paper we propose a general framework to deal with datasets where a binary outcome is subject to misclassification and, for some sampling units, neither the error-prone variable of interest nor the covariates are recorded. A model to describe the observed data is for-malized and eficient likelihood-based generalized method of moments (GMM) estimators are suggested. These estimators merely require the formulation of the conditional distribution of the latent outcome given the covariates. The conditional probabilities which describe the error and the nonresponse mechanisms are estimated simultaneously with the parameters of inter-est. In a small Monte Carlo simulation study our GMM estimators revealed a very promising performance.

In this paper we propose a general framework to deal with the presence of covariate mea-surement error in endogenous stratifield samples. Using Chesher’s (2000) methodology, we develop approximately consistent estimators for the parameters of the structural model, in the sense that their inconsistency is of smaller order than that of the conventional estimators which ignore the existence of covariate measurement error. The approximate bias corrected estimators are obtained by applying the generalized method of moments (GMM) to a modifeld version of the moment indicators suggested by Imbens and Lancaster (1996) for endogenous stratified samples. Only the specification of the conditional distribution of the response vari-able given the latent covariates and the classical additive measurement error model assumption are required, the availability of information on both the marginal probability of the strata in the population and the variance of the measurement error not being essential. A score test to detect the presence of covariate measurement error arises as a by-product of this approach. Monte Carlo evidence is presented which suggests that, in endogenous stratified samples of moderate sizes, the modified GMM estimators perform well.