Spatial Quantile Regression

Trzpiot, Grażyna

doi:10.2478/v10103-012-0040-8

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…The spatial dimension plays a central role when one wants to address house price dynamics. The spatial component has been introduced only recently in quantile modelling of house prices (Trzpiot 2012;McMillen 2012). An increasing number of studies have used spatial econometrics to control for spatial dependence and spatial heterogeneity (Wan et al 2017).…”

Section: The Use Of Qr-based Models For the House Price Functionmentioning

confidence: 99%

A spatial semiparametric M-quantile regression for hedonic price modelling

et al. 2023

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This paper proposes an M-quantile regression approach to address the heterogeneity of the housing market in a modern European city. We show how M-quantile modelling is a rich and flexible tool for empirical market price data analysis, allowing us to obtain a robust estimation of the hedonic price function whilst accounting for different sources of heterogeneity in market prices. The suggested methodology can generally be used to analyse nonlinear interactions between prices and predictors. In particular, we develop a spatial semiparametric M-quantile model to capture both the potential nonlinear effects of the cultural environment on pricing and spatial trends. In both cases, nonlinearity is introduced into the model using appropriate bases functions. We show how the implicit price associated with the variable that measures cultural amenities can be determined in this semiparametric framework. Our findings show that the effect of several housing attributes and urban amenities differs significantly across the response distribution, suggesting that buyers of lower-priced properties behave differently than buyers of higher-priced properties.

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Section: The Use Of Qr-based Models For the House Price Functionmentioning

confidence: 99%

A spatial semiparametric M-quantile regression for hedonic price modelling

et al. 2023

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“…The QSAR model of order τ blends the two approaches mentioned above. It can be written as follows (Trzpiot, 2012;Kostov, 2009):…”

Section: Quantile Spatial Autoregressive Model -Qsar Modelmentioning

confidence: 99%

The classification of spatial quantile regression models for healthy life years in European countries

Trzpiot

Orwat-Acedańska

2019

Argumenta Oeconomica

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EU countries are heterogeneous in terms of healthy life years (HLY). A quantile spatial autoregression model is employed to identify the factors affecting HLY. Quantile regression allows to measure the impact of covariates on different parts of the conditional distribution of a dependent variable. This is especially useful if the distribution is asymmetric or trimmed. Quantile spatial autoregression models add the spatial dimension to the standard quantile regressions. The aims of the study are as follows: identification of the factors affecting healthy life years of men and women in the EU countries, and clustering of the estimates obtained for the different quantile orders. We analyze to what extent spatial quantile regression can be useful in studying the behavior of HLY. We use exogenous factors belonging to three groups: socio-economic, healthcare and lifestyle. After initial estimation of the models for 19 quantile orders, the insignificant factors are removed and the remaining parameters are re-estimated. The final estimates are then clustered to facilitate the interpretation of the results. Instrumental variables method coupled with bootstrap techniques are employed for estimation and inference while the k-means algorithm is used for clustering. We find that the impact of the factors on HLY varies for different quantile orders. In general, the quantiles of low and medium orders are affected by the factors from all the three groups (socio-economic, healthcare and lifestyle). For the medium and high orders, the socioeconomic factors are no longer important. We argue that the spatial quantile autoregression models offer a new perspective for studying the factors affecting HLY in the EU countries.

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“…At the second stage, the variable W Y is added on 12 Literature refers to this method as "fitted value" approach and applications in a spatial framework can be found in Zietz et al (2008), Liao and Wang (2012) and Kostov (2013). 13 An in-depth analysis on this procedure is in Yang and Su (2007), Kostov (2009) andTrzpiot (2012). 14 To provide robustness to our results, in the next section we estimate our model following the approach developed by Chernozhukov and Hansen (2006).…”

Section: Spatial Quantile Regressionmentioning

confidence: 99%

Does agricultural subsidies foster Italian southern farms? A Spatial Quantile Regression Approach

Castris¹,

Gennaro²

2018

Preprint

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During the last decades, public policies become a central pillar in supporting and stabilising agricultural sector. Since 1962, EU policy-makers developed the so-called Common Agricultural Policy (CAP) to ensure competitiveness and a common market organisation for agricultural products. In 2003, CAP was substantially reformed by the development of a single payment scheme not linked to production (decoupling), but focusing on income stabilization and the sustainability of agricultural sector. Notwithstanding farmers are highly dependent to public support, literature on the role played by the CAP in fostering agricultural performances is still scarce and fragmented, while major efforts are devoted to analyse the relationship between farm technical efficiency and subsidies (Bojnec and Latruffe, 2013;Zhu and Demeter, 2012). Actual CAP policies increases performance differentials between Northern Central EU countries and peripheral regions (Giannakis and Bruggeman, 2015). This paper aims to evaluate the effectiveness of CAP in stimulate performances by focusing on Italian lagged Regions. Moreover, agricultural sector is deeply rooted in place-based production processes. In this sense, economic analysis which omit the presence of spatial dependence produce biased estimates of the performances. Therefore, this paper, using data on subsidies and economic results of farms from the RICA dataset which is part of the Farm Accountancy Data Network (FADN), proposes a spatial Augmented Cobb-Douglas Production Function to evaluate the effects of subsidies on farm's performances. The major innovation in this paper is the implementation of a micro-founded quantile version of a spatial lag model (Kim and Muller, 2004) to examine how the impact of the subsidies may vary across the conditional distribution of agricultural performances. Results show an increasing shape which switch from negative to positive at the median and becomes statistical significant for higher quantiles. Additionally, spatial autocorrelation parameter is positive and significant across all the conditional distribution, suggesting the presence of significant spatial spillovers in agricultural performances.

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Spatial Quantile Regression

Cited by 11 publications

References 10 publications

A spatial semiparametric M-quantile regression for hedonic price modelling

A spatial semiparametric M-quantile regression for hedonic price modelling

The classification of spatial quantile regression models for healthy life years in European countries

Does agricultural subsidies foster Italian southern farms? A Spatial Quantile Regression Approach

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