A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices

Abstract Land price studies typically employ hedonic analysis to identify the impact of land characteristics on price. Owing to the spatial fixity of land, however, the question of possible spatial dependence in agricultural land prices arises. The presence of spatial dependence in agricultural land prices can have serious consequences for the hedonic model analysis. Ignoring spatial autocorrelation can lead to biased estimates in land price hedonic models. We propose using a flexible quantile regression-based estimation of the spatial lag hedonic model allowing for varying effects of the characteristics and, more importantly, varying degrees of spatial autocorrelation. In applying this approach to a sample of agricultural land sales in Northern Ireland we find that the market effectively consists of two relatively separate segments. The larger of these two segments conforms to the conventional hedonic model with no spatial lag dependence, while the smaller, much thinner market segment exhibits considerable spatial lag dependence. Un mod�le h�donique � r�gression quantile spatiale des prix des terrains agricoles R�sum� Les �tudes sur le prix des terrains font g�n�ralement usage d'une analyse h�donique pour identifier l'impact des caract�ristiques des terrains sur le prix. Toutefois, du fait de la fixit� spatiale des terrains, la question d'une �ventuelle d�pendance spatiale sur la valeur des terrains agricoles se pose. L'existence d'une d�pendance spatiale dans le prix des terrains agricoles peut avoir des cons�quences importantes sur l'analyse du mod�le h�donique. En ignorant cette corr�lation s�rielle, on s'expose au risque d'�valuations biais�es des mod�les h�doniques du prix des terrains. Nous proposons l'emploi d'une estimation � base de r�gression flexible du mod�le h�donique � d�calage spatial, tenant compte de diff�rents effets des caract�ristiques, et surtout de diff�rents degr�s de corr�lations s�rielles spatiales. En appliquant ce principe � un �chantillon de ventes de terrains agricoles en Irlande du Nord, nous d�couvrons que le march� se compose de deux segments relativement distincts. Le plus important de ces deux segments est conforme au mod�le h�donique traditionnel, sans d�pendance du d�calage spatial, tandis que le deuxi�me segment du march�, plus petit et beaucoup plus �troit, pr�sente une d�pendance consid�rable du d�calage spatial. Un modelo hed�nico de regresi�n cuantil espacial de los precios del terreno agr�cola Resumen T�picamente, los estudios del precio de la tierra emplean un an�lisis hed�nico para identificar el impacto de las caracter�sticas de la tierra sobre el precio. No obstante, debido a la fijeza espacial de la tierra, surge la cuesti�n de una posible dependencia espacial en los precios del terreno agr�cola. La presencia de dependencia espacial en los precios del terreno agr�cola puede tener consecuencias graves para el modelo de an�lisis hed�nico. Ignorar la autocorrelaci�n espacial puede conducir a estimados parciales en los modelos hed�nicos del precio de la tierra. Proponemos el uso de una valoraci�n basada en una regresi�n cuantil flexible del modelo hed�nico del lapso espacial que tenga en cuenta los diversos efectos de las caracter�sticas y, particularmente, los diversos grados de autocorrelaci�n espacial. Al aplicar este planteamiento a una muestra de ventas de terreno agr�cola en Irlanda del Norte, descubrimos que el mercado consiste efectivamente de dos segmento relativamente separados. El m�s grande de estos dos segmentos se ajusta al modelo hed�nico convencional sin dependencia del lapso espacial, mientras que el segmento m�s peque�o, y mucho m�s fino, muestra una dependencia considerable del lapso espacial.Spatial lag, quantile regression, hedonic model, C13, C14, C21, Q24,

Convergence: A Story of Quantiles and Spillovers

In this paper, we revisit the analysis of cross-country convergence by combining spatial econometrics and panel quantile regressions to estimate conditional -convergence models. Moreover, we use both exogenous and endogenous weight matrices. Our results show that indeed the effects of initial per capita income, investment rate, population growth and human capital on growth rates vary considerably across the estimated quantiles. Convergence is not a generalized phenomenon across the conditional growth distribution. Moreover, while using exogenous spatial weight matrices does not substantially alter the findings found in a-spatial models, it appears that endogenous weights dramatically affect the estimates of the convergence process

New results on the influence of climate on the distribution of population and economic activity

This paper applies G-Econ+, an updated version of the G-Econ database by Nordhaus, to analyze the influence of climatic and geographic factors on the geographic distribution of population and economic activity. I discuss options for improved treatment of several statistical problems associated with G-Econ, which are not addressed adequately in the original G-Econ analysis. Reanalysis of key results from the original G-Econ analysis corrects some surprising results therein. Extensive sensitivity analysis determines the robustness of the relationship between climatic factors and economic activity across alternative central estimators. Further analysis assesses revealed climatic preferences of population, the effects of climate parameters on different quantiles of economic variables, and synergies between temperature and precipitation. I find that population density has a much stronger influence on output density than output per capita. Furthermore, least developed countries are located in a climatic zone where all indicators of economic activity decline with increasing temperature.Climate; macroeconomics; population; cross-sectional analysis; G-Econ

Specification Testing in Econometric Models

The thesis consits of three independent articles. First, specification tests for the m-dimensional spatial autoregressive (SAR) panel model are provided. Therefore, we derive the limiting distribution of the specification test statistics and examine size and power properties in finite sample simulations. In the empirical application we analyze the Euro Stoxx 50 returns. Regarding this, a 3-dimensional SAR panel model incorporating global dependencies, dependencies inside industrial branches and local dependencies is assumed. The investigation shows the tests’ ability to detect inaccurate Value-at-Risk forecasts. Secondly, we propose a new non-parametric test for detecting relevant breaks in copula functions. We assume that the data is driven by two non-equal copulas C1 and C2. Under the null hypothesis, the copula difference within an appropriate norm is smaller than a certain positive adjustable threshold . Within the alternative hypothesis, the copula difference exceeds the fixed value. The test is based on a cumulative sum approach of the empirical copula with sequentially estimated marginals. We propose a bootstrap procedure to compute critical values. The Monte Carlo simulation study indicates that the test results in a reasonable sized and powered testing procedure. A real data application of the DAX30 up to cross sectional dimension N = 30 shows the test’s ability to detect relevant break points. Finally, we propose a novel consistent specification test for quantile regression models where we allow the covariate effects to be quantile dependent and nonlinear. To achieve this, we parameterize the conditional quantile functions by appropriate basis functions, rather than parametrically and hence allowing to test for functional forms beyond linearity while retaining the linear cases as special cases. Due to the dependence on the quantile itself covariate-quantile relations can differ for distinct quantiles. The induced class of conditional distribution functions can finally be tested with a Cramér-von Mises type test statistic. We derive the theoretical limit distribution and propose a practical bootstrap method. To increase the power of our test, we suggest a modified test statistic using quantile regression splines. A detailed Monte Carlo experiment shows that the test results in a reasonable sized testing procedure with large power. An application to conditional income disparities between East and West Germany over the period 2001 − 2010 indicates that there are still significant differences across the quantiles of the conditional income distributions, when conditioning on age

What role for human capital in the growth process: new evidence from endogenous latent factor panel quantile regressions

The estimates for the human capital effect in cross-country growth regressions have been subject of considerable controversy. We argue that human capital is intrinsically a multidimensional construct. We construct human capital measure by combining available alternative proxies via confirmatory factor analysis. Using panel data endogenous quantile regression methods we analyse the whole conditional growth distribution by simultaneously accounting for the potential endogeneity of human capital and country specific effects. Our results conform to theoretical expectations and we are able to demonstrate the beneficial effect of both the measurement approach and the endogeneity correction on the derivation of theoretically consistent estimates

Immigrants and the U.S. Wage Distribution

A large body of literature estimates the relative wage impacts of immigration on low- and high-skill natives, but it is unclear how these effects map onto changes of the wage distribution. I document the movement of foreign-born workers in the U.S. wage distribution, showing that, since 1980, they have become increasingly overrepresented in the bottom. Downgrading of education and experience obtained abroad partially drives this pattern. I then undertake two empirical approaches to deepen our understanding of the way foreign-born workers shape the wage structure. First, I estimate a standard theoretical model featuring constant elasticity of substitution technology and skill types stratified across wage deciles. Second, I estimate reduced-form quantile treatment effects by constructing a ceteris paribus counterfactual wage distribution with lower immigration levels. Both analyses uncover a similar monotone pattern: a one percentage point increase in the share of foreign-born leads to a 0.2–0.3 (0.2–0.4) percent wage decrease (increase) in the bottom (top) decile and asserts no significant pressure in the middle. When analyzing the drivers of this pattern, I find suggestive evidence for a novel mechanism through which local labor markets absorb foreign-born workers: occupational differentiation of immigrants relative to natives

The Geography of Average Income and Inequality: Spatial Evidence from Austria

This paper investigates the nexus between regional income levels and inequality. We present a novel small-scale inequality database for Austrian municipalities to address this question. Our dataset combines individual tax data of Austrian wage tax payer on regionally disaggregated scale with census and geographical information. This setting allows us to investigate regional spillover effects of average income and various measures of income inequality. Using this data set we find distinct regional clusters of both high average wages and high earnings inequality in Austria. Furthermore we use spatial econometric regressions to quantify the effects between income levels and a number of inequality measures such as the Gini and 90/10 quantile ratios. (authors' abstract)Series: Department of Economics Working Paper Serie