Asymptotics of the regression quantile basic solution under misspecification

Knight, Keith

doi:10.1007/s10492-008-0006-0

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Additionally, it allows dealing with the error measurement problems. Since Koenker and Bassett (1978) and Bassett and Koenker (1978), an extensive literature has studied the asymptotic behaviour of the quantile regression (see, for instance, Weiss [1990]; Portnoy [1991]; Knight [2008]).…”

Section: Methodsmentioning

confidence: 99%

Does Corporate Tax Burden Affect Growth? Evidences from OECD Countries

Dima

2017

Journal of Heterodox Economics

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This paper explores the tax burden - economic growth nexus. It advances an explanatory framework for the existence of such nexus. First, we argue that tax burden reduces the income remaining at the disposal of the private sector. Second, we empirically test for the existence of a non-linear impact of corporate tax burden on growth for a dataset of 21 OECD countries, for the period 1975 - 2012. We mostly involve mixed effects models with three levels of nested random effects. Our main empirical result consists in the evidences of a non-linear relation between corporate tax burden and economic growth.

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Section: Methodsmentioning

confidence: 99%

Does Corporate Tax Burden Affect Growth? Evidences from OECD Countries

Dima

2017

Journal of Heterodox Economics

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“…To establish rates of convergence of the estimator obtained in Step 1 we will apply Lemma 1. Consider the events Ω 1 , Ω 2 , and Ω 3 as defined in (D. 49)…”

Section: Degreementioning

confidence: 99%

Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk

Belloni¹,

Chen²,

Chernozhukov³

2016

Preprint

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The understanding of co-movements, dependence, and influence between variables of interest is key in many applications. Broadly speaking such understanding can lead to better predictions and decision making in many settings. We propose Quantile Graphical Models (QGMs) to characterize prediction and conditional independence relationships within a set of random variables of interest. Although those models are of interest in a variety of applications, we draw our motivation and contribute to the financial risk management literature. Importantly, the proposed framework is intended to be applied to non-Gaussian settings, which are ubiquitous in many real applications, and to handle a large number of variables and conditioning events.We propose two distinct QGMs. First, Condition Independence Quantile Graphical Models (CIQGMs)characterize conditional independence at each quantile index revealing the distributional dependence structure. Second, Prediction Quantile Graphical Models (PQGMs) characterize the best linear predictor under asymmetric loss functions. A key difference between those models is the (non-vanishing) misspecification between the best linear predictor and the conditional quantile functions. We also propose estimators for those QGMs. Due to high-dimensionality, the two distinct QGMs require different estimators. The estimators are based on high-dimensional techniques including (a continuum of) 1 -penalized quantile regressions (and low biased equations), which allow us to handle the potential large number of variables. We build upon a recent literature to obtain new results for valid choice of the penalty parameters, rates of convergence, and confidence regions that are simultaneously valid.We illustrate how to use QGMs to quantify tail interdependence (instead of mean dependence) between a large set of variables which is relevant in applications concerning with extreme events. We show that the associated tail risk network can be used for measuring systemic risk contributions. We also apply the framework to study international financial contagion and the impact of market downside movement on the dependence structure of assets' returns.

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“…It deals as well with error measurement problems. Starting with the seminal work of Koenker and Basset (1978), respectively Basset and , an extensive literature has studied the asymptotic behaviour of the quantile regression (see, for instance , Weiss 1990;Portnoy 1991;Knight 2008).…”

Section: Quantile Regressionsmentioning

confidence: 99%

Income Distribution and Social Tolerance

Dima

2015

Soc Indic Res

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Social tolerance, i.e. the tolerance for the intrinsic diversity of large social groups, can be viewed as a synergic effect of the features of both individuals and socioeconomic environment. This paper proposes a twofold contribution to the literature. First, it advances a conceptual framework in which tolerance at individual levels is explained by social polarization-in the form of income distribution-and the perceived quality of the social relationships and structures. Second, the regression analysis-involving micro-data from World Values Survey covering a time span between 2010 and 2014, for 48 countries-provides robust evidences for a non-linear impact of income distribution on social tolerance. This impact appears to be U-shaped and displays a pronounced degree of asymmetry. Also, labour market position, education, class self-identification, gender, age, marital status, levels of individuals' personal security and sharing of post-materialist values matter in explaining social tolerance. The same main determinants contribute to the formation of both social tolerance and social capital. The differences are related to the amplitude as well as to the degree of symmetry for the corresponding non-linear transmission channels.

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Asymptotics of the regression quantile basic solution under misspecification

Cited by 8 publications

References 21 publications

Does Corporate Tax Burden Affect Growth? Evidences from OECD Countries

Does Corporate Tax Burden Affect Growth? Evidences from OECD Countries

Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk

Income Distribution and Social Tolerance

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