Abstract
Chapter one establishes conditions for nonparametric identification of structural components of the collective household model, as for example the conditional sharing rule. In particular it deals with the nonseparable nature of observed demands with respect to unobserved heterogeneity, which arises as a consequence of the bargaining structure of the model. As a result, this allows researches to answer welfarerelated questions on an individual level for a heterogeneous population.
Chapter two deals with the Collective Axiom of Revealed Preference also in the context of unobserved heterogeneity and shows how one can exploit data from single households in a nonparametric setting to study the empirical validity of the collective axiom. This approach makes use of a finitedimensional characterization of demands and shows how one can test the collective model or the assumption of preference stability with respect to household composition using a partialidentification approach.
Chapter three treats the estimation of Value at Risk in the context of financial time series. To be more precise, it is shown how one can directly estimate a smooth transition generalized conditional quantile model which allows for asymmetric responses to past innovations such as different dynamic behaviour succeeding negative and positive news. The model is generalized in a sense that it may depend on past conditional volatilities for which an auxiliary estimator is developed based on composite quantile regression.
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Award date  18 Nov 2016 
Place of Publication  Tilburg 
Publisher  
Print ISBNs  978 90 5668 489 1 
Publication status  Published  2016 
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Topics in nonparametric identification and estimation. / Hubner, Stefan.
Tilburg : CentER, Center for Economic Research, 2016. 149 p.Research output: Thesis › Doctoral Thesis › Scientific
TY  THES
T1  Topics in nonparametric identification and estimation
AU  Hubner, Stefan
PY  2016
Y1  2016
N2  This dissertation consists of three chapters in which nonparametric methods are developed to estimate econometric models in different contexts. Chapter one and two focus on the collective household consumption model, whereas Chapter three considers the estimation of a smooth transition conditional quantile model in a financial time series context. They all have in common that the distribution of the model's unobserved components as well as some or all of the model's primitives are identified nonparametrically. Chapter one establishes conditions for nonparametric identification of structural components of the collective household model, as for example the conditional sharing rule. In particular it deals with the nonseparable nature of observed demands with respect to unobserved heterogeneity, which arises as a consequence of the bargaining structure of the model. As a result, this allows researches to answer welfarerelated questions on an individual level for a heterogeneous population.Chapter two deals with the Collective Axiom of Revealed Preference also in the context of unobserved heterogeneity and shows how one can exploit data from single households in a nonparametric setting to study the empirical validity of the collective axiom. This approach makes use of a finitedimensional characterization of demands and shows how one can test the collective model or the assumption of preference stability with respect to household composition using a partialidentification approach.Chapter three treats the estimation of Value at Risk in the context of financial time series. To be more precise, it is shown how one can directly estimate a smooth transition generalized conditional quantile model which allows for asymmetric responses to past innovations such as different dynamic behaviour succeeding negative and positive news. The model is generalized in a sense that it may depend on past conditional volatilities for which an auxiliary estimator is developed based on composite quantile regression.
AB  This dissertation consists of three chapters in which nonparametric methods are developed to estimate econometric models in different contexts. Chapter one and two focus on the collective household consumption model, whereas Chapter three considers the estimation of a smooth transition conditional quantile model in a financial time series context. They all have in common that the distribution of the model's unobserved components as well as some or all of the model's primitives are identified nonparametrically. Chapter one establishes conditions for nonparametric identification of structural components of the collective household model, as for example the conditional sharing rule. In particular it deals with the nonseparable nature of observed demands with respect to unobserved heterogeneity, which arises as a consequence of the bargaining structure of the model. As a result, this allows researches to answer welfarerelated questions on an individual level for a heterogeneous population.Chapter two deals with the Collective Axiom of Revealed Preference also in the context of unobserved heterogeneity and shows how one can exploit data from single households in a nonparametric setting to study the empirical validity of the collective axiom. This approach makes use of a finitedimensional characterization of demands and shows how one can test the collective model or the assumption of preference stability with respect to household composition using a partialidentification approach.Chapter three treats the estimation of Value at Risk in the context of financial time series. To be more precise, it is shown how one can directly estimate a smooth transition generalized conditional quantile model which allows for asymmetric responses to past innovations such as different dynamic behaviour succeeding negative and positive news. The model is generalized in a sense that it may depend on past conditional volatilities for which an auxiliary estimator is developed based on composite quantile regression.
M3  Doctoral Thesis
SN  978 90 5668 489 1
T3  CentER Dissertation Series
PB  CentER, Center for Economic Research
CY  Tilburg
ER 