0000000000071082
AUTHOR
Alois Kneip
showing 5 related works from this author
Common functional component modelling
2005
Functional data analysis (FDA) has become a popular technique in applied statistics. In particular, this methodology has received considerable attention in recent studies in empirical finance. In this talk we discuss selected topics of functional principal components analysis that are motivated by financial data.
On Behavioral Heterogeneity
2006
An index of “behavioral heterogeneity” for every finite population of households is defined. It is shown that the higher the index of behavioral heterogeneity the less sensitive depends the aggregate consumption expenditure ratio upon prices. As a consequence, a high index implies a tendency for the Jacobian of aggregate demand to have a dominant negative diagonal.
Functional Data Analysis and Mixed Effect Models
2004
Panel studies in econometrics as well as longitudinal studies in biomedical applications provide data from a sample of individual units where each unit is observed repeatedly over time (age, etc.). In this context, mixed effect models are often applied to analyze the behavior of a response variable in dependence of a number of covariates. In some important applications it is necessary to assume that individual effects vary over time (age, etc.).
Time Trends in the Joint Distributions of Income and Age
2001
We propose a method of analyzing time changes of joint income-age densities. Change is decomposed into time invariant components which act on the densities as deformations with time varying strength. The functional form of these components is estimated non parametrically from cross sectional data. The method is applied to analyze British household data on income and age for the years 1968–95. It is learned that for the young and middle aged there is a trend towards increasing inequality, while during the early eighties there seems to occur a reversal in the evolution of the income distribution for the old.
Aggregate Behavior and Microdata
2004
Abstract It is shown how one can effectively use microdata in modelling the change over time in an aggregate (e.g. mean consumption expenditure) of a large and heterogeneous population. The starting point of our aggregation analysis is a specification of explanatory variables on the micro-level. Typically, some of these explanatory variables are observable and others are unobservable. Based on certain hypotheses on the evolution over time of the joint distributions across the population of these explanatory variables we derive a decomposition of the change in the aggregate which allows a partial analysis: to isolate and to quantify the effect of a change in the observable explanatory variab…