66 research outputs found
Explaining individual response using aggregated data
Empirical analysis of individual response behavior is sometimes
limited due to the lack of explanatory variables at the individual
level. In this paper we put forward a new approach to estimate the
effects of covariates on individual response, where the covariates
are unknown at the individual level but observed at some aggregated
level. This situation may, for example, occur if the response
variable is available at the household level but covariates only at
the zip-code level.
We describe the missing individual covariates by a latent variable
model which matches the sample information at the aggregate level.
Parameter estimates can be obtained using maximum likelihood or a
Bayesian approach. We illustrate the approach estimating the effects
of household characteristics on donating behavior to a Dutch
charity. Donating behavior is observed at the household level, while
the covariates are only observed at the zip-code level
Essays on Finite Mixture Models
Finite mixture distributions are a weighted average of a ¯nite number of distributions.
The latter are usually called the mixture components. The weights are usually described
by a multinomial distribution and are sometimes called mixing proportions. The mixture
components may be the same type of distributions with di®erent parameter values but
they may also be completely di®erent distributions (Everitt and Hand, 1981; Titterington
et al., 1985). Therefore, ¯nite mixture distributions are very °exible for modeling data.
They are frequently used as a building block within many modern econometric models.
The speci¯cation of the mixture distribution depends on the modeling problem at hand.
In this thesis, we introduce new applications of ¯nite mixtures to deal with several
di®erent modeling issues. Each chapter of the thesis focusses on a speci¯c modeling
issue. The parameters of some of the resulting models can be estimated using standard
techniques but for some of the chapters we need to develop new estimation and inference
methods. To illustrate how the methods can be applied, we analyze at least one empirical
data set for each approach. These data sets cover a wide range of research ¯elds, such as
macroeconomics, marketing, and political science. We show the usefulness of the methods
and, in some cases, the improvement over previous methods in the literature
Modeling regional house prices
We develop a parsimonious panel model for quarterly regional house prices, for which both the cross-section and the time series dimension is large. The model allows for stochastic trends, cointegration, cross-equation correlations and, most importantly, latent-class clustering of regions. Class membership is fully data-driven and based on (i) average growth rates of house prices, (ii) the propagation of shocks to house prices across regions, also known as the ripple effect, and (iii) the relationship of house prices with economic growth and other variables. Applying the model to quarterly data for the Netherlands, we find convincing evidence for the existence of two distinct clusters of regions, with pronounced differences in house price dynamics
A Bayesian approach to two-mode clustering
We develop a new Bayesian
approach to estimate the parameters of a latent-class model for the joint clustering of both modes of two-mode data matrices. Posterior results are obtained using a Gibbs sampler with data augmentation.
Our Bayesian approach has three advantages over existing methods.
First, we are able to do statistical inference on the model parameters, which would not be possible using frequentist estimation procedures. In addition, the Bayesian approach allows us to provide statistical criteria for determining the optimal numbers of clusters. Finally, our Gibbs sampler has fewer problems with local optima in the likelihood function and empty classes than the EM algorithm used in a frequentist approach. We apply the Bayesian estimation method of the latent-class two-mode clustering model to two empirical data sets. The first data set is the Supreme Court voting data set of Doreian, Batagelj, and Ferligoj (2004). The second data set comprises the roll call votes of the United States House
Labour market transitions and job satisfaction
The paper investigates the relationship between job satisfaction and labour market transitions. Using a multinomial logit model, a model is estimated on the basis of individual data in which transitions are explained from individual characteristics, job characteristics, dissatisfaction with the job and discrepancies between the actual and the desired number of hours worked. Transitions can be changes in the hours worked, changes to a different job and/or employers, or combinations. Furthermore, people may lose their job and leave employment out of free will. The model has been estimated for three categories of workers according to the number of hours worked. The results show that both dissatisfaction with the job and discrepancies with respect to the hours worked have a significant impact on transition probabilities. Contrary to what is sometimes believed there is no structural increase in transition probabilities. We are still far away from a ‘transtional labour market’. The paper also shows that transitions significantly increase job satisfaction. However, despite the strong improvement in the labour market situation in the 1990s, the percentage of the workers experiencing a dscrepancy between the actual and the desired number of hours has not diminished
ChiSCor: A Corpus of Freely Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science
In this resource paper we release ChiSCor, a new corpus containing 619
fantasy stories, told freely by 442 Dutch children aged 4-12. ChiSCor was
compiled for studying how children render character perspectives, and
unravelling language and cognition in development, with computational tools.
Unlike existing resources, ChiSCor's stories were produced in natural contexts,
in line with recent calls for more ecologically valid datasets. ChiSCor hosts
text, audio, and annotations for character complexity and linguistic
complexity. Additional metadata (e.g. education of caregivers) is available for
one third of the Dutch children. ChiSCor also includes a small set of 62
English stories. This paper details how ChiSCor was compiled and shows its
potential for future work with three brief case studies: i) we show that the
syntactic complexity of stories is strikingly stable across children's ages;
ii) we extend work on Zipfian distributions in free speech and show that
ChiSCor obeys Zipf's law closely, reflecting its social context; iii) we show
that even though ChiSCor is relatively small, the corpus is rich enough to
train informative lemma vectors that allow us to analyse children's language
use. We end with a reflection on the value of narrative datasets in
computational linguistics.Comment: 12 pages, 5 figures, forthcoming in Proceedings of the 27th
Conference on Computational Natural Language Learning (CoNLL
Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding
Current Large Language Models (LLMs) are unparalleled in their ability to
generate grammatically correct, fluent text. LLMs are appearing rapidly, and
debates on LLM capacities have taken off, but reflection is lagging behind.
Thus, in this position paper, we first zoom in on the debate and critically
assess three points recurring in critiques of LLM capacities: i) that LLMs only
parrot statistical patterns in the training data; ii) that LLMs master formal
but not functional language competence; and iii) that language learning in LLMs
cannot inform human language learning. Drawing on empirical and theoretical
arguments, we show that these points need more nuance. Second, we outline a
pragmatic perspective on the issue of `real' understanding and intentionality
in LLMs. Understanding and intentionality pertain to unobservable mental states
we attribute to other humans because they have pragmatic value: they allow us
to abstract away from complex underlying mechanics and predict behaviour
effectively. We reflect on the circumstances under which it would make sense
for humans to similarly attribute mental states to LLMs, thereby outlining a
pragmatic philosophical context for LLMs as an increasingly prominent
technology in society.Comment: 15 pages, 0 figures, Forthcoming in Proceedings of the 2023
Conference on Empirical Methods in Natural Language Processin
Informing, simulating experience, or both: A field experiment on phishing risks
Cybersecurity cannot be ensured with mere technical solutions. Hackers often use fraudulent emails to simply ask people for their password to breach into organizations. This technique, called phishing, is a major threat for many organizations. A typical prevention measure is to inform employees but is there a better way to reduce phishing risks? Experience and feedback have often been claimed to be effective in helping people make better decisions. In a large field experiment involving more than 10,000 employees of a Dutch ministry, we tested the effect of information provision, simulated experience, and their combination to reduce the risks of falling into a phishing attack. Both approaches substantially reduced the proportion of employees giving away their password. Combining both interventions did not have a larger impact
Estimating non-response bias in a survey on alcohol consumption: comparison of response waves
AIMS: According to 'the continuum of resistance model' late respondents
can be used as a proxy for non-respondents in estimating non-response
bias. In the present study, the validity of this model was explored and
tested in three surveys on alcohol consumption. METHODS: The three studies
collected their data by means of mailed questionnaires on alcohol
consumption whereby two studies also performed a non-response follow-up.
RESULTS: Comparisons of early respondents, late respondents and
non-respondents in one study showed some support for 'the continuum of
resistance model', although another study could not confirm this result.
Comparison of alcohol consumption between three time response groups
showed no significant linear pattern of differences between response
waves. CONCLUSIONS: The hypothesis that late respondents are more similar
to non-respondents than early respondents, could not be confirmed or
rejected. Repeated mailings are effective in obtaining a greater sample
size, but seem ineffective in improving the representativeness of alcohol
consumption surveys
Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests
To what degree should we ascribe cognitive capacities to Large Language
Models (LLMs), such as the ability to reason about intentions and beliefs known
as Theory of Mind (ToM)? Here we add to this emerging debate by (i) testing 11
base- and instruction-tuned LLMs on capabilities relevant to ToM beyond the
dominant false-belief paradigm, including non-literal language usage and
recursive intentionality; (ii) using newly rewritten versions of standardized
tests to gauge LLMs' robustness; (iii) prompting and scoring for open besides
closed questions; and (iv) benchmarking LLM performance against that of
children aged 7-10 on the same tasks. We find that instruction-tuned LLMs from
the GPT family outperform other models, and often also children. Base-LLMs are
mostly unable to solve ToM tasks, even with specialized prompting. We suggest
that the interlinked evolution and development of language and ToM may help
explain what instruction-tuning adds: rewarding cooperative communication that
takes into account interlocutor and context. We conclude by arguing for a
nuanced perspective on ToM in LLMs.Comment: 14 pages, 4 figures, Forthcoming in Proceedings of the 27th
Conference on Computational Natural Language Learning (CoNLL
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