7,540 research outputs found
An Empirical Model of Industry Dynamics with Common Uncertainty and Learning from the Actions of Competitors
This paper advances our collective knowledge about the role of learning
in retail agglomeration. Uncertainty about new markets provides an
opportunity for sequential learning, where one rm s past entry
decisions signal to others the potential pro tability of risky markets.
The setting is Canada s hamburger fast food industry from its early days
in 1970 to 2005, for which simple analysis of my unique data reveals
empirical patterns pointing towards retail agglomeration. The notion
that uninformed potential entrants have an incentive to learn, but not
informed incumbents, motivates an intuitive double-di¤erence
approach that separately identi es learning by exploiting
di¤erences in the way potential entrants and incumbents react to
spillovers. This identi cation strategy con rms that information
externalities are key drivers of agglomeration. Esti- mates from a
dynamic oligopoly model of entry with information externalities provide
further evidence of learning, as I show that common uncertainty matters.
Counterfac- tual analysis reveals that an industry with uncertainty is
initially less competitive than an industry with certainty, but catches
up over time. Furthermore, there are many instances in which chains
enter markets they would have avoided had they not faced uncertainty.
Finally, consistent with the interpretation of uncertainty as an entry
barrier, I nd that chains place signi cant premiums on certainty at
proportions beyond 2% of their total value from being monopolists
A comparative study of genotype imputation programs
Background Genotype imputation infers missing genotypic data computationally, and has been reported
to be highly useful in various genetic studies; e.g., genome-wide association studies and genomic selection.
Motivation While various genotype imputation programs have been evaluated via different measurements,
some, such as Pearson correlation, may not be appropriate for a given context and may result in misleading
results. Further, most evaluations of genotype imputation programs are focused on human data. Finally, the
most commonly used measurement, concordance, is unable to determine a difference in performance in some
cases.
Research Questions (1) How do popular genotype imputation programs (i.e., Minimac and Beagle) perform
on plant data as compared to human data? (2) Can we find measures that better discriminate imputation
performance when concordance does not? and (3) What do alternate measures indicate for the performance
of these imputation programs?
Methods Since Kullback-Leibler divergence (K-L divergence) and Hellinger distance can aid in ranking
statistical inference methods, they can be highly useful in our study. To amplify signals from K-L divergence
and Hellinger distance, we obtain their negative logarithmic values (i.e., negative logarithmic K-L divergence
(NLKLD) and negative logarithmic Hellinger distance (NLHD)) so that larger values indicate better imputation
results. With NLKLD and NLHD, we investigate the performance of two existing genotype imputation
programs (i.e., Beagle and Minimac) on data from plants, specifically Arabidopsis thaliana and rice, as well
as human. For each pair of organisms to be compared, we select data from one chromosome of each organism
such that approximately the same number of samples/participants and SNPs are present for each organism.
Finally, we apply different missing rates for target datasets and different sample size ratios between reference
and target datasets for sensitivity analysis of the imputation programs.
Results We demonstrate that in a general case where single nucleotide polymorphisms (SNPs) with different
minor allele frequencies (MAFs) are imputed at the same concordance, both NLKLD and NLHD capture a
difference in the imputation performance. Such a difference reflects not only the difference of correspondence
between the known and imputed MAFs, but also the difference of chance agreement between the known
and imputed genotypes. Additionaly, neither Minimac nor Beagle performs better on either A. thaliana or
human data. However, Beagle performs better on human data than on rice data. Finally, the majority of
both NLKLD and NLHD results from all experimental data indicate that Minimac outperforms Beagle.
Conclusions (1) Although neither Minimac nor Beagle consistently performs better on either plant or
human data, Beagle evidently performs better on human data than on rice data; (2) NLKLD and NLHD
can be more discriminating than concordance and should be considered in comparing different genotype
imputation programs to determine superior imputation methods; and (3) the NLKLD and NLHD results
suggest that Minimacās imputation method is superior to Beagleās. Further study can involve confirming
these trends with runs on more experimental data
Exploiting the Choice-Consumption Mismatch: A New Approach to Disentangle State Dependence and Heterogeneity
This paper oļ¬ers a new identiļ¬cation strategy for disentangling structural state dependence from unobserved heterogeneity in preferences. Our strategy exploits market environments where there is a choice-consumption mismatch. We ļ¬rst demonstrate the eļ¬ectiveness of our identiļ¬cation strategy in obtaining unbiased state dependence estimates via Monte Carlo analysis and highlight its superiority relative to the extant choice-set variation based approach. In an empirical application that uses data of repeat transactions from the car rental industry, we ļ¬nd evidence of structural state dependence, but show that state dependence eļ¬ects may be overstated without exploiting the choice-consumption mismatches that materialize through free upgrades
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