Conditions for swappability of records in a microdata set when some marginals are fixed

We consider swapping of two records in a microdata set for the purpose of disclosure control. We give some necessary and sufficient conditions that some observations can be swapped between two records under the restriction that a given set of marginals are fixed. We also give an algorithm to find another record for swapping if one wants to swap out some observations from a particular record. Our result has a close connection to the construction of Markov bases for contingency tables with given marginals

Are Estimates of Asymmetric First-Price Auction Models Credible? Semi & Nonparametric Scrutinizations

Structural first-price auction estimation methods, built upon Bayesian Nash Equilibrium (BNE), have provided prolific empirical findings. However, due to the latent nature of underlying valuations, the assumption of BNE is not feasibly testable with field data, a fact that evokes harsh criticism on the literature. To respond to skepticism regarding credibility, we provide a focused answer by scrutinizing estimates derived from experimental asymmetric auction data in which researchers observe valuations. We test the statistical equivalence between the estimated and true value distributions. The Kolmogorov-Smirnov test fails to reject the distributional equivalence, strongly supporting the credibility of structural asymmetric auction estimates

Minimal and minimal invariant Markov bases of decomposable models for contingency tables

We study Markov bases of decomposable graphical models consisting of primitive moves (i.e., square-free moves of degree two) by determining the structure of fibers of sample size two. We show that the number of elements of fibers of sample size two are powers of two and we characterize primitive moves in Markov bases in terms of connected components of induced subgraphs of the independence graph of a hierarchical model. This allows us to derive a complete description of minimal Markov bases and minimal invariant Markov bases for decomposable models.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ207 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Do Crime-Prone Areas Attract Gambling Shops? A Case of London Boroughs

We investigate a causal effect of crime on the number of betting shops by using annual data from London boroughs (2007-2015). Using an instrumental variable strategy, we estimate a panel model accounting for omitted variables and borough-level heterogeneity. Our estimation results show that a 1% increase in crime rate causes a 1.2% increase in the number of betting shops (per capita). Put differently, a new betting shop opens in a borough for every 1.4% increase in the local crime rate, on average. The causal effect is robust across a variety of specifications, although the magnitude varies across models