Transverse momentum distributions in Semi-inclusive Deep Inelastic Scattering

Within a perturbative approach to Quantum Chromodynamics (QCD), we show how to extend ordinary DGLAP longitudinal evolution equations to include the radiative transverse momentum generated in the collinear branching regime. Considering Semi-inclusive Deep Inelastic Scattering as a reference process, we perform such a generalization both in the current and in the target fragmentation region. These distributions are then used to predict semi-inclusive Deep Inelastic Scattering cross-sections onto the whole phase space of the detected hadron.Comment: 6 pages. Invited talk at "Symmetries and Spin", Prague, July 19-2

Construction of nice nilpotent Lie groups

We illustrate an algorithm to classify nice nilpotent Lie algebras of dimension $n$ up to a suitable notion of equivalence; applying the algorithm, we obtain complete listings for $n\leq9$. On every nilpotent Lie algebra of dimension $\leq 7$, we determine the number of inequivalent nice bases, which can be $0$, $1$, or $2$. We show that any nilpotent Lie algebra of dimension $n$ has at most countably many inequivalent nice bases.Comment: v3: Condition (N3) has been changed to exclude diagrams with arrows with the same label as the starting node, this will not affect the rest of the paper or the results, since this condition was implicitly assumed through the paper. Added a final remark 3.9. Presentation improved and bibliography updated. Article 28 Pages; Tables in ancillary file 137 page

Inference in Dynamic Discrete Choice Problems under Local Misspecification

Single-agent dynamic discrete choice models are typically estimated using heavily parametrized econometric frameworks, making them susceptible to model misspecification. This paper investigates how misspecification affects the results of inference in these models. Specifically, we consider a local misspecification framework in which specification errors are assumed to vanish at an arbitrary and unknown rate with the sample size. Relative to global misspecification, the local misspecification analysis has two important advantages. First, it yields tractable and general results. Second, it allows us to focus on parameters with structural interpretation, instead of "pseudo-true" parameters. We consider a general class of two-step estimators based on the K-stage sequential policy function iteration algorithm, where K denotes the number of iterations employed in the estimation. This class includes Hotz and Miller (1993)'s conditional choice probability estimator, Aguirregabiria and Mira (2002)'s pseudo-likelihood estimator, and Pesendorfer and Schmidt-Dengler (2008)'s asymptotic least squares estimator. We show that local misspecification can affect the asymptotic distribution and even the rate of convergence of these estimators. In principle, one might expect that the effect of the local misspecification could change with the number of iterations K. One of our main findings is that this is not the case, i.e., the effect of local misspecification is invariant to K. In practice, this means that researchers cannot eliminate or even alleviate problems of model misspecification by changing K