1,238 research outputs found
Valence-shell double photoionization of alkaline-earth-metal atoms
We apply the convergent close-coupling formalism to describe direct double photoionization (DPI) of the valence n s2 shell of alkaline-earth-metal atoms: beryllium (n=2), magnesium (n=3), and calcium (n=4). We consider the range of photon energies below the onset of resonant and Auger ionization processes where the subvalent and core electrons can be treated as spectators. By comparing alkaline-earth-metal atoms with helium, we elucidate the role of the ground state and final ionized state correlations in DPI of various quasi-two-electron atoms
Model Adequacy Checks for Discrete Choice Dynamic Models
This paper proposes new parametric model adequacy tests for possibly
nonlinear and nonstationary time series models with noncontinuous data
distribution, which is often the case in applied work. In particular, we
consider the correct specification of parametric conditional distributions in
dynamic discrete choice models, not only of some particular conditional
characteristics such as moments or symmetry. Knowing the true distribution is
important in many circumstances, in particular to apply efficient maximum
likelihood methods, obtain consistent estimates of partial effects and
appropriate predictions of the probability of future events. We propose a
transformation of data which under the true conditional distribution leads to
continuous uniform iid series. The uniformity and serial independence of the
new series is then examined simultaneously. The transformation can be
considered as an extension of the integral transform tool for noncontinuous
data. We derive asymptotic properties of such tests taking into account the
parameter estimation effect. Since transformed series are iid we do not require
any mixing conditions and asymptotic results illustrate the double simultaneous
checking nature of our test. The test statistics converges under the null with
a parametric rate to the asymptotic distribution, which is case dependent,
hence we justify a parametric bootstrap approximation. The test has power
against local alternatives and is consistent. The performance of the new tests
is compared with classical specification checks for discrete choice models
New goodness-of-fit diagnostics for conditional discrete response models
This paper proposes new specification tests for conditional models with
discrete responses, which are key to apply efficient maximum likelihood
methods, to obtain consistent estimates of partial effects and to get
appropriate predictions of the probability of future events. In particular, we
test the static and dynamic ordered choice model specifications and can cover
infinite support distributions for e.g. count data. The traditional approach
for specification testing of discrete response models is based on probability
integral transforms of a jittered discrete data which leads to continuous
uniform iid series under the true conditional distribution. Then, standard
specification testing techniques for continuous variables could be applied to
the transformed series, but the extra randomness from jitters affects the power
properties of these methods. We investigate in this paper an alternative
transformation based only on original discrete data that avoids any
randomization. We analyze the asymptotic properties of goodness-of-fit tests
based on this new transformation and explore the properties in finite samples
of a bootstrap algorithm to approximate the critical values of test statistics
which are model and parameter dependent. We show analytically and in
simulations that our approach dominates the methods based on randomization in
terms of power. We apply the new tests to models of the monetary policy
conducted by the Federal Reserve
Specification tests for nonlinear dynamic models
We propose a new adequacy test and a graphical evaluation tool for nonlinear
dynamic models. The proposed techniques can be applied in any setup where
parametric conditional distribution of the data is specified, in particular to
models involving conditional volatility, conditional higher moments,
conditional quantiles, asymmetry, Value at Risk models, duration models,
diffusion models, etc. Compared to other tests, the new test properly controls
the nonlinear dynamic behavior in conditional distribution and does not rely on
smoothing techniques which require a choice of several tuning parameters. The
test is based on a new kind of multivariate empirical process of
contemporaneous and lagged probability integral transforms. We establish weak
convergence of the process under parameter uncertainty and local alternatives.
We justify a parametric bootstrap approximation that accounts for parameter
estimation effects often ignored in practice. Monte Carlo experiments show that
the test has good finite-sample size and power properties. Using the new test
and graphical tools we check the adequacy of various popular heteroscedastic
models for stock exchange index data
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