28,988 research outputs found
Partial Consistency with Sparse Incidental Parameters
Penalized estimation principle is fundamental to high-dimensional problems.
In the literature, it has been extensively and successfully applied to various
models with only structural parameters. As a contrast, in this paper, we apply
this penalization principle to a linear regression model with a
finite-dimensional vector of structural parameters and a high-dimensional
vector of sparse incidental parameters. For the estimators of the structural
parameters, we derive their consistency and asymptotic normality, which reveals
an oracle property. However, the penalized estimators for the incidental
parameters possess only partial selection consistency but not consistency. This
is an interesting partial consistency phenomenon: the structural parameters are
consistently estimated while the incidental ones cannot. For the structural
parameters, also considered is an alternative two-step penalized estimator,
which has fewer possible asymptotic distributions and thus is more suitable for
statistical inferences. We further extend the methods and results to the case
where the dimension of the structural parameter vector diverges with but slower
than the sample size. A data-driven approach for selecting a penalty
regularization parameter is provided. The finite-sample performance of the
penalized estimators for the structural parameters is evaluated by simulations
and a real data set is analyzed
Risks of Large Portfolios
Estimating and assessing the risk of a large portfolio is an important topic
in financial econometrics and risk management. The risk is often estimated by a
substitution of a good estimator of the volatility matrix. However, the
accuracy of such a risk estimator for large portfolios is largely unknown, and
a simple inequality in the previous literature gives an infeasible upper bound
for the estimation error. In addition, numerical studies illustrate that this
upper bound is very crude. In this paper, we propose factor-based risk
estimators under a large amount of assets, and introduce a high-confidence
level upper bound (H-CLUB) to assess the accuracy of the risk estimation. The
H-CLUB is constructed based on three different estimates of the volatility
matrix: sample covariance, approximate factor model with known factors, and
unknown factors (POET, Fan, Liao and Mincheva, 2013). For the first time in the
literature, we derive the limiting distribution of the estimated risks in high
dimensionality. Our numerical results demonstrate that the proposed upper
bounds significantly outperform the traditional crude bounds, and provide
insightful assessment of the estimation of the portfolio risks. In addition,
our simulated results quantify the relative error in the risk estimation, which
is usually negligible using 3-month daily data. Finally, the proposed methods
are applied to an empirical study
Exact solution to the steady-state dynamics of a periodically-modulated resonator
We provide an analytic solution to the coupled-mode equations describing the
steady-state of a single periodically-modulated optical resonator driven by a
monochromatic input. The phenomenology of this system was qualitatively
understood only in the adiabatic limit, i.e. for low modulation speed. However,
both in and out of this regime, we find highly non-trivial effects for specific
parameters of the modulation. For example, we show complete suppression of the
transmission even with zero detuning between the input and the static resonator
frequency. We also demonstrate the possibility for complete, lossless frequency
conversion of the input into the side-band frequencies, as well as for
optimizing the transmitted signal towards a given target temporal waveform. The
analytic results are validated by first-principle simulations
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