7,510 research outputs found
The great moderation through the lens of sectoral spillovers
Publisher Copyright: Copyright © 2023 The Authors.We estimate sectoral spillovers around the Great Moderation with the help of forecast error variance decomposition tables. Obtaining such tables in high dimensions is challenging because they are functions of the estimated vector autoregressive coefficients and the residual covariance matrix. In a simulation study, we compare various regularization methods on both and conduct a comprehensive analysis of their performance. We show that standard estimators of large connectedness tables lead to biased results and high estimation uncertainty, both of which are mitigated by regularization. To explore possible causes for the Great Moderation, we apply a cross-validated estimator on sectoral spillovers of industrial production in the US from 1972 to 2019. We find that the spillover network has considerably weakened, which hints at structural change, for example, through improved inventory management, as a critical explanation for the Great Moderation.publishersversionpublishe
Transform methods for precision continuum and control models of flexible space structures
An open loop optimal control algorithm is developed for general flexible structures, based on Laplace transform methods. A distributed parameter model of the structure is first presented, followed by a derivation of the optimal control algorithm. The control inputs are expressed in terms of their Fourier series expansions, so that a numerical solution can be easily obtained. The algorithm deals directly with the transcendental transfer functions from control inputs to outputs of interest, and structural deformation penalties, as well as penalties on control effort, are included in the formulation. The algorithm is applied to several structures of increasing complexity to show its generality
Vector Autoregressions with Machine Learning
I develop three new types of vector autoregressions that use supervised
machine learning models to estimate coefficients in place of ordinary least
squares. I use these models to estimate the effects of monetary policy on the
real economy. Overall, I find that the machine learning vector autoregressions
produce impulse responses that are well behaved and similar to their ordinary
least squares counterparts. In practice, the machine learning vector autoregressions
produce more conservative estimates than the traditional ordinary
least squares vector autoregressions. Additionally, I establish a simulation
scheme to compare the relative efficiency of impulse responses generated from
machine learning and ordinary least squares vector autoregressions. To calculate
condence intervals, I use a bias corrected bootstrapping method from
Politis and Romano (1994) called the stationary bootstrap. In future work, I
intend to compare these impulse responses using simulated data from Killian
and Kim (2011)
A Stochastic Majorize-Minimize Subspace Algorithm for Online Penalized Least Squares Estimation
Stochastic approximation techniques play an important role in solving many
problems encountered in machine learning or adaptive signal processing. In
these contexts, the statistics of the data are often unknown a priori or their
direct computation is too intensive, and they have thus to be estimated online
from the observed signals. For batch optimization of an objective function
being the sum of a data fidelity term and a penalization (e.g. a sparsity
promoting function), Majorize-Minimize (MM) methods have recently attracted
much interest since they are fast, highly flexible, and effective in ensuring
convergence. The goal of this paper is to show how these methods can be
successfully extended to the case when the data fidelity term corresponds to a
least squares criterion and the cost function is replaced by a sequence of
stochastic approximations of it. In this context, we propose an online version
of an MM subspace algorithm and we study its convergence by using suitable
probabilistic tools. Simulation results illustrate the good practical
performance of the proposed algorithm associated with a memory gradient
subspace, when applied to both non-adaptive and adaptive filter identification
problems
News-driven business cycles in small open economies
The focus of this paper is on news-driven business cycles in small open economies.
We make two significant contributions. First, we develop a small open economy
model where the presence of financial frictions permits the replication of business
cycle co-movements in response to news shocks. Second, we use VAR analysis to
identify news shocks using data on four advanced small open economies. We find
that expected shocks about the future Total Factor Productivity generate business
cycle co-movements in output, hours, consumption and investment. We also find
that news shocks are associated with countercyclical current account dynamics.
Our findings are robust across a number of alternative identification schemes
Linear and Nonlinear Encoding Properties of an Identified Mechanoreceptor on the Fly wing Measured with Mechanical Noise Stimuli
The wing blades of most flies contain a small set of distal campaniform sensilla, mechanoreceptors that respond to deformations of the cuticle. This paper describes a method of analysis based upon mechanical noise stimuli which is used to quantify the encoding properties of one of these sensilla (the d-HCV cell) on the wing of the blowfly Calliphora vomitoria (L.). The neurone is modelled as two components, a linear filter that accounts for the frequency response and phase characteristics of the cell, followed by a static nonlinearity that limits the spike discharge to a narrow portion of the stimulus cycle. The model is successful in predicting the response of campaniform neurones to arbitrary stimuli, and provides a convenient method for quantifying the encoding properties of the sensilla.
The d-HCV neurone is only broadly frequency tuned, but its maximal response near 150 Hz corresponds to the wingbeat frequency of Calliphora. In the range of frequencies likely to be encountered during flight, the d-HCV neurone fires a single phase-locked action potential for each stimulus cycle. The phase lag of the cell decreases linearly with increasing frequency such that the absolute delay between stimulus and response remains nearly constant. Thus, during flight the neurone is capable of firing one precisely timed action potential during each wingbeat, and might be used to modulate motor activity that requires afferent input on a cycle-by-cycle basis
The dynamic behaviour of budget components and output – the cases of France, Germany, Portugal, and Spain
The main focus of this paper is the relation between the cyclical components of total revenues and expenditures and the budget balance in France, Germany, Portugal, and Spain. We try to uncover past trends behind the development of public finances that contribute to explaining the current stance of fiscal policy. The disaggregate analysis of fiscal policy in an SVAR that mixes long and short-term constraints allows us to look into the transmission channels of fiscal policy and to derive a model-based indicator of structural balance. The main conclusions are that fiscal slippages are mainly due to reversals in tax policies, which are unmatched by expenditure adjustments. As a consequence, deficits rise when economic conditions worsen but cause a ‘ratcheting up’ in the size of government in economic booms. The Stability and Growth Pact has not eradicated these procyclical policies. Bad policies in good times also contribute to aggregate macroeconomic instability.fiscal indicator; structural balance; output gap; SGP; EMU; SVAR; short and longterm restrictions.
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