pinecone/core-2020-05-10-deduplication huggingface dataset:
Higher-order surface FEM for incompressible Navier-Stokes flows on
  manifolds

Stationary and instationary Stokes and Navier-Stokes flows are considered on
two-dimensional manifolds, i.e., on curved surfaces in three dimensions. The
higher-order surface FEM is used for the approximation of the geometry,
velocities, pressure, and Lagrange multiplier to enforce tangential velocities.
Individual element orders are employed for these various fields. Stream-line
upwind stabilization is employed for flows at high Reynolds numbers.
Applications are presented which extend classical benchmark test cases from
flat domains to general manifolds. Highly accurate solutions are obtained and
higher-order convergence rates are confirmed.Comment: Submitted to International Journal for Numerical Methods in Fluids
  V1: Initial submission V2: Corrected errors in strong forms, revised
  discussion of the result
An immersed boundary method for solving compressible flow with
  arbitrarily irregular and moving geometry

In this paper, a novel immersed boundary method is developed, validated, and
applied. Through devising a second-order three-step flow reconstruction scheme,
the proposed method is able to enforce the Dirichlet, Neumann, Robin, and
Cauchy boundary conditions in a straightforward and consistent manner. Equipped
with a fluid-solid coupling framework that integrates high-order temporal and
spatial discretization schemes, numerical experiments concerning flow involving
stationary and moving objects, convex and concave geometries, no-slip and slip
wall boundary conditions, as well as subsonic and supersonic motions are
conducted to validate the method. It is demonstrated that the proposed method
can provide efficient, accurate, and robust boundary treatment for solving flow
with arbitrarily irregular and moving geometries on Cartesian grids
A multiscale approach to hybrid RANS/LES wall modeling within a
  high-order discontinuous Galerkin scheme using function enrichment

We present a novel approach to hybrid RANS/LES wall modeling based on
function enrichment, which overcomes the common problem of the RANS-LES
transition and enables coarse meshes near the boundary. While the concept of
function enrichment as an efficient discretization technique for turbulent
boundary layers has been proposed in an earlier article by Krank & Wall (J.
Comput. Phys. 316 (2016) 94-116), the contribution of this work is a rigorous
derivation of a new multiscale turbulence modeling approach and a corresponding
discontinuous Galerkin discretization scheme. In the near-wall area, the
Navier-Stokes equations are explicitly solved for an LES and a RANS component
in one single equation. This is done by providing the Galerkin method with an
independent set of shape functions for each of these two methods; the standard
high-order polynomial basis resolves turbulent eddies where the mesh is
sufficiently fine and the enrichment automatically computes the
ensemble-averaged flow if the LES mesh is too coarse. As a result of the
derivation, the RANS model is consistently applied solely to the RANS degrees
of freedom, which effectively prevents the typical issue of a log-layer
mismatch in attached boundary layers. As the full Navier-Stokes equations are
solved in the boundary layer, spatial refinement gradually yields wall-resolved
LES with exact boundary conditions. Numerical tests show the outstanding
characteristics of the wall model regarding grid independence, superiority
compared to equilibrium wall models in separated flows, and achieve a speed-up
by two orders of magnitude compared to wall-resolved LES
Methods for Implementing the Stream Boundary Condition in DSMC Computations

In the direct simulation Monte Carlo (DSMC) method for simulating rarefied gas flows, the velocities of simulator particles that cross a simulation boundary and enter the simulation space are typically generated using the acceptance-rejection method that samples the velocities from the theoretical velocity distribution. This paper analyses an alternative technique, where the velocities of entering particles are obtained by extending the simulation procedures to a region adjacent to the simulation space, and considering the movement of particles generated within that region during the simulation time step. The alternative method may be considered as a form of acceptance-rejection technique. The number flux obtained using the alternative method is slightly lower than the theoretical number flux, due to a depleted population of high velocities. Methods of obtaining the correct number flux are presented. The alternative method allows acceptance of all possible velocities, and represents an improvement over the acceptance-rejection method where some velocities are excluded to improve computational efficiency. For upstream boundaries in high speed flows, the alternative method is more computationally efficient than the acceptance-rejection method. However, for downstream boundaries, the alternative method is extremely inefficient. The alternative method, with the correct theoretical number flux, should therefore be used in favour of the acceptance-rejection method only for upstream boundaries in DSMC computations
A substructure-based iterative inner solver coupled with Uzawa's algorithm for the Stokes problem

A domain decomposition method with Lagrange multipliers for the Stokes problem is developed and analysed. A common approach to solve the Stokes problem, termed the Uzawa algorithm, is to decouple the velocity and the pressure. This approach yields the Schur complement system for the pressure Lagrange multiplier which is solved with an iterative solver. Each outer iteration of the Uzawa procedure involves the inversion of a Laplacian in each spatial direction. The objective of this paper is to effectively solve this inner system (the vector Laplacian system) by applying the finite-element tearing and interconnecting (FETI) method. Previously calculated search directions for the FETI solver are reused in subsequent outer Uzawa iterations. The advantage of the approach proposed in this paper is that pressure is continuous across the entire computational domain. Numerical tests are performed by solving the driven cavity problem. An analysis of the number of outer Uzawa iterations and inner FETI iterations is reported. Results show that the total number of inner iterations is almost numerically scalable since it grows asymptotically with the mesh size and the number of subdomains
Implementation and validation of a slender vortex filament code: its application to the study of a four-vortex wake model

A computational code EZ-vortex is developed for the motion of slender vortex filaments of closed or open shape. The integro-differential equations governing the motion of the vortex centrelines are either the Callegari and Ting equations, which are the leading order solution of a matched asymptotic analysis, or equivalent forms of these equations. They include large axial velocity and nonsimilar profiles in the vortical cores. The fluid may be viscous or inviscid. This code is validated both against known solutions of these equations and results from linear stability analyses. The linear and non-linear stages of a perturbed two-vortex wake and of a four-vortex wake model are then computed
An effective RBFN-boundary integral approach for the analysis of natural convection flow

This paper presents a new radial basis function network-boundary integral approach for the analysis of natural convection flow. The use of integral equations (IEs) allows the set of simultaneous unknowns to be confined to the boundary only. In this study, all boundary values including geometries are represented by indirect radial basis function networks (IRBFNs), resulting in an effective boundary element method (BEM) especially for the achievement of high Rayleigh numbers with relatively coarse and uniform meshes. Convergence is obtained up to a Rayleigh number of 1.0e7 in the case of a square cavity using a uniform mesh of 31 × 31 and a Rayleigh number of 5.0e4 in the case of a horizontal concentric annulus using a uniform mesh of 11 × 21
Shock wave numerical structure and the carbuncle phenomenon

Since the development of shock-capturing methods, the carbuncle phenomenon has been reported to be a spurious solution produced by almost all currrently available contact-preserving methods. The present analysis indicates that the onset of carbuncle phenomenon is actually strongly related to the shock wave numerical structure. A matrix-based stability analysis as well as Euler nite volume computations are compared to illustrate the importance of the internal shock structure to trigger the carbuncle phenomenon
Numerical Solutions of 2-D Steady Incompressible Driven Cavity Flow at
  High Reynolds Numbers

Numerical calculations of the 2-D steady incompressible driven cavity flow
are presented. The Navier-Stokes equations in streamfunction and vorticity
formulation are solved numerically using a fine uniform grid mesh of 601x601.
The steady driven cavity solutions are computed for Re<21,000 with a maximum
absolute residuals of the governing equations that were less than 10-10. A new
quaternary vortex at the bottom left corner and a new tertiary vortex at the
top left corner of the cavity are observed in the flow field as the Reynolds
number increases. Detailed results are presented and comparisons are made with
benchmark solutions found in the literature
Econometric Modelling for Short-Term inflation Forecasting in the Euro Area

This paper examines the problem of forecasting macro-variables which are observed monthly (or quarterly) and result from geographical and sectorial aggregation. The aim is to formulate a methodology whereby all relevant information gathered in this context could provide more accurate forecasts, be frequently updated, and include a disaggregated explanation as useful information for decision-making. The appropriate treatment of the resulting disaggregated data set requires vector modelling, which captures the long-run restrictions between the different time series and the short-term correlations existing between their stationary transformations. Frequently, due to a lack of degrees of freedom, the vector model must be restricted to a block-diagonal vector model. This methodology is applied in this paper to inflation in the euro area, and shows that disaggregated models with cointegration restrictions improve accuracy in forecasting aggregate macro-variables. Copyright © 2007 John Wiley & Sons, Ltd.Publicad
Can Panel Data Really Improve the Predictability of the Monetary Exchange Rate Model?

A common explanation for the inability of the monetary model to beat
the random walk in forecasting future exchange rates is that conventional
time series tests may have low power, and that panel data should generate
more powerful tests. This paper provides an extensive evaluation of this
power argument to the use of panel data in the forecasting context. In
particular, by using simulations it is shown that although pooling of the
individual prediction tests can lead to substantial power gains, pooling
only the parameters of the forecasting equation, as has been suggested in
the previous literature, does not seem to generate more powerful tests.
The simulation results are illustrated through an empirical application.
International Equity Flows and the Predictability of U.S. Stock Returns

We examined the link between international equity flows and U.S. stock returns. Based on the results of tests of in-sample and out-of-sample predictability of stock returns, we found evidence of a strong positive (negative) link between international equity flows and contemporaneous (one-month-ahead) stock returns. Our results also indicate that an investor, in real time, could have used information on the link between international equity flows and one-month-ahead stock returns to improve the performance of simple trading rules.
Forecasting macroeconomic variables in a small open economy : a comparison between small- and large-scale models

This paper compares the forecasting ability of five alternative types of models
in predicting four key macroeconomic variables, namely, per capita growth
rate, the CPI inflation, the money market rate, and the growth rate of the
nominal effective exchange rate for the South African economy. Unlike the
theoretical small open economy new Keynesian dynamic stochastic general
equilibrium, the unrestricted VAR, and the small-scale Bayesian vector autoregressive
models, which are estimated based on four variables, dynamic factor
models and the large-scale BVAR models use information from a data-rich
environment containing 266 macroeconomic time series observed over the
period 1983:01 to 2002:04. The results, based on root mean square errors, for
one- to eight-quarter-ahead out-of-sample forecasts over the horizon of 2003:01
to 2006:04, show that, except for the growth rate of the of nominal effective
exchange rate, large-scale BVARs outperform the other four types of models
consistently and, generally, significantly.http://www3.interscience.wiley.com/journal/2966/home?CRETRY=1&SRETRY=
Identifying good inflation forecaster

The objective of this paper is to identify the best indicator variable in forecasting inflation in Malaysia. Due to the fact that Malaysia experienced the rise of CPI by 4.8 percent in March 2006, the country’s highest inflation rate in seven years, there is a need to foresee future trend of general price level. To determine whether certain indicator (variable) could predict inflation, we construct a simple forecasting model that incorporates the variable. We estimate a two-variable VECM model of quasi-tradable inflation using monthly data covering the period 1980:01 to 2006:12. We alternate between the following inflation indicators: commodity prices, financial indicators and economic activities. We evaluate each model using out-of-sample forecast. The study proposes that a simple model using industrial production index improves the accuracy of inflation forecasts. The results support our hypothesis.
Real time estimation of potential output and output gap for theeuro-area: comparing production function with unobserved componentsand SVAR approaches

We develop a new version of the production function (PF) approach usually used for estimating the output gap of the euro area. Our version does not call for any (often imprecise) measure of the capital stock and improves the estimation of the trend total factor productivity. We asses this approach by comparing it with two other multivariate methods mostly used for output gap estimates, a multivariate unobserved components (MUC) model and a Structural Vector Auto-Regressive (SVAR) model.
The comparison is conducted by relying on assessment criteria such as the concordance of the turning points chronology with a reference one, the inflation forecasting power and the real-time consistency of the estimates.
Two contributions are achieved. Firstly, we take into account data revisions and their impact on the output gap estimates by using vintage datasets coming from the Euro Area Business Cycle (EABCN) Real-Time Data-Base (RTDB). Secondly, the PF approach, generally employed by policy-makers despite of its difficult implementation, is assessed. We thus improve on previous papers which limited their assessment on other multivariate methods, e.g. MUC or SVAR models.
The different methods show different ranks in relation to the three criteria. This new PF estimate appears highly concordant with the reference chronology. Its forecasting power appears favourable only for the shortest horizon (1 month). Finally, the SVAR model appears more consistent in real-time.
Using CAViaR models with implied volatility for value-at-risk estimation

This paper proposes VaR estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market’s expectation of risk. Forecast combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models, a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residual. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P500 daily returns
Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003–2008?

Recently developed structural models of the global crude oil market imply that the surge in the real price of oil between mid 2003 and mid 2008 was driven by repeated positive shocks to the demand for all industrial commodities, reflecting unexpectedly high growth mainly in emerging Asia. We evaluate this proposition using an alternative data source and a different econometric methodology. Rather than inferring demand shocks from an econometric model, we utilize a direct measure of global demand shocks based on revisions of professional real gross domestic product (GDP) growth forecasts. We show that forecast surprises during 2003–2008 were associated primarily with unexpected growth in emerging economies (in conjunction with much smaller positive GDP‐weighted forecast surprises in the major industrialized economies), that markets were repeatedly surprised by the strength of this growth, that these surprises were associated with a hump‐shaped response of the real price of oil that reaches its peak after 12–16 months, and that news about global growth predict much of the surge in the real price of oil from mid 2003 until mid 2008 and much of its subsequent decline. Copyright © 2012 John Wiley & Sons, Ltd
Hierarchical shrinkage in time-varying parameter models

In this paper, we forecast EU area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: (i) time varying; (ii) constant over time; or (iii) shrunk to zero. The econometric methodology decides automatically to which category each coefficient belongs. Our empirical results indicate the benefits of such an approach. © 2013 John Wiley & Sons, Ltd
A monthly indicator of employment in the euro area: real time analysis of indirect estimates

The paper presents the results of an extensive real time analysis of alternative model-based approaches to derive a monthly indicator of employment for the euro area. In the experiment the Eurostat quarterly national accounts series of employment is temporally disaggregated using the information coming from the monthly series of unemployment. The strategy benefits of the contribution of the information set of the euro area and its 6 larger member states, as well as the split into the 6 sections of economic activity. The models under comparison include univariate regressions of the Chow and Lin' type where the euro area aggregate is directly and indirectly derived, as well as multivariate structural time series models of small and medium size. The specification in logarithms is also systematically assessed. The largest multivariate setups, up to 49 series, are estimated through the EM algorithm. Main conclusions are the following: mean revision errors of disaggregated estimates of employment are overall small; a gain is obtained when the model strategy takes into account the information by both sector and member state; the largest multivariate setups outperforms those of small size and the strategies based on classical disaggregation methods.
How predictable are equity covariance matrices? Evidence from high frequency data for four markets.

Most pricing and hedging models rely on the long-run temporal stability of a sample covariance matrix. Using a large dataset of equity prices from four countries—the USA, UK, Japan and Germany—we test the stability of realized sample covariance matrices using two complementary approaches: a standard covariance equality test and a novel matrix loss function approach. Our results present a pessimistic outlook for equilibrium models that require the covariance of assets returns to mean revert in the long run. We find that, while a daily first-order Wishart autoregression is the best covariance matrix-generating candidate, this non-mean-reverting process cannot capture all of the time series variation in the covariance-generating process
A quantile regression approach to equity premium prediction

Copyright © 2014 John Wiley & Sons, Ltd. We propose a quantile regression approach to equity premium forecasting. Robust point forecasts are generated from a set of quantile forecasts using both fixed and time-varying weighting schemes, thereby exploiting the entire distributional information associated with each predictor. Further gains are achieved by incorporating the forecast combination methodology into our quantile regression setting. Our approach using a time-varying weighting scheme delivers statistically and economically significant out-of-sample forecasts relative to both the historical average benchmark and the combined predictive mean regression modeling approach
Last Night a Shrinkage Saved My Life: Economic Growth, Model Uncertainty and Correlated Regressors

We compare the predictive ability of Bayesian methods which deal simultaneously with model uncertainty and correlated regressors in the framework of cross-country growth regressions. In particular, we assess methods with spike and slab priors combined with different prior specifications for the slope parameters in the slab. Our results indicate that moving away from Gaussian g-priors towards Bayesian ridge, LASSO or elastic net specifications has clear advantages for prediction when dealing with datasets of (potentially highly) correlated regressors, a pervasive characteristic of the data used hitherto in the econometric literature
Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data-Snooping Bias

In this paper, we present two neural‐network‐based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange‐traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data‐snooping bias and the time‐consuming and biased processes involved in optimizing their parameter
Factor models of stock returns: GARCH errors versus time-varying betas

This paper investigates the implications of time-varying betas in factor models for stock returns. It is shown that a single-factor model (SFMT) with autoregressive betas and homoscedastic errors (SFMT-AR) is capable of reproducing the most important stylized facts of stock returns. An empirical study on the major US stock market sectors shows that SFMT-AR outperforms, in terms of in-sample and out-of-sample performance, SFMT with constant betas and conditionally heteroscedastic (GARCH) errors, as well as two multivariate GARCH-type models