Results 71 to 80 of about 15,233,893 (167)
Network Effects in Corporate Emissions: Evidence from a Data‐Dependent Spatial Panel Model
Journal of Money, Credit and Banking, EarlyView.Abstract We study spillover effects in corporate toxic emissions using a heterogeneous panel of U.S. industrial facilities from 2000 to 2023. Rather than imposing a network structure a priori, we uncover an unobserved web of influence directly from the data using recent advances in high‐dimensional network econometrics.
STYLIANOS ASIMAKOPOULOS +3 more
wiley +1 more source
The Evolution of Interest‐Rate Models: From the Yield Curve to the Swaption Cube
Journal of Economic Surveys, EarlyView.ABSTRACT Interest‐rate modelling is often taught as a catalogue of competing stochastic equations, obscuring why models were created and why modern sell‐side desks use several simultaneously. This survey reorganises the field around five layers of a pricing architecture: curve construction; arbitrage‐free dynamics; volatility‐smile representation ...
Xuan Feng +2 more
wiley +1 more source
SMCTC: Sequential Monte Carlo in C++ [PDF]
Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature.
Adam M. Johansen
core
Journal of Oral Rehabilitation, EarlyView.In this exploratory case–control study, plasma PLP (vitamin B6), homocysteine and ACTH concentrations did not differ between patients with burning mouth syndrome (BMS) and controls, and none discriminated patients individually. Within patients, however, psychological distress mapped onto a homocysteine–ACTH relationship from which PLP appeared ...
Ashley Lebel +3 more
wiley +1 more source
Computing mean first exit times for stochastic processes using multi-level Monte Carlo [PDF]
, 2012 The multi-level approach developed by Giles (2008) can be used to estimate mean first exit times for stochastic differential equations, which are of interest in finance, physics and chemical kinetics.
Roj, Mikolaj, Higham, Desmond
core +1 more source
Journal of Regional Science, EarlyView.ABSTRACT Using two approaches, we contribute to sub‐national economic performance measurement by netting out the influence of a region's performance on its industries. The first approach measures performance using technical efficiency and involves introducing the first multilevel spatial stochastic frontier model.
Anthony J. Glass, Karligash Kenjegalieva
wiley +1 more source
Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models
Journal of Time Series Analysis, EarlyView.ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley +1 more source
Testing Distributional Granger Causality With Entropic Optimal Transport
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
On Testing for Independence Between Generalized Error Models of Several Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We define generalized innovations associated with generalized error models having arbitrary distributions, that is, distributions that can be mixtures of continuous and discrete distributions. These models include stochastic volatility models and regime‐switching models with possibly zero‐inflated regimes.
Kilani Ghoudi +2 more
wiley +1 more source
Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)
Journal of Time Series Analysis, EarlyView.ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
wiley +1 more source