English
Related papers

Related papers: The Dynamic Persistence of Economic Shocks

200 papers

Dynamic networks consist of a sequence of time-varying networks, and it is of great importance to detect the network change points. Most existing methods focus on detecting abrupt change points, necessitating the assumption that the…

Methodology · Statistics 2023-10-13 Yuzhao Zhang , Jingnan Zhang , Yifan Sun , Junhui Wang

Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the…

Machine Learning · Statistics 2016-11-08 Zhen Xu , Wen Dong , Sargur Srihari

Accurately predicting stock repurchases is crucial for quantitative investment and risk management, yet traditional static models fail to capture the complex temporal dependencies of corporate financial conditions. This paper proposes a…

Statistical Finance · Quantitative Finance 2026-04-14 Xiang Ao , Jingxuan Zhang , Xinyu Zhao

Range-based volatility estimators are widely used in financial econometrics to quantify risk and market stress, yet their application to local commodity markets remains limited. This paper shows how open-high--low-close (OHLC) volatility…

Statistical Finance · Quantitative Finance 2026-03-04 Bo Pieter Johannes Andrée

In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence. Our method uses temporal latent variables to capture…

Machine Learning · Computer Science 2020-02-26 Ruizhi Deng , Yanshuai Cao , Bo Chang , Leonid Sigal , Greg Mori , Marcus A. Brubaker

Models based on neural networks and machine learning are seeing a rise in popularity in space physics. In particular, the forecasting of geomagnetic indices with neural network models is becoming a popular field of study. These models are…

Machine Learning · Computer Science 2022-03-15 Brecht Laperre , Jorge Amaya , Giovanni Lapenta

In this paper we develop a novel hidden Markov graphical model to investigate time-varying interconnectedness between different financial markets. To identify conditional correlation structures under varying market conditions and…

Methodology · Statistics 2024-12-06 Beatrice Foroni , Luca Merlo , Lea Petrella

Recent innovations in diffusion probabilistic models have paved the way for significant progress in image, text and audio generation, leading to their applications in generative time series forecasting. However, leveraging such abilities to…

Machine Learning · Computer Science 2025-11-07 Yuansan Liu , Sudanthi Wijewickrema , Dongting Hu , Christofer Bester , Stephen O'Leary , James Bailey

We study the robustness of system estimation to parametric perturbations in system dynamics and initial conditions. We define the problem of sensitivity-based parametric uncertainty quantification in dynamical system estimation. The main…

Systems and Control · Electrical Eng. & Systems 2025-09-09 Ayush Pandey

Vector autoregressive (VAR) models are widely used in practical studies, e.g., forecasting, modelling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this paper introduces a new…

Econometrics · Economics 2021-11-02 Yayi Yan , Jiti Gao , Bin Peng

We analyze a simple macroeconomic model where rational inflation expectations is replaced by a boundedly rational, and genuinely sticky, response to changes in the actual inflation rate. The stickiness is introduced in a novel way using a…

Dynamical Systems · Mathematics 2017-11-23 Pavel Krejci , Eyram Kwame , Harbir Lamba , Dmitrii Rachinskii

This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modelled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of…

Applications · Statistics 2023-08-23 Daniil Karzanov

High frequency data in finance have led to a deeper understanding on probability distributions of market prices. Several facts seem to be well stablished by empirical evidence. Specifically, probability distributions have the following…

Statistical Mechanics · Physics 2009-10-31 Jaume Masoliver , Miquel Montero , Josep M. Porra

We consider the scenario where the parameters of a probabilistic model are expected to vary over time. We construct a novel prior distribution that promotes sparsity and adapts the strength of correlation between parameters at successive…

Machine Learning · Statistics 2015-11-10 Dani Yogatama , Bryan R. Routledge , Noah A. Smith

Empirical modelling often aims for the simplest model consistent with the data. A new technique is presented which quantifies the consistency of the model dynamics as a function of location in state space. As is well-known, traditional…

Chaotic Dynamics · Physics 2009-11-10 Patrick E. McSharry , Leonard A. Smith

In this paper we discuss a scaling approach to business fluctuations. Our starting point consists in recognizing that concepts and methods derived from physics have allowed economists to (re)discover a set of stylized facts which have to be…

In this paper, we consider the problem of estimating parameters of a linear regression model. Using a hybrid systems framework, a hybrid algorithm is proposed allowing the estimate to converge to the exact value of the unknown parameters in…

Systems and Control · Electrical Eng. & Systems 2026-03-04 Adnane Saoud , Ryan S. Johnson , Ricardo G. Sanfelice

Macro-economic models describe the dynamics of economic quantities. The estimations and forecasts produced by such models play a substantial role for financial and political decisions. In this contribution we describe an approach based on…

Neural and Evolutionary Computing · Computer Science 2013-09-24 Gabriel Kronberger , Stefan Fink , Michael Kommenda , Michael Affenzeller

This paper is concerned with the study of the stability of dynamical systems evolving on time scales. We first {formalize the notion of matrix measures on time scales, prove some of their key properties and make use of this notion to study…

Dynamical Systems · Mathematics 2022-06-10 Giovanni Russo , Fabian Wirth

This paper presents a novel machine learning approach to GDP prediction that incorporates volatility as a model weight. The proposed method is specifically designed to identify and select the most relevant macroeconomic variables for…

General Economics · Economics 2023-07-12 Ali Lashgari
‹ Prev 1 8 9 10 Next ›