2020
DOI: 10.3390/math8112073
|View full text |Cite
|
Sign up to set email alerts
|

US Policy Uncertainty and Stock Market Nexus Revisited through Dynamic ARDL Simulation and Threshold Modelling

Abstract: Since the introduction of the measure of economic policy uncertainty, businesses, policymakers, and academic scholars closely monitor its momentum due to expected economic implications. The US is the world’s top-ranked equity market by size, and prior literature on policy uncertainty and stock prices for the US is conflicting. In this study, we reexamine the policy uncertainty and stock price nexus from the US perspective, using a novel dynamically simulated autoregressive distributed lag setting introduced in… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
13
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
8

Relationship

3
5

Authors

Journals

citations
Cited by 17 publications
(13 citation statements)
references
References 54 publications
0
13
0
Order By: Relevance
“…This aligns with the conclusions drawn from wavelet causality analysis, where mutual causation is evident at varying frequency levels. While a corpus of scholarly research underscores the influence of government economic policies on stock market volatility and firm fundamentals (Arouri & Roubaud, 2016;Chen et al, 2017;Drobetz et al, 2018;He & Niu, 2018;Hussain et al, 2020;Jory et al, 2020;Khan et al, 2020;Khojah et al, 2023;Wu et al, 2022), our findings present a nuanced perspective. In line with the limited empirical evidence for example, Arouri and Roubaud (2016) who identified an insignificant impact of EPU on Chinese stocks through time series regression analysis.…”
Section: Resultsmentioning
confidence: 70%
“…This aligns with the conclusions drawn from wavelet causality analysis, where mutual causation is evident at varying frequency levels. While a corpus of scholarly research underscores the influence of government economic policies on stock market volatility and firm fundamentals (Arouri & Roubaud, 2016;Chen et al, 2017;Drobetz et al, 2018;He & Niu, 2018;Hussain et al, 2020;Jory et al, 2020;Khan et al, 2020;Khojah et al, 2023;Wu et al, 2022), our findings present a nuanced perspective. In line with the limited empirical evidence for example, Arouri and Roubaud (2016) who identified an insignificant impact of EPU on Chinese stocks through time series regression analysis.…”
Section: Resultsmentioning
confidence: 70%
“… Khan, Ahmed, Olah, & Popp, 2020 ; M. I. Khan, Teng, & Khan, 2020 ; M. K. Khan, Teng, Khan, & Khan, 2019 ), this novel approach is capable of predicting the actual positive and negative changes in the explanatory variable and its subsequent impact on the dependent variable. Moreover, it can stimulate, estimate, and automatically predict, and graph said changes.…”
Section: Which Cointegration Methods and Why?mentioning
confidence: 99%
“…There are several advantages of SARDL over the simple ARDL approach: (i) SARDL is used to overcome the issues in simple ARDL estimator for estimation in the long and short run. This novel model has the ability of stimulation, estimation, and robotic calculation of counterfactual adjustment in one explanatory variable and its effect on explained variables while keeping other control variables constant [48,49].…”
Section: Econometric Methodologymentioning
confidence: 99%