The determinants of crude oil prices: Evidence from ARDL and nonlinear ARDL approaches

This paper is an innovative attempt to empirically investigate the determinants of crude oil prices. The main objective is to distinguish between short-and long-term effects of some covariates on oil prices. The autoregressive distributed lag (ARDL) approach is applied to daily series spanning the period from January 2, 2003, to May 24, 2021, to analyze long-run relationships and short-run dynamics. The paper also focuses on the asymmetric effects of covariates and a nonlinear ARDL (NARDL) approach is used to explore this asymmetry. The use of an asymmetric error correction model with asymmetric cointegration provides new insights for examining the determinants of oil prices. All investigations of underlying oil price fluctuations are examined both before and in the COVID-19 pandemic. Our results, based on different econometric specifications, have key policy implications for policymakers both with and without COVID-19 potential considerations.

EVIDENCE FROM DYNAMIC SYMMETRIC AND ASYMMETRIC CAUSALITY TESTS ON THE RELATIONSHIP BETWEEN TAX REVENUES AND PUBLIC EXPENDITURES IN G7 COUNTRIES

In this study, it is aimed to determine the symmetric and asymmetric causal relations between tax revenues and public expenditures in G7 countries. Annual data for the years 1990 through 2021 were used to determine the relationships between the variables. The Hacker and Hatemi-J (2012) bootstrap symmetric causality test, the Hatemi-J (2012) bootstrap asymmetric causality test, and the Hatemi-J (2021) dynamic bootstrap symmetric and asymmetric causality tests were used. The symmetric and asymmetric causality tests revealed few causal linkages between the variables, however the dynamic symmetric and asymmetric causality tests revealed more causal relationships. According to our research, it is essential to use dynamic analysis methods that can generate unique outcomes for sub-periods rather than analysis methods that generate a single result for the entire period in dynamic domains like public expenditure and national tax policies. In reality, it has been noted that throughout the Quantitative Easing period introduced following the 2008 Global Financial Crisis in the USA and during the COVID 19 process, public spending have expanded independently of budget revenues. Similar circumstances occurred in France during the EU debt crisis (2013– 2017), in Italy during the Great Recession of 2007–2009, and during COVID 19. When the global economic environment was favorable between 2017 and 2019, Germany, United Kingdom, and Italy organized their public expenditures in accordance with tax revenues, functioning within the framework of the Tax-Spend Hypothesis. As a result, for the effectiveness of fiscal policy, nations may use various fiscal policy techniques during various economic conjuncture times. © 2022, Symmetrion. All rights reserved.

The determinants of crude oil prices: Evidence from ARDL and nonlinear ARDL approaches

This paper is an innovative attempt to empirically investigate the determinants of crude oil prices. The main objective is to distinguish between short- and long-term effects of some covariates on oil prices. The autoregressive distributed lag (ARDL) approach is applied to daily series spanning the period from January 2, 2003, to May 24, 2021, to analyze long-run relationships and short-run dynamics. The paper also focuses on the asymmetric effects of covariates and a nonlinear ARDL (NARDL) approach is used to explore this asymmetry. The use of an asymmetric error correction model with asymmetric cointegration provides new insights for examining the determinants of oil prices. All investigations of underlying oil price fluctuations are examined both before and in the COVID-19 pandemic. Our results, based on different econometric specifications, have key policy implications for policymakers both with and without COVID-19 potential considerations.

A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data.

In parametric statistical modeling and inference, it is critical to develop generalizations of existing statistical distributions to make them more flexible in modeling real data sets. Thus , this paper contributes to the subject by investigating a new flexible and versatile generalized family of distributions defined from the alliance of the families known as beta-G and Topp-Leone generated (TL-G), inspiring the name of BTL-G family. The characteristics of this new family are studied through analytical, graphical and numerical approaches. Statistical features of the family such as expansion of density function (pdf), cumulative function (cdf), moments (MOs), incomplete moments (IMOs), mean deviation (MDE), and entropy (ENT) are calculated. The model parameters' maximum likelihood estimates (MaxLEs) and Bayesian estimates (BEs) are provided. Symmetric and Asymmetric Bayesian Loss function have been discussed. A complete simulation study is proposed to illustrate their numerical efficiency. The considered model is also applied to analyze two different kinds of genuine COVID 19 data sets. We show that it outperforms other well-known models defined with the same baseline distribution, proving its high level of adaptability in the context of data analysis.