Dynamics of oil prices, exchange rates and asset prices in the GCC countries

We analyse the relationship between the oil prices, asset prices, and foreign exchange rates in the selected GCC economies, namely United Arab Emirates (UAE), Qatar, Kuwait and Saudi Arabia. Using a time-varying parameter VAR we study the coherence, conditional volatility and impulse responses of the exchange rates and stock markets to oil price shocks over specific periods and policy regimes. The model is identified using sign-restrictions imposed on the impulse responses over contemporaneous and long horizons. Our results suggest that the impact of oil prices on the exchange rate and asset prices are time dependent. Hence there is a loss in information when using standard linear models that average out effects over time. The response of the exchange rates and asset prices to oil prices weakens and strengthens depending on the regime of the markets. The period following financial crisis uniformly strengthens the relationships between the variables. The responses also vary across the GCC economies, emphasizing the fact that differences exists across these economies although their economic structures increasingly becoming similar

International Labour Force Participation Rates by Gender: Unit Root or Structural Breaks?

This paper examines the possibility of unit roots in the presence of endogenously determined multiple structural breaks in the total, female and male labour force participation rates (LFPR) for Australia, Canada and the USA. We extend the procedure of Gil-Alana (2008) for single structural break to the case of multiple structural breaks at endogenously determined dates using the principles suggested by Bai and Perron (1998). We use the Robinson (1994) LM test to determine the fractional order of integration. We find that endogenously determined structural breaks render the total, female and male LFPR series stationary or at best mean-reverting.Labour Force Participation Rates, Gender, Fractional Integration, Structural Breaks

Unlocking the Impact of Climate Change Mitigation Policies: A Comprehensive Study of Clean and Dirty Innovation Dynamics

Achieving the Paris Climate Agreement’s goal of limiting global warming to 1.5 °C to 2 °C by the end of the century will require massive investments in environmental technologies and a drastic shift away from high-carbon technologies. This paper investigates the impact of climate change mitigation policies on clean energy innovation. A statistical evaluation of the impact of public policies on the rate and direction of innovation for a lowcarbon future is complicated by the nature of the data and the absence of benchmarks. In addition, the statistical analysis is further complicated by the spillover effects between clean and dirty innovation and by the lag effects. In this paper, the authors assess the effects of both public policies, such as carbon taxes and green subsidies, and economic and environmental conditions, such as oil prices, large recessions, climate-related disasters, etc., on clean innovation using a nonparametric method based on the copula distribution of clean innovation. The authors collect data from the European Patent Office (EPO) Worldwide Patent Statistical (PATSTAT) Database, both on clean and dirty patents. This database is managed by the EPO and compiles data from patent offices around the world. The emphasis is put on inventions for which a patent application has been submitted to the United States Patent and Trademark Office (USPTO). The inventions are dated based on the date of their first patent application. Clean innovation refers to patents in areas such as renewable energy generation and electric vehicles, while dirty innovation refers to fossil-based energy generation and internal combustion engines. The authors employ a novel nonparametric test against pairwise differences, especially in tail dependence structures, which we measure with tail copulas, thereby avoiding the possibility of parametric misspecification. This method also permits to examine the effects of various interventions and economic conditions on different portions of the distribution, with a particular emphasis on tail dependence. The authors identify nonlinear dependence structures between clean innovation, public policies, and economic determinants like the oil price and recession. By comparing the effects of clean and dirty innovation, we can determine whether the effect on clean innovation is distinct. The findings indicate that the tightening of environmental policies since the early 1990s has statistically and economically contributed to the increase in clean innovation. The findings can bolster public support for green R&D. In addition, they suggest that green policies may be able to increase the knowledge diffusion of clean innovation

Regime-Dependent Financial Risk Transmission and Connectedness in MENA Economies: A Smooth Transition Threshold Vector Autoregressive Analysis

This study examines the impact of global financial market conditions on risk connectedness and transmission among MENA economies. Using weekly stock market volatilities and a smooth transition threshold vector autoregressive model, the authors analyze risk transmission under varying financial stress levels. Results show stronger risk interdependency during high-stress periods, with Kuwait, Oman, Qatar, Saudi Arabia, Turkey, and the UAE as net risk transmitters. The regime-dependent model reveals stronger risk transmission compared to the overall mean-based VAR model

The Relationship Between The Inflation Rate And Inequality Across U.S. States: A Semiparametric Approach

This paper uses a cross-state panel for the United States over the 1976–2007 period to assess the relationship between income inequality and the inflation rate. Employing a semiparametric instrument variable (IV) estimator, we find that the relationship depends on the level of the inflation rate. A positive relationship occurs only if the states exceed a threshold level of inflation rate. Below this value, inflation rate lowers income inequality. The results suggest that a nonlinear relationship exists between income inequality and the inflation rate. © 2018 Springer Science+Business Media B.V., part of Springer Natur

Partisan Conflict and Income Inequality in the United States: A Nonparametric Causality-in-Quantiles Approach

This paper examines the predictive power of a partisan conflict on income inequality. Our study contributes to the existing literature by using the newly introduced nonparametric causality-in-quantile testing approach to examine how political polarization in the United States affects several measures of income inequality and distribution overtime. The study uses annual time-series data between the periods 1917–2013. We find evidence in support of a dynamic causal relationship between partisan conflict and income inequality, except at the upper end of the quantiles. Our empirical findings suggest that a reduction in partisan conflict will lead to a reduction in our measures of income inequality, but this requires that inequality is not exceptionally high

Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach

This study introduces a novel time-varying parameter vector autoregression (TVP-VAR) based extended joint connectedness approach in order to characterize connectedness of 11 agricultural commodity and Crude Oil futures prices spanning from July 1, 2005 to May 1, 2020. Our results reveal that the system-wide dynamic connectedness is heterogeneous over time and driven by economic events. Peaks have been reached during the Global Financial Crisis, European Governmental Debt Crisis, and the COVID-19 pandemic. Further findings show that commodities such as Crude Oil, Grains, Livestock, Sugar, and Soybean Oil tend to be the main net transmitters of shocks while Corn, Lean Hogs, Soybeans, Cattle, and Wheat are the main receivers of shocks. Pairwise connectedness on the other hand shows that Crude Oil not only affects other commodity markets, but is also equally responsive to innovations that take place in most of these markets explaining the high interconnectedness of the network. Finally, we illustrate the importance of the chosen normalization technique employed in the connectedness framework as the retrieved findings have important implications for investors to design strategies for optimization of portfolio and asset allocation, reduction in downside risk along with hedging strategies. The full implementation and replication code is available at: https://github.com/GabauerDavid/ConnectednessApproach

On the Nonlinear Causality Between Inflation and Its Uncertainty in G-3 Countries

This study examines the dynamic relationship between monthly inflation and inflation uncertainty in Japan, the US and the UK by employing linear and nonlinear Granger causality tests for the 1957:01-2006:10 period. Using a generalised autoregressive conditional heteroskedasticity (GARCH) model to generate a measure of inflation uncertainty, the empirical evidence from the linear and nonlinear Granger causality tests indicate a bi-directional causality between the series. The estimates from both the linear vector autoregressive (VAR) and nonparametric regression models show that higher inflation rates lead to greater inflation uncertainty for all countries as predicted by Friedman (1977). Although VAR estimates imply no significant impact, except for Japan, nonparametric estimates show that inflation uncertainty raises average inflation in all countries, as suggested by Cukierman and Meltzer (1986). Thus, inflation and inflation uncertainty have a positive predictive content for each other, supporting the Friedman and Cukierman-Meltzer hypotheses, respectively. JEL classification codes: C22, E31

INTERNATIONAL LABOUR FORCE PARTICIPATION RATES BY GENDER: UNIT ROOT OR STRUCTURAL BREAKS?

This paper examines the possibility of unit roots in the presence of endogenously determined multiple structural breaks in the total, female and male labour force participation rates (LFPR) for Australia, Canada and the USA. We extend the procedure of Gil-Alana for a single structural break to the case of multiple structural breaks at endogenously determined dates using the principles suggested by Bai and Perron. We use the Robinson LM test to determine the fractional order of integration. We find that endogenously determined structural breaks render the total, female and male LFPR series stationary or at best mean-reverting