Search results

The optimum response to the different stages of a major burns incident is still not established. The fire in a café in Volendam on New Year's Eve 2000 was the worst incident in recent Dutch history and resulted in mass burn casualties. The fire has been the subject of several investigations concerned with organisational and medical aspects. Based on the findings in these investigations, a multidisciplinary research group started a consensus study. The aim of this study was to further identify areas of improvement in the care after mass burns incidents.

The consensus process comprised three postal rounds (Delphi Method) and a consensus conference (modified nominal group technique). The multidisciplinary panel consisted of 26 Dutch‐speaking experts, working in influential positions within the sphere of disaster management and healthcare.

In response to the postal questionnaires, consensus was reached for 66 per cent of the statements. Six topics were subsequently discussed during the consensus conference; three topics were discussed within the plenary session and three during subgroup meetings. During the conference, consensus was reached for seven statements (one subject generated two statements). In total, the panel agreed on 21 statements. These covered the following topics: registration and evaluation of disaster care, capacity planning for disasters, pre hospital care of victims of burns disasters, treatment and transportation priorities, distribution of casualties (including interhospital transports), diagnosis and treatment and education and training.

In disaster medicine, the paper shows how a consensus process is a suitable tool to identify areas of improvement of care after mass burns incidents.

This study reexamines the sustainability of fiscal policy in Sweden.

To test the sustainability of fiscal policy, two approaches are used; the methodology of Kejriwal and Perron (2010), testing for multiple structural changes in a cointegrated regression model and time-varying cointegration test of Bierens and Martins (2010), and Martins (2015).

Using the first approach of testing for multiple structural changes in a cointegrated regression model, the results indicate that government spending and revenue are cointegrated with two breaks. An estimation of a two-break long-run model shows that the slope coefficient increases from 0.678 to 0.892 from the first to the second regime, implying that fiscal deficits were weakly sustainable in the first two regimes, from 1800 to 1943, and from 1944 to 1974. Further, results from time-varying cointegration test indicate that cointegration between spending and revenue in Sweden is time-varying. Fiscal deficits were found to be unsustainable for the periods 1801–1811, 1831–1838, 1853–1860 , 1872–1882, 1897–1902, 1929–1940 and 1976–1982 and weakly sustainable over the rest of the study period.

A number of implications arise from this study: (1) Accounting for breaks in cointegration analysis and in the estimation of the level relationship between spending and revenue is very important because ignoring breaks may lead to an overestimated slope coefficient and hence a bias on the magnitude of fiscal deficit sustainability. (2) In testing for cointegration between spending and revenue, assuming a constant cointegrating slope when it is actually time-varying can also be misleading because deficits can be sustainable for a period of time and unsustainable over another period.

The contribution of this study is three-fold; first, the study uses a long series of annual data spanning over a period of two centuries, from 1800 to 2011. Second, because of the importance of structural change in economics, to examine the existence of a level relationship between spending and revenue, the study uses the methodology of Kejriwal and Perron (2010) to test for multiple structural changes in a cointegrated regression model, as well as time-varying cointegration of Bierens and Martins (2010) and Martins (2015).

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

This study reexamines fiscal deficit sustainability in South Africa.

The study applies three cointegration testing approaches, namely testing for multiple structural changes in a cointegrated regression model, time-varying cointegration test and asymmetric cointegration test.

The results point to the existence of a level relationship between government revenue and spending. In addition, the long-run equilibrium relationship between government revenue and spending in South Africa is found to be characterized by breaks. As such, assuming a constant cointegrating slope may be misleading. Results from time-varying cointegration and an estimation of a cointegrated two-break model indicate that cointegrating coefficient has been time-varying but has remained less than 1 for the entire study period, indicating that fiscal deficits have been weakly sustainable. This finding is also confirmed by the results from an estimated asymmetric error correction model.

In view of the findings, authorities should put in place policies to improve the fiscal budgetary stance and reinforce the sustainability of the fiscal deficits in South Africa. Among other things, South Africa could undertake reforms to state-owned companies to reduce their reliance on public funds, slow down the pace of the public sector wage growth and devise effective economic measures to boost long-term growth. In addition, tax compliance and other revenue collection measures should be enhanced for additional tax revenue.

The contribution of this study is twofold; first, the study uses a long series of annual data spanning over a century, from 1913 to 2020. Indeed, cointegration is better modeled using long spans of time series data. Second, to examine the existence of a level relationship between spending and revenue, the study uses cointegration tests which allow capturing time-variation in the cointegrating slope coefficient, and accounting for asymmetries in the relationship between government spending and revenue. It is important to allow for time-variation in the cointegrating slope coefficient, especially when it has been hardly treated in the empirical literature on fiscal deficit sustainability. Allowing for time-variation in the cointegrating slope coefficient helps us to analyze fiscal deficit sustainability by periods of time. Indeed, the degree of fiscal sustainability can change from one time period to another.

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross-sectional and panel data models, and in this chapter we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point-identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a “non-standard” exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor.

We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also show that, despite our nonparametric approach, our tests can detect local alternatives to conditional independence that decay to zero at the parametric rate. Our approach gives the first nonparametric tests for time-series conditional independence that can detect local alternatives at the parametric rate. Monte Carlo simulations suggest that our tests perform well in finite samples. We apply our test to test for a key identifying assumption in the literature on nonparametric, nonseparable models by studying the returns to schooling.

This study proposed an optimal model to examine the relationship between the Bitcoin price and six macroeconomic variables – the Bitcoin price, Standard and Poor's 500 volatility index, US treasury 10-year yield, US consumer price index, gold price and dollar index. It also examined the effectiveness of the vector error correction model (VECM) in analyzing the interrelationship among these variables. The authors employed the following approach: first, the authors sampled the period August 2010–February 2022. This is because Bitcoin achieved a market capitalization of more than US$1 tn over this period, gaining market attention and acceptance from retail, corporate and institutional investors. Second, the authors employed a VECM with the six macroeconomic variables. Finally, the authors expanded the long-run equilibrium relationship (time-invariant cointegration)-based VECM to develop a time-varying cointegration (TVC) VECM. The authors estimated the TVC VECM using the Chebyshev polynomial specification based on various information criteria. The results showed that the Bitcoin price can be modeled with the VECM (p = 1, r = 1). The TVC approach generated more explanatory power for Bitcoin pricing, indicating the effectiveness of the approach for modeling the long-run relationship between Bitcoin price and macroeconomic variables.