Search results

The purpose of this paper is to study the volatility spillover among 33 worldwide sovereign Credit Default Swap (CDS) markets and their underlying bond markets.

In contrast to prior studies, the authors incorporate heteroscedasticity, asymmetric leverage effects and long-memory features of sovereign credit spreads simultaneously through a bivariate FIEGARCH model and a Bayesian cointegrated vector autoregressive model.

Similar to the literature, the findings confirm that strong evidence of credit risk spillover between credit markets is accentuated during two recent crisis periods. However, the country-by-country analysis indicates that countries exhibit different sensitivity levels and divergent reactions to financial shocks. Further, the authors show that the bidirectional interrelationship evolves over time and across countries emphasizing the necessity of time-varying national regulatory policies and trading positions.

Based on a large data set that covers the recent two financial crises and using complex methods, the work focuses on sovereign tensions that have repercussions on banks’ solvency and refinancing conditions. Yet, the study is a hot topic since that during crisis periods in the financial markets, direct and indirect interconnections increase between sovereign risk and banking risk. Using new econometric approaches, the results show that each country exhibits a different behavior toward the credit risk which is relevant to both portfolio managers and policy makers. The time-varying spillover effects detected between markets are an accurate indicator of financial stability, allowing policy makers to put in place personalized economic policies. On the other hand, markets’ participants could take advantages of the results by adjusting their trading and hedging positions on the dynamic co-movements. The findings reveal, as well, that the sovereign crisis has more weakened the global financial and banking system than the subprime crisis. The authors previously tackled the cross-country contagion phenomenon in the CDS markets, and this manuscript builds on the prior study to enhance the obtained results.

The purpose of this paper is to re-examine the long-run relationship between radiative forcing (including emissions of carbon dioxide, sulphur oxides, methane and solar radiation) and temperatures from a structural time series modelling perspective. The authors assess whether forcing measures are cointegrated with global temperatures using the structural time series approach.

A Bayesian approach is used to obtain estimates that represent the uncertainty regarding this relationship. The estimated structural time series model enables alternative model specifications to be consistently compared by evaluating model performance.

The results confirm that cointegration between radiative forcing and temperatures is consistent with the data. However, the results find less support for cointegration between forcing and temperature data than found previously.

Given considerable debate within the literature relating to the “best” way to statistically model this relationship and explain results arising as well as model performance, there is uncertainty regarding our understanding of this relationship and resulting policy design and implementation. There is a need for further modelling and use of more data.

There is divergence of views as to how best to statistically capture, explain and model this relationship. Researchers should avoid being too strident in their claims about model performance and better appreciate the role of uncertainty.

The results of this study make a contribution to the literature by employing a theoretically motivated framework in which a number of plausible alternatives are considered in detail, as opposed to simply employing a standard cointegration framework.

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 chapter contributes to the growing global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multicountry setting, and the results are compared to those obtained from standard single country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations are exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.45 when the rise in output is due to a fiscal expansion as compared to 0.76 when the rise in output follows from a favorable output shock.

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters that characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.

An understanding of the determination of real wages is crucial in analysing the determination of the natural rate of unemployment or NAIRU. Uses cointegration techniques to examine a core theoretical model of the long‐run determinants of real wages involving unit labour costs, unemployment, union power and the replacement ratio. Considers the different measures of union power and the duration of unemployment and alternative specifications involving the “wedge” but a robust cointegrating relationship is not found. These results can be interpreted in several ways: concepts such as union power or the “generosity” of benefits may be measured inadequately; the theoretical understanding of the long‐run determinants of real earnings may remain seriously incomplete; alternatively the short spans of data examined may be insufficient for the application of cointegration techniques, although the sample sizes examined here are fairly typical of most macroeconomic time series.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

Analyses and checks the annual forecasts produced each autumn from four prominent UK economic modelling organizations. Compares these forecasts with those of three Bayesian vector‐autoregressive models. Examines the accuracy for each set of forecasts up to four years ahead and for different horizons. Examines the direction of the forecasts and the effect of forming simple combinations of the different forecasts. Finds evidence that while the BVAR forecasts are inferior to those from the economic models, they contain information which could be used in order to improve the other forecasts.

This paper proposes a Bayesian procedure to investigate the purchasing power parity (PPP) utilizing an exponential smooth transition vector error correction model (VECM). Employing a simple Gibbs sampler, we jointly estimate the cointegrating relationship along with the nonlinearities caused by the departures from the long-run equilibrium. By allowing for nonlinear regime changes, we provide strong evidence that PPP holds between the US and each of the remaining G7 countries. The model we employed implies that the dynamics of the PPP deviations can be rather complex, which is attested to by the impulse response analysis.

The purpose of this paper is to analyze the effects of corruption on economic growth, human development and natural resources in Latin American and Nordic countries.

Using the hierarchical prior of Gelman et al. (2003), a Bayesian panel Vector AutoRegression (VAR) model is estimated. In addition, two alternative approaches are considered, namely, a panel error correction VAR model and an asymmetric panel VAR model.

The results reveal some relevant contrasts: (1) in Latin America there is support for the sand the wheels hypothesis in Bolivia and Chile, support for the grease the wheels hypothesis in Colombia and no significant impact of corruption on growth in Brazil and Peru, while in Nordic countries the response of growth to shocks in corruption is negative in all cases; (2) corruption negatively affects human development in all countries from both regions; (3) corruption tends to spur natural resources sector in Latin American countries, while it is detrimental for natural resources sector in Nordic countries.

The panel VAR approach uses recursive scheme identification. The authors have analyzed robustness using alternative ordering of the variables. The authors also have followed two alternatives suggested by the Referee: a panel error correction VAR model and a panel asymmetric VAR model. However, another more sophisticated identification scheme could be used. Also other variables could be introduced in the VAR model.

Regardless of the issue of the “grease” vs the “sand the wheels” debate, corruption should be reduced because it is anyway harmful for human development. The differences in the results for Latin American and Nordic countries show that the effects of corruption have to be assessed considering the different institutional and economic conditions of the countries analyzed.

Governments should seek to reduce corruption because, despite corruption can have mixed effects on economic growth in some contexts, it is anyway harmful for human development. Besides, the finding that in some Latin American countries more activity in the extractive industries is generated by means of corruption confirm the association between corruption and extractivism found by Gudynas (2017) and can explain why there are issues of environmental damage and social conflict linked to natural resources in those countries.

The present study contributes to the literature by presenting evidence on the effects of corruption on growth, human development and natural resources sector in Latin American and Nordic countries. It is the first study on economics of corruption which directly compares Latin American and Nordic countries. This is relevant because there are important differences between both regions since Latin American countries tend to suffer from widespread corruption, while the Nordic ones have a high level of transparency. It is also the first in using a Bayesian panel VAR approach in order to evaluate the effects of corruption.