http://hdl.handle.net/2263/2391 2018-04-26T22:24:36Z 2018-04-26T22:24:36Z Lin, Jiangpeng Inglesi-Lotz, Roula Chang, Tsangyao http://hdl.handle.net/2263/64722 2018-04-26T01:07:40Z 2018-04-01T00:00:00Z

Revisiting CO2 emissions convergence in G18 countries Lin, Jiangpeng; Inglesi-Lotz, Roula; Chang, Tsangyao This study revisits whether CO2 emissions converge in G18 countries over the period of 1950–2013. To work on this empirical analysis, we employ a more powerful quantile unit root test with per capita CO2 emissions. While conventional unit root tests fail to reject convergence in CO2 emissions in these G18 countries, quantile unit root test results demonstrate CO2 emissions converged in 5 of these G18 countries (i.e., Australia, Brazil, Canada, Germany, and India). Our empirical results have important policy implications for the governments of G18 countries to direct efficient and effective energy policies to reduce the CO2 emissions.

2018-04-01T00:00:00Z Segnon, Mawuli Gupta, Rangan Bekiros, Stelios Wohar, Mark E. http://hdl.handle.net/2263/64712 2018-04-25T01:07:36Z 2018-04-01T00:00:00Z

Forecasting US GNP growth : the role of uncertainty Segnon, Mawuli; Gupta, Rangan; Bekiros, Stelios; Wohar, Mark E. A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.

2018-04-01T00:00:00Z Balcilar, Mehmet Gupta, Rangan Van Eyden, Renee Thompson, Kirsten L. Majumdar, Anandamayee http://hdl.handle.net/2263/64711 2018-04-25T01:07:34Z 2018-03-01T00:00:00Z

Comparing the forecasting ability of financial conditions indices : the case of South Africa Balcilar, Mehmet; Gupta, Rangan; Van Eyden, Renee; Thompson, Kirsten L.; Majumdar, Anandamayee In this paper we test the forecasting ability of three estimated financial conditions indices (FCIs) with respect to key macroeconomic variables of output growth, inflation and interest rates. We do this by forecasting the aforementioned macroeconomic variables based on the information contained in the three alternative FCIs using a Bayesian VAR (BVAR), nonlinear logistic vector smooth transition autoregression (VSTAR) and nonparametric (NP) and semi-parametric (SP) regressions, and compare the results with the standard benchmarks of random-walk, univariate autoregressive and classical VAR models. The three FCIs are constructed using rolling-window principal component analysis (PCA), dynamic model averaging (DMA) in the context of a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model, and a time-varying parameter vector autoregressive (TVP-VAR) model with constant factor loadings. Our results suggest that the VSTAR model performs best in the case of forecasting output and inflation, while a SP specification proves to be the best for forecasting the interest rate. More importantly, statistical testing for significant differences in forecast errors across models corroborates the finding of superior predictive ability of the nonlinear models.

2018-03-01T00:00:00Z Ji, Qiang Marfatia, Hardik A. Gupta, Rangan http://hdl.handle.net/2263/64710 2018-04-25T01:07:30Z 2018-04-01T00:00:00Z

Information spillover across international real estate investment trusts : evidence from an entropy-based network analysis Ji, Qiang; Marfatia, Hardik A.; Gupta, Rangan In this study, we unveil information spillover between international real estate markets using an entropy-based network approach for real estate investment trusts (REIT). Our novel approach is simple and yet flexible enough to accommodate the nature and extent of information spillover among several components of the global housing network. For a network of nine leading industrial economies, we unveil static and time-varying information spillover of REIT returns using total transfer entropy, pairwise net transfer entropy and directional (“From”, “To”) transfer entropy. Evidence suggests that the greatest pairwise transfer entropy is from the US to Australia, whereas France, the Netherlands, New Zealand and Singapore are the largest information recipients in the network. The time-varying evolution of total transfer entropy also exhibits a declining trend for the integration of global housing market during our sample period.

2018-04-01T00:00:00Z