MODELING TOURISM–ENVIRONMENT RELATIONSHIP IN AUSTRALIA: DOES ASYMMETRY MATTER?

Prior empirical studies have employed various econometric estimation techniques to study the environmental effect of tourism demand. Prominently, these econometric modeling techniques implicitly assume that the environmental effect of tourism is symmetrical, which could sometimes be problematic. This study, therefore, utilized two econometric estimation techniques, namely, the Pesaran et al. ( 2001 ). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326) symmetric autoregressive distributed lag (ARDL) and Shin et al. ( 2014 ). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in Honor of Peter Schmidt, pp. 281–314. New York: Springer) nonlinear ARDL (NARDL) estimation technique to disentangle the effect of tourism demand on carbon emissions in Australia. The results from the symmetric ARDL model reveal that tourism demand significantly increases carbon emissions in the long run, indicating that a 1% increase in tourism demand contributes to a 0.155% increase in carbon emissions in the long run. Contrarily, the NARDL model shows that a positive shock (an increase) in tourism demand reduces carbon emissions while a negative shock (a decrease) in tourism demand increases carbon emissions in the long run. From the NARDL estimate, a 1% increase in tourism demand is associated with a 0.220% decline in carbon emissions, while a 1% decrease in tourism demand increases carbon emissions by 0.250%. Therefore, I argue that carbon emissions depend not only on the size of tourism demand but also on the pattern — thus the increase and decline — of tourism demand. The implications of these results for policy are discussed.

Testing for structural break in aggregated consumption function of Russian households

The paper considers a simple aggregated consumption function for Russian economy in which households consume a constant fraction of a permanent income. The value of this fraction is estimated by households within the framework of the adaptive expectations process based on the dynamics of GDP at constant consumption prices. Testing for a structural break at an unknown date in the parameter of the propensity to consume is performed. The results of econometric estimation, taking into account the presence of an endogeneity in the regression equation, demonstrate that after 2014 there was a structural break, as a result of which the parameter of the propensity to consume of permanent GDP decreased by 6.5—9.2%.

Predicting mortality for Covid-19 in the US using the delayed elasticity method

The evolution of the pandemic caused by COVID-19, its high reproductive number and the associated clinical needs, is overwhelming national health systems. We propose a method for predicting the number of deaths, and which will enable the health authorities of the countries involved to plan the resources needed to face the pandemic as many days in advance as possible. We employ OLS to perform the econometric estimation. Using RMSE, MSE, MAPE, and SMAPE forecast performance measures, we select the best lagged predictor of both dependent variables. Our objective is to estimate a leading indicator of clinical needs. Having a forecast model available several days in advance can enable governments to more effectively face the gap between needs and resources triggered by the outbreak and thus reduce the deaths caused by COVID-19.