The paper is devoted to modelling the corruption perception index in panel data framework. As corruption index is bounded from below and above, traditional econometric multiple regression will produce a bad quality model. In order to correct that, we propose a mathematical framework for modelling bounded variables implementing a logistic function. It is shown that corruption is best explained by GDP per capita and all other major macroeconomic indicators cannot add any statistically significant improvement to the models' accuracy. Thus, we assume, that society wealthiness facilitates the reduction of corruption acts. Indeed, if some individual lives in a society that does not experiences almost any shortage of resources of whatever kind, the less interested this person is in getting wealthier by applying some corruption schemes. These methods are rather popular in less wealthy countries, where temptation to engage into corruption is higher, since it can drastically increase individual's utility function. Therefore, in this research we assert, that the growth of wealth in a society makes corruption recede and not the other way around (reducing corruption helps increase GDP per capita). However, the most counterintuitive finding of this research is the fact, that GDP per capita, adjusted by purchasing power parity, produces the model of a worse quality then just using plain GDP per capita. This fact can be tentatively explained by the flaws in the methodology of calculating these adjustments, since the basket of goods varies drastically across the countries.

Growing energy demand but stagnant production followed by volatile exchange rate leads Pakistan to energy imbalances and potential economic contraction. Yet, studies on sectoral energy imports are limited and inconclusive without accessing the asymmetric effect of currency fluctuations. We examine the impacts of Pakistani rupee volatility on monthly energy imports based on the nonlinear autoregressive distributed lag (NARDL) estimations. Augmented Dickey–Fuller and Phillips–Perron tests were used to conduct unit root testing, and the bound testing approach was used to examine the long-term cointegration. The long-run asymmetry was tested with the Wald test, and using the NARDL model, we examined both short-run and long-run asymmetric effects of exchange rate volatility on energy imports. The bound test was established and supported through ECMt−1 (t-test), cointegrating the relationship between exchange rate volatility and energy imports in a long term. Among others, both short-run and long-run asymmetric effects were found for crude oil, coal, electricity, and petroleum products. Rupee depreciation increased crude oil and electricity imports, while the appreciation effects were insignificant. Overall, the empirical assessment reveals that the foreign exchange volatility effect is sectoral specific and asymmetric in Pakistan. It offers new insights into re-strategizing the energy policy and refining the import substitution plan.

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