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An assessment of debt dynamics and its sustainability is very important in formalizing prudent and effective macroeconomic policies especially for the economies with weak macroeconomic fundamentals and alarming debt levels. Keeping in view the recent debt escalation in Pakistan, this study aims to explore the important factors that influence the public debt dynamics in case of Pakistan and to evaluate its sustainability. This study applies the debt dynamic approach for empirical assessment of drivers of changing debt levels and analysis of public debt sustainability. Furthermore, ARDL approach is utilized to study the short- and long-run debt dynamics using historic data from 1975 to 2021. This study is distinct from already existing work on debt assessment in Pakistan as it examines both important dimensions of public debt (determinants & stability) by employing the novel dynamic debt modelling approach and using most recent data. The study finds a positive and significant impact of fiscal deficit, exchange rate depreciation and interest rate on public debt in Pakistan. The debt sustainability analysis also reveals the instability of public debt for the entire study period except for few years. The regression results corroborate with the findings from stability analysis, and the main driving forces for increasing the debt burden of the country are found to be the fiscal indiscipline along with the rising cost on account of ER depreciation and higher interest rates. [ABSTRACT FROM AUTHOR]

In social sciences, a plethora of studies utilize nominal data to establish the relationship between the variables. This, in turn, requires the correct use of correlation technique. The choice of correlation technique depends upon the underlying assumptions and power of the test of significance. The objective of the research is to explore the best measure of association for nominal data in terms of size, power and bias in estimation. Monte Carlo simulations reveal that the Phi and Pearson correlation statistics performs equally well in terms of size, power, and bias for naturally dichotomous variables. When both variables are artificially dichotomized, the Tetrachoric statistic has an edge in terms of bias to Pearson correlation statistic. If one variable is continuous and other is artificially dichotomized, the Biserial correlation measure turns out to be less biased as compared to Pearson statistic although both statistics exhibit similar power and size properties. If one variable is continuous and other is naturally dichotomized, it is hard to choose between the Point Biserial and Pearson correlation measures. Finally, if one variable is naturally dichotomous and other is artificially dichotomized, correlation coefficient V is compared with Pearson, Phi and Tetrachoric correlation techniques in terms of bias in estimate. The results indicate that the Tetrachoric statistic considerably overestimates the correlation value against non-normal distributions. Pearson and Phi correlation slightly underestimate the correlation value. In contrast, the correlation statistic V perform well. [ABSTRACT FROM AUTHOR]

Many parametric statistical inferential procedures in finite samples depend crucially on the underlying normal distribution assumption. Dozens of normality tests are available in the literature to test the hypothesis of normality. Availability of such a large number of normality tests has generated a large number of simulation studies to find a best test but no one arrived at a definite answer as all depends critically on the alternative distributions which cannot be specified. A new framework, based on stringency concept, is devised to evaluate the performance of the existing normality tests. Mixture of t-distributions is used to generate the alternative space. The LR-tests, based on Neyman–Pearson Lemma, have been computed to construct a power envelope for calculating the stringencies of the selected normality tests. While evaluating the stringencies, Anderson–Darling (AD) statistic turns out to be the best normality test. [ABSTRACT FROM AUTHOR]

Consumer Confidence Index (CCI) plays an important role through providing decision makers and economic forecasters with required information about present and future economic condition. These indices play a unique role in determining public policies as well as business decisions. Consumer confidence index defines the degree of optimism on the current state of the economy that consumers are expressing through their activities of saving and spending which lead to economic growth of the country. Positive changes in consumer confidence should lead to the economic growth while negative changes impede the economic growth of the countries. This study is an attempt to empirically evaluate the link between the CCI and economic growth of the selected European countries: United Kingdom, Germany, France, Denmark and Netherland. Panel co-integration procedures are applied to establish the long run relationship between the CCI and economic growth for the period of 1996 (1)-2012 (4). Empirical results show the existence of the long run relationship between consumer confidence and economic growth. [ABSTRACT FROM AUTHOR]