The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets.

International trade contributes a significant role in economic development of a country. A country that performs well in international trade signifies economic stability. Pakistan has maintained International economic relationships with other countries over the years. This study examined the association of Gross domestic product (GDP) with exports, imports and trade openness in the perspective of Pakistani economy from 1985 to 2023 by using Ordinary Least Square (OLS) method. Descriptive statistics, unit root test and correlogram of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) were determined. The data has been evaluated using EViews software. The outcomes of the research revealed that all series Exports, Imports and Trade Openness have significant impact on GDP. Moreover, Imports and exports have positive effect on GDP while trade Openness has negative impact on GDP. All the series were stationary at first difference I(1) whereas Residuals were stationary at level I(0). Through which it was concluded that Cointegration exist. Overall OLS model was best fit. But CUSUM square showed that model was not stable and have some structure breaks. It is recommended that researchers should extend this study and remove structure breaks by using state space models, Markov Regime switching and threshold models.