This study investigates the causal impact of foreign direct investment (FDI) on economic growth in Turkey using annual time series data spanning the period 1980–2020. Economic growth is proxied by real per capita gross domestic product (GDP). The dataset was sourced from internationally reputable institutions such as the World Bank and TurkStat, and all variables were log-transformed to ensure suitability for statistical analysis. Due to the presence of variables with different orders of integration, the Toda–Yamamoto causality approach—robust to variables with mixed integration orders—was employed. The stationarity of the series was tested using the Augmented Dickey-Fuller and Phillips-Perron tests, while the optimal lag length was determined based on information criteria. Diagnostic tests for autocorrelation, heteroskedasticity, and structural stability were conducted to ensure model adequacy. The empirical findings reveal a statistically significant unidirectional causality running from FDI to economic growth at the 1% significance level, with no evidence of reverse causality. These results indicate that FDI functions not only as a source of external capital, but also as a strategic driver of growth by enhancing productivity, facilitating knowledge and technology transfer, and driving structural transformation. By incorporating a long-term dataset and accounting for the macroeconomic structure, this study offers a methodologically robust and contextually grounded contribution to the literature on the FDI-growth nexus in developing economies

The rapid development of digital technologies has changed the way data is collected, stored, and analyzed. Big Data has created new opportunities and challenges for econometric research. This article discusses the integration of econometric methods with big data analysis, the methodological innovations needed, and the results of empirical economic research. The study also highlights modern tools and ideas that help econometricians manage the complexity and scale of large data sets while maintaining model accuracy and interpretability. It also highlights how the combination of traditional econometric thinking and computational methods can improve the quality and scope of economic analysis in the modern digital economy

This article analyzes the development trends of communication and informatization services for the population in the Kashkadarya region and develops multifactor empirical models for forecasting these services. Based on regional statistical data, the key influencing factors are identified, and their impact on service volume is assessed using econometric methods. The study employs regression analysis, time series analysis, and modern econometric approaches in model construction. The obtained results allow forecasting the future dynamics of communication and informatization services and provide practical recommendations for sector development. The findings are significant for enhancing regional economic growth, improving digital infrastructure, and ensuring effective management of the service market

This study analyses historical data (2000–2024) to forecast Uzpromstroybank’s deposit base and total resource trends using simple OLS regression models. A linear time-trend model is estimated for both series to capture long-run growth. The regression results show strong, significant upward trends (high R², statistically significant coefficients) for deposits and resources, indicating robust growth. Forecasts generated by extrapolating these trends suggest continued expansion of the bank’s deposits and resources in the short term. The findings are relevant for bank management and policymakers, as they highlight the trajectory of funding sources in Uzbekistan’s banking sector. Limitations include the simplicity of the linear model and potential structural changes, nonetheless, the results provide a baseline projection and underline the importance of improving deposit mobilization and financial sector reforms.

This study investigates the causal nexus of imports, exports, and economic growth in Uzbekistan using time series evidence and advanced econometric techniques. The empirical analysis begins with unit root testing by employing the Augmented Dickey-Fuller (ADF) test, followed by the Johansen cointegration test to establish long-run equilibrium relationships between the variables. A Vector Error Correction Model (VECM) is employed to test short- and long-run causality. The results reveal unidirectional causality from imports to exports, changes in import activity significantly influence export performance. The study further finds bidirectional (bicausal) relations between imports and economic growth and between exports and economic growth. The implications of these findings are that both trade components do play a significant role in the economic development of Uzbekistan. The results emphasize the importance of balanced trade policy that benefits both growth in imports and promotion of exports in order to achieve long-term economic growth.

The purpose of this study is to study the relationship between factors affecting the expansion of Islamic investment projects in the regions. The time series data from 2004 to 2024 on the number of Islamic investment projects in the regions and the independent factors that may affect were analyzed. The VAR model was used in our study. According to the results of our study, it was found that the selected independent factors are positively related to Islamic investment projects in the short term.

This article focuses on analyzing the growth of China's industry during the period from 1992 to 2023 and calculating its forecast for the next 5 years. The aim of the article is to examine the short-term and long-term relationships between China's industrial development and the economic indicators of the selected factors. A quantitative method was applied in the study. To analyze the industry that characterizes economic development and the macroeconomic factors influencing this indicator, econometric time series models were used, including OLS (Ordinary Least Squares) and VAR.