Infrastructure Indicators - Mongolia

Importance of Infrastructure Indicators: Infrastructure indicators are essential for evaluating the efficiency of the infrastructure sector across various modes. These indicators measure infrastructure health, industry performance, and the movement of goods and people, offering insights into demand, capacity, and development.

Infrastructure and Basic Services: Strong infrastructure, including transport systems, electricity, sanitation, and drinkable water, is vital for economic development and quality of life. Adequate transport infrastructure ensures the efficient delivery of goods and services, while access to basic services is crucial for supporting communities and enabling sustainable growth.

Challenges and Adaptation in the Sector: The infrastructure sector faces challenges from regulations, air travel demand, e-commerce growth, technological advancements, and sustainability concerns. To overcome these challenges, stakeholders must adapt strategies, embrace innovation, and prioritize sustainable practices, ensuring the sector remains resilient and responsive to global needs.

Data coverage:

The dataset encompasses data from 152 countries. The charts depict the situation of each country in six different domains. These domains are socioeconomic indicators, macroeconomic indicators, health indicators, digital and connectivity indicators, consumption indicators, as well as infrastructure indicators. Within these domains, various segments are covered, including demography, economic measures, economic inequality, employment, consumption, health determinants, and much more.

Modeling approach:

The composition of each domain follows a comprehensive approach that combines both top-down and bottom-up methodologies, with each domain and segment being guided by a specific rationale. To evaluate the situation of these six domains within each country, we rely on pertinent indicators and data from reputable international institutions, local national statistical offices, industry associations, and leading private institutions. Additionally, we undertake data processing procedures to address issues such as missing timelines, outliers, and data inconsistency. Our data processing incorporates advanced statistical techniques, including interpolation, exponential moving weighted average, and the Savitzky-Golay filter. These methods contribute to the refinement and enhancement of data quality.

Forecasts:

In our forecasting process, a wide range of statistical techniques is utilized based on the characteristics of the markets. For example, the S-curve function is employed to forecast the adoption of new technology, products, and services, aligning the forecast model with the theory of innovation adoption. Additionally, the data is forecasted using ARIMA with and without seasonality considerations, exponential trend smoothing, and the Compound Annual Growth Rate (CAGR), with the option to incorporate adjustment factors when necessary. Lastly, ML algorithms such as KNN and XGBoost are used in combination with ARIMA for time series forecasting and missing data estimation. These techniques enable accurate and reliable forecast methods tailored to the unique characteristics of the data in each market and country.

Additional notes:

The data is updated twice per year or every time there is a significant change in their dynamics. The impacts of the COVID-19 pandemic and of the Russia/Ukraine war are considered at a country-specific level.