GDP Deflator in Indonesia increased to 175.81 points in the first quarter of 2025 from 172.14 points in the fourth quarter of 2024. This dataset provides - Indonesia GDP Deflator - actual values, historical data, forecast, chart, statistics, economic calendar and news.

GDP Deflator in India increased to 172.60 points in 2024 from 170.20 points in 2023. This dataset provides - India GDP Deflator - actual values, historical data, forecast, chart, statistics, economic calendar and news.

GDP Deflator in Canada increased to 128.20 points in the fourth quarter of 2024 from 127.10 points in the third quarter of 2024. This dataset provides - Canada GDP Deflator - actual values, historical data, forecast, chart, statistics, economic calendar and news.

GDP Deflator in Thailand decreased to 159.25 points in the first quarter of 2025 from 164.98 points in the fourth quarter of 2024. This dataset provides - Thailand GDP Deflator - actual values, historical data, forecast, chart, statistics, economic calendar and news.

BackgroundThe reserve of a country is a reflection of the strength of fulfilling its financial liabilities. However, during the past several years, a regular variation of the total reserve has been observed on a global scale. The reserve of Bangladesh is also influenced by several economic and financial indicators such as total debt, net foreign assets, net domestic credit, inflation GDP deflator, net exports (% of GDP), and imports of goods and services (% of GDP), as well as foreign direct investment, GNI growth, official exchange rate, personal remittances, and so on. Therefore, the authors aimed to identify the nature of the relationship and influence of economic indicators on the total reserve of Bangladesh using a suitable statistical model.Methods and materialsTo meet the objective of this study, the secondary data set was extracted from the World Bank’s website which is openly accessible over the period 1976 to 2020. Moreover, the model used the appropriate splines to describe the non-linearity. The performance of the model was evaluated by the Akaike information criterion (AIC), Bayesian information criterion (BIC), and adjusted R-square.ResultsThe total reserve of Bangladesh gradually increased since 2001, and it reached its peak in 2020 which was 43172 billion US dollars. The data were first utilized to build a multiple linear regression model as a base model, but it was later found that the model has severe multicollinearity problems, with a maximum value of VIF for GNI of 499.63. Findings revealed that total debt, inflation, import, and export are showing a non-linear relationship with the total reserve in Bangladesh. Therefore, the authors applied the Generalized Additive Model (GAM) model to take advantage of the nonlinear relationship between the reserve and the selected covariates. The overall response, which is linearly tied to the net foreign asset in the GAM model, will change by 14.43 USD for every unit change in the net foreign asset. It is observed that the GAM model performs better than the multiple linear regression.ConclusionA non-linear relationship is observed between the total reserve and different economic indicators of Bangladesh. The authors believed that this study will be beneficial to the government, monetary authorities also to the people of the country to better understand the economy.

"The resilience of the domestic economic systems of the countries along the Belt and Road reflects the level of resilience of the domestic economic systems of each country, and the higher the value of the data, the stronger the resilience of the domestic economic systems of the countries along the Belt and Road. The resilience of domestic economic systems includes macroeconomic development resilience, industrial and service sector development resilience, and the data products are prepared with reference to the World Bank statistical database, using GDP per capita, gross fixed capital formation as a percentage of GDP, inflation as measured by GDP deflator, and gross savings as measured by GDP deflator for countries along the Belt and Road from 2000 to 2019. The resilience products of the domestic economic system are prepared through a comprehensive diagnosis based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator, using year-on-year data of six indicators: GDP per capita, gross fixed capital formation as a percentage of GDP, gross savings as a percentage of GDP, industrial value added as a percentage of GDP, and service value added as a percentage of GDP. "The resilience dataset of the domestic economic systems of the countries along the Belt and Road is an important reference for analysing and comparing the resilience of the domestic economic systems of various countries.

Australia Real Interest Rate data was reported at 1.647 % pa in 2019. This records a decrease from the previous number of 3.370 % pa for 2018. Australia Real Interest Rate data is updated yearly, averaging 3.307 % pa from Dec 1961 (Median) to 2019, with 59 observations. The data reached an all-time high of 10.090 % pa in 1991 and a record low of -6.018 % pa in 1974. Australia Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.;International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.;;

Macroeconomics refers to the entire national economy or the national economy as a whole, as well as its economic activities and operational status. "The data set of macroeconomic development resilience of countries along the Belt and Road reflects the level of macroeconomic development resilience of the countries along the Belt and Road, and the higher the data value, the stronger the macroeconomic development resilience of the countries along the Belt and Road. The macroeconomic development resilience dataset is prepared with reference to the World Bank's statistical database, using year-on-year changes in four indicators: GDP per capita, gross fixed capital formation as a percentage of GDP, inflation as measured by the GDP deflator, and total savings as a percentage of GDP for countries along the "Belt and Road" from 2000 to 2019. The macroeconomic development resilience product was prepared through a comprehensive diagnosis based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator. "The resilience dataset of macroeconomic development of countries along the Belt and Road is an important reference for analysing and comparing the resilience of macroeconomic development of various countries.