| Literature DB >> 29167816 |
Yee Peng Chow1, Junaina Muhammad2, Bany Ariffin Amin Noordin2, Fan Fah Cheng2.
Abstract
This data article provides macroeconomic data that can be used to generate macroeconomic volatility. The data cover a sample of seven selected countries in the Asia Pacific region for the period 2004-2014, including both developing and developed countries. This dataset was generated to enhance our understanding of the sources of macroeconomic volatility affecting the countries in this region. Although the Asia Pacific region continues to remain as the most dynamic part of the world's economy, it is not spared from various sources of macroeconomic volatility through the decades. The reported data cover 15 types of macroeconomic data series, representing three broad categories of indicators that can be used to proxy macroeconomic volatility. They are indicators that account for macroeconomic volatility (i.e. volatility as a macroeconomic outcome), domestic sources of macroeconomic volatility and external sources of macroeconomic volatility. In particular, the selected countries are Malaysia, Thailand, Indonesia and Philippines, which are regarded as developing countries, while Singapore, Japan and Australia are developed countries. Despite the differences in level of economic development, these countries were affected by similar sources of macroeconomic volatility such as the Asian Financial Crisis and the Global Financial Crisis. These countries were also affected by other similar external turbulence arising from factors such as the global economic slowdown, geopolitical risks in the Middle East and volatile commodity prices. Nonetheless, there were also sources of macroeconomic volatility which were peculiar to certain countries only. These were generally domestic sources of volatility such as political instability (for Thailand, Indonesia and Philippines), natural disasters and anomalous weather conditions (for Thailand, Indonesia, Philippines, Japan and Australia) and over-dependence on the electronic sector (for Singapore).Entities:
Keywords: Asia Pacific; Macroeconomic; Volatility
Year: 2017 PMID: 29167816 PMCID: PMC5686458 DOI: 10.1016/j.dib.2017.11.015
Source DB: PubMed Journal: Data Brief ISSN: 2352-3409
Fig. 1Indicators for volatility as a macroeconomic outcome. Data source: Authors’ own calculations using data from IMF International Financial Statistics.
Fig. 2A. Indicators for domestic sources of volatility. Data sources: Authors’ own calculations using data from IMF International Financial Statistics and World Bank World Development Indicators. B. Indicators for domestic sources of volatility. Data sources: Authors’ own calculations using data from IMF International Financial Statistics, United Nations ESCAP Statistical Database, Federal Reserve Economic Data, OECD and central banks of various countries.
Fig. 3Indicators for external sources of volatility. Data sources: Authors’ own calculations using data from IMF International Financial Statistics, World Bank World Development Indicators and Department of Statistics of various countries.
Variable definitions.
| Variable definition | Data frequency | Source of Data |
|---|---|---|
| Growth rate of real GDP | Quarterly | IFS |
| Growth rate of CPI | Quarterly | IFS |
| Growth rate of PPI | Quarterly | IFS |
| Relative prices (measured as CPI over PPI) | Quarterly | IFS |
| Growth rate of exports FOB | Quarterly | IFS |
| Growth rate of imports CIF | Quarterly | IFS |
| Monetary growth (growth rate of money and quasi money, M2) | Annually | WDI |
| Growth rate of nominal deposit rates | Quarterly | IFS |
| Growth rate of nominal lending rates | Quarterly | IFS |
| Real interest rate (measured as nominal lending rate adjusted for inflation as measured by the GDP deflator) | Quarterly | IFS |
| Fiscal result as a proportion of GDP | Annually | ESCAP, OECD, central banks |
| Growth rate of real broad effective exchange rates | Monthly | FRED |
| Openness coefficient (measured as the sum of exports and imports over GDP) | Quarterly | IFS, FRED |
| Net FDI inflows as a proportion of GDP | Annually | WDI |
| Net portfolio equity inflows as a proportion of GDP | Annually | WDI, IFS, DOS |
FOB denotes Free On Board.
CIF denotes Cost, Insurance and Freight.
| Subject area | Economics |
|---|---|
| More specific subject area | Macroeconomics |
| Type of data | Figures, table and Excel files |
| How data was acquired | Data are acquired from International Financial Statistics (IFS) published by the International Monetary Fund (IMF), World Development Indicators (WDI) by the World Bank, Federal Reserve Economic Data (FRED) by the Federal Reserve Bank of St. Louis, Economic and Social Commission for Asia and the Pacific (ESCAP) Statistical Database by the United Nations, the Organization for Economic Co-operation and Development (OECD), central banks and Department of Statistics (DOS) of each sample country |
| Data format | Aggregated, processed |
| Experimental factors | The sample was extracted by merging information from IFS, WDI, FRED, ESCAP, OECD, central banks and DOS. Sample construction involved converting the raw data collected from the various sources into either growth rates or ratios. |
| Experimental features | The macroeconomic data series represent three broad categories of indicators that can be used to proxy macroeconomic volatility. They are indicators that account for macroeconomic volatility (i.e. volatility as a macroeconomic outcome), domestic sources of macroeconomic volatility and external sources of macroeconomic volatility. |
| Data source location | Malaysia, Thailand, Indonesia, Philippines, Singapore, Japan and Australia |
| Data accessibility | Data are available within this article |