• https://publications.iadb.org/en/publication/10939/chinas-emergence-world-economy-and-business-cycles-latin-america

Cesa-Bianchi, Ambrogio, et al. (2012). GVAR Data associated with: China's Emergence in the World Economy and Business Cycles in Latin America. IDB Open Data. https://doi.org/10.60966/lwl7yy3a

1. English

What is this dataset, and who produced it?

This is the GVAR data used in the study “China’s Emergence in the World Economy and Business Cycles in Latin America”. It was produced by the IDB’s Department of Research and Chief Economist (see department page).

What is the temporal and country coverage?

The data are quarterly, spanning from 1979Q2 through 2009Q4 (i.e., 1979–2009).
They cover 25 advanced and emerging economies, plus the euro area, representing more than 90% of world GDP.

Which countries are included in the panel?

Some included economies are: Argentina, Brazil, Chile, Mexico, Peru, China, the United States, Japan, Germany, France, Italy, Canada, Australia, Belgium, Finland, Norway, Sweden, Switzerland, and the euro area.

Which macro-financial variables does the dataset include?

The dataset comprises:
- Real GDP
- CPI inflation
- Real equity prices
- Real exchange rates
- Short-term interest rates
- Long-term interest rates
- Price of oil

What is the intended use of this dataset?

It underpins estimation of a GVAR (Global Vector Autoregression) model—specifically as in the referenced study—to examine how shocks propagate from China (and other major economies) to Latin America.

What are the key findings from the study on shock transmission?

• The long-run impact of a China GDP shock on Latin America has tripled since the mid-1990s.
• Meanwhile, the effect of a U.S. GDP shock has halved over that same timeframe.
• Indirect linkages (via China’s influence on Latin America’s trading partners) are shown to be nearly as significant as direct trade links in driving shock transmission.

How can I access or download the data and related code?

The dataset is available for download in CSV, JSON, and XLS formats via the IDB Open Data portal. The baseline GVAR code and any updates are linked under the “Related URL” section of the dataset page.

What limitations or caveats should users be aware of?

• The time series ends in 2009, so post-2009 dynamics aren’t captured.
• The GVAR model uses general GDP shocks, not shocks broken down by structural demand/supply.
• Results depend on the time-varying trade weight matrices used in the model’s counterfactuals.

Why is the gvar data china latin america dataset important?

It offers a deep, historical macro-financial panel for key global and Latin American economies, enabling:
- Analysis of shock transmission across countries
- Insights into how China’s rise reshapes business cycle co-movements in Latin America
- Counterfactual experiments (e.g., fixed vs evolving trade linkages)

What is the definition of the Global Vector Autoregressive (GVAR) model in econometrics?

The GVAR model is a multi-country econometric framework that links domestic variables (such as GDP, inflation, and interest rates) to foreign variables constructed as trade-weighted averages of other countries' variables. It allows analysis of international spillovers and interconnected business cycles.

What data requirements are needed for GVAR macroeconomic modeling?

The dataset includes quarterly data from 1979 to 2009 for 25 major advanced and emerging economies plus the euro area. Variables include real GDP, the CPI inflation rate, real equity prices, real exchange rates, short- and long-term interest rates, and oil prices. Together, these account for over 90% of global GDP.

How does GVAR differ from standard VAR models for multi-country analysis?

Unlike a standard VAR, which models variables for a single country, a GVAR incorporates cross-country linkages by including foreign variables. This enables analysis of global shocks and spillover effects across economies.

How are foreign variables constructed in GVAR datasets?

Foreign variables are typically trade-weighted averages of other countries’ macroeconomic indicators. For example, a Latin American country’s foreign GDP variable would be a weighted average of GDP from its main trading partners.

What role do weight vectors play in GVAR model construction?

Weight vectors determine the relative importance of each partner country in constructing foreign variables. They are usually based on bilateral trade shares, ensuring that the model reflects actual economic interdependencies.

How can GVAR be used for spillover effects analysis?

By modeling interconnected economies, GVAR allows researchers to trace how shocks in one country (e.g., China’s growth or oil price changes) propagate to others, including Latin American economies, through trade and financial linkages.

What software packages are available for estimating GVAR models?

Researchers often use MATLAB, R, or specialized econometric toolkits. The IDB dataset provides baseline GVAR code alongside the data, enabling replication and extension of published studies.

What does “GVAR data” mean in this context?

It refers to harmonized quarterly macroeconomic indicators used to estimate the Global Vector Autoregressive model. The dataset is specifically tied to the study “China’s Emergence in the World Economy and Business Cycles in Latin America.”