shifting gears
The sign restrictions are imposed for the first two periods. Oil prices are modeled as an exogenous process in all economies. The exception is India, where given that it is the third-largest importer of oil, its demand could affect global oil prices on the margin, so a model with endogenous oil prices is used. All variables, except interest rates, are transformed to growth rates using first difference log transformations and annualized. The model is estimated using Bayesian priors with 10000 draws and 2000 burn-in draws. The Minnesota prior is used with hyperparameters on the first own lag of each variable set at 0.8, overall tightness is 0.1, cross-variable weighting is 0.9, and lag decay is 1.2. The model is estimated with four lags and on quarterly data from 2000Q1 (or based on availability) to 2021Q3 with forecasts for unavailable data points. Output data is real GDP in local currency units, and consumer inflation data is headline consumer price inflation partly due to the limited availability of core inflation. Both real GDP and inflation are seasonally adjusted by source or Haver Analytics. The real exchange rate is trade-weighted and deflated using the consumer price index. The oil price is the average of Brent crude, West Texas Intermediate, and Dubai Fateh. In Pakistan, the model excludes a monetary policy variable due to limited data, while the output variable is manufacturing production. In Bangladesh, industrial production is used. In Sri Lanka, base money is the monetary policy variable. In Maldives, the model excludes a monetary policy variable given that the economy imposes an exchange rate anchor vis-à-vis the U.S. dollar. A South Asia average is calculated as the country average weighted using 2020 constant GDP in U.S. dollars. The unprecedented nature and size of the COVID-19 shock present possible challenges to the effective modeling of the pandemic, especially for the historical decomposition of the VAR model. However, to deal with the significant change in volatility, the VAR model includes stochastic volatility in the error structure, a generic version of what is suggested in Lenza and Primiceri (2020) as a solution to modeling COVID-19.
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