Is Economic Recovery a Myth? Robust Estimation of Impulse Responses

By C. Teulings, N. Zubanov. Social Science Research Network, 2014

319 citations recorded by the source corpus.

There is a lively debate on the persistence of the current banking crisis' impact on GDP. Impulse Response Functions (IRF) estimated by Cerra and Saxena (2008) suggest that the effects of earlier crises were long-lasting. We show that standard estimates of IRFs are highly sensitive to misspecification of the underlying data generation process. Direct estimation of IRFs by a methodology similar to Jorda's (2005) local projection method is robust to misspecifications of the data generation process but yields biased estimates when country fixed effects are added. We propose a simple method to deal with this bias, which we apply to panel data from 99 countries for the period 1974-2001. Our estimates suggest that an average banking crisis leads to an output loss of around 10 percent with little sign of recovery. GDP losses from banking crises are more severe for African countries and economies in transition.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/1f24e405c6cce47638124aa99fcbd24d4e793dbb

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Citations in the corpus

How Costly are Debt Crises? (2011, unknown): “As shown by Teulings and Zubanov (2010), a possible bias from estimating Equation 3 using country fixed effects is that the error term of the equation may have a non-zero expected value, due to the interaction of fixed effects and country-specific arrival rates of crises.”
The distributional effects of capital account liberalization (2018, methodology): “Teulings and Zubanov (2014) and Bernal-Verdugo et al. (2013) note that the IRFs using ARDL models can be sensitive to the choice of the number of lags.”
Gone with the Headwinds: Global Productivity (2017, methodology): “The effects are estimated using local projections method (Jordà 2005), controlling for lagged growth, past reforms, crisis dummies and using a bias correction suggested by Teulings and Zubanov (2014). See further details in Appendix VIII.”
The Economic Effects of COVID-19 Containment Measures (2020, methodology): “Third, we follow Teulings and Zubanov (2014) and include leads of 7 As for NO2, the percent effects are computed as (e q h-1)*100.”
Oil Prices and Inflation Dynamics: Evidence from Advanced and Developing Economies (2017, background): “The specification also includes the forward leads of the of global oil inflation between time 0 (the date of the oil price shock) and the end of the forecast horizon (k) to correct the bias in the impulse response inherent in local projection methods (Teulings and Zubanov, 2014).”