Adams, R. M., K. J. Bryant, B. A. Mccarl, D. M. Legler, J. O’Brien, A. Solow, and R. Weiher (1995). Value of Improved Long-Range Weather Information. Contemporary Economic Policy 13 (3), 10–19.
Baxter, M. and M. A. Kouparitsas (2005). Determinants of Business Cycle Comovement: A Robust Analysis. Journal of Monetary Economics 52 (1), pp. 113–157.
Bennetton, J., P. Cashin, D. Jones, and J. Soligo (1998). An Economic Evaluation of Bushfire Prevention and Suppression. Australian Journal of Agricultural and Resource Economics 42 (2), 149–175.
Brunner, A. D. (2002). El Nino and World Primary Commodity Prices: Warm Water or Hot Air? Review of Economics and Statistics 84(1), 176–183.
Buckle, R. A., K. Kim, H. Kirkham, N. McLellan, and J. Sharma (2002). A Structural VAR Model of the New Zealand Business Cycle. New Zealand Treasury Working Paper 02/26.
Cashin, P., K. Mohaddes, and M. Raissi (2012). The Global Impact of the Systemic Economies and MENA Business Cycles. IMF Working Paper WP/12/255.
Cashin, P., K. Mohaddes, M. Raissi, and M. Raissi (2014). The Differential Effects of Oil Demand and Supply Shocks on the Global Economy. Energy Economics 44, 113–134.
Changnon, S. A. (1999). Impacts of 1997–98 El Niño Generated Weather in the United States. Bulletin of the American Meteorological Society 80, 1819–1827.
Chudik, A. and M. H. Pesaran (2013). Econometric Analysis of High Dimensional VARs Featuring a Dominant Unit. Econometric Reviews 32(5–6), 592–649.
Debelle, G. and G. Stevens (1995). Monetary Policy Goals for Inflation in Australia. Reserve Bank of Australia Research Discussion Paper 9503.
Dees, S., F. di Mauro, M. H. Pesaran, and L. V. Smith (2007). Exploring the International Linkages of the Euro Area: A Global VAR Analysis. Journal of Applied Econometrics 22, 1–38.
Dell, M., B. F. Jones, and B. A. Olken (2014). What Do We Learn from the Weather? The New Climate-Economy Literature. Journal of Economic Literature 52(3), 740–98.
Handler, P. and E. Handler (1983). Climatic Anomalies in the Tropical Pacific Ocean and Corn Yields in the United States. Science 220(4602), 1155–1156.
Iizumi, T., J.-J. Luo, A. J. Challinor, G. Sakurai, M. Yokozawa, H. Sakuma, M. E. Brown, and T. Yamagata (2014). Impacts of El Niño Southern Oscillation on the Global Yields of Major Crops. Nature Communications 5.
Kamber, G., C. McDonald, and G. Price (2013). Drying Out: Investigating the Economic Effects of Drought in New Zealand. Reserve Bank of New Zealand Analytical Note Series AN2013/02.
Lee, K. and M. H. Pesaran (1993). Persistence Profiles and Business Cycle Fluctuations in a Disaggregated Model of UK Output Growth. Ricerche Economiche 47, 293–322.
Mohaddes, K. and M. H. Pesaran (2015). Oil Supply Shocks and the Global Economy: A Counterfactual Analysis. Cambridge Working Papers in Economics, forthcoming.
Pesaran, M. H., Y. Shin, and R. J. Smith (2000). Structural Analysis of Vector Error Correction Models with Exogenous I(1) Variables. Journal of Econometrics 97, 293–343.
Pesaran, M. H., L. Vanessa Smith, and R. P. Smith (2007). What if the UK or Sweden had Joined the Euro in 1999? An Empirical Evaluation Using a Global VAR. International Journal of Finance & Economics 12(1), 55–87.
Saini, S. and A. Gulati (2014). El Niño and Indian Droughts - A Scoping Exercise. Indian Council for Research on International Economic Relations Working Paper 276.
Solow, A., R. Adams, K. Bryant, D. Legler, J. O’Brien, B. McCarl, W. Nayda, and R. Weiher (1998). The Value of Improved ENSO Prediction to U.S. Agriculture. Climatic Change 39(1), 47–60.
We are grateful to Tiago Cavalcanti, Hamid Davoodi, Govil Manoj, Rakesh Mohan, Sam Ouliaris, Hashem Pesaran, Vicki Plater, Ajay Shah, Ian South, Garima Vasishtha, Rick van der Ploeg, Yuan Yepez and seminar participants at the IMF’s Asia and Pacific Department Discussion Forum, the IMF’s Institute for Capacity Development, Oxford University, and the 13th Research Meeting of the National Institute of Public Finance and Policy and the Department of Economic Affairs of the Ministry of Finance in India for constructive comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily represent those of the International Monetary Fund or IMF policy.
Faculty of Economics and Girton College, University of Cambridge, United Kingdom.
El Niño is a band of above-average ocean surface temperatures that periodically develops off the Pacific coast of South America, and causes major climatological changes around the world.
The Southern Oscillation index (SOI) measures air-pressure differentials in the South Pacific (between Tahiti and Darwin). Deviations of the SOI index from their historical averages indicate the presence of El Niño (warm phase of the Southern Oscillation cycle) or La Niña (cold phase of the Southern Oscillation cycle) events—see Section II. for more details.
The GVAR methodology is a novel approach to global macroeconomic modeling as it combines time series, panel data, and factor analysis techniques to address the curse of dimensionality problem in large models, and is able to account for spillovers and the effects of ubserved and unobserved common factors (e.g. commodity-price shocks and global finacial cycle)—see Section III.A. for additional details.
Changnon (1999) also argues that an El Niño event can benefit the economy of the United States on a net basis—amounting to 0.2% of GDP during the 1997/98 period.
La Niña weather events (cold phases of the Southern Oscillation cycle) produce the opposite climate variations from El Niño occurances. Since the effects of La Niña on fisheries along the coast of South America, where El Niño was named, are benign, they received relatively little attention.
The main justification for using bilateral trade weights, as opposed to financial weights, is that the former have been shown to be the most important determinant of national business cycle comovements (see Baxter and Kouparitsas (2005)).
Weak exogeneity test results for all countries and variables are available upon request.
An exception is Mohaddes and Pesaran (2015) which explicitly models the oil market as a dominant unit in the GVAR framework.
See http://www.imf.org/external/np/res/commod/table2.pdf for the details on these commodities and their weights.
Note that significance (for a particular variable and country) does not have to be seen on impact as the effects of El Niño in most regions are felt during one specific season and hence could happen in a particular quarter rather than all quarters.
In 1980–81 the ratio of Kharif to Rabi crop production was 1.5. In 2013–14 it is estimated at 0.95 (see, India Economic Survey 2014–15).
During the years 2002, 2004 and 2009 (all years of poor monsoons), CPI inflation averaged 4.1%, 3.9%, and 12.3% in India, respectively.