## 4.1 Stability of estimates ## Source details **Canonical URL:** [4.1 Stability of estimates](https://www.imf.org/-/media/files/publications/wp/2017/wp17104.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2017/wp17104.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2017/wp17104.pdf.json) --- ### Approach to assessing stability - The VAR model contains nearly 30 coefficients,10 and overall stability is gauged via the cumulative long-run effects of the structural shocks on each of the three endogenous variables, captured by matrix (A0−A1−A2)−1. - Footnote detail preserved: 10 = 18 lagged coefficients, 6 contemporaneous coefficients, and 3 intercepts. - Stability is evaluated by re‑estimating the cumulative long‑run matrix (A0−A1−A2)−1 on subsamples with varying start dates (from January 1985 down to January 2005) while keeping the end date fixed at December 2015. - The shortest sample (start = January 2005) is intended to cover the period of the shale revolution, which started in the mid-2000s (see e.g. Kilian (2016)). ### Empirical findings on parameter stability - The estimates of (A0−A1−A2)−1 across subsamples appear generally stable. - Some variability is observed when data from the late-1990s drop out of the sample, affecting responses of: - Oil supply, and - World aggregate demand. - Once data from the early 2000s are dropped from the sample, estimates mostly return to their previous levels. - Conclusion: while estimates differ slightly across subsamples, the evidence provides some degree of confidence in the model’s usefulness to understand oil market dynamics over the more recent period. ### Figure and interpretation notes - Figure 6: - Each panel shows the corresponding row of the estimate of the cumulative long-run matrix (A0−A1−A2)−1. - The date on the horizontal axis marks the starting date of the sample. - All samples end in December 2015. - The stability check focuses on cumulative long‑run effects rather than on individual coefficients because the VAR comprises nearly 30 coefficients and separate inspection would be infeasible. *Source: wp17104 - 4.1 Stability of estimates* ### 4.1 Stability of estimates ### 4.1 Stability of estimates ### Approach to assessing stability - The VAR model contains nearly 30 coefficients,10 and overall stability is gauged via the cumulative long-run effects of the structural shocks on each of the three endogenous variables, captured by matrix (A0−A1−A2)−1. - Footnote detail preserved: 10 = 18 lagged coefficients, 6 contemporaneous coefficients, and 3 intercepts. - Stability is evaluated by re‑estimating the cumulative long‑run matrix (A0−A1−A2)−1 on subsamples with varying start dates (from January 1985 down to January 2005) while keeping the end date fixed at December 2015. - The shortest sample (start = January 2005) is intended to cover the period of the shale revolution, which started in the mid-2000s (see e.g. Kilian (2016)). ### Empirical findings on parameter stability - The estimates of (A0−A1−A2)−1 across subsamples appear generally stable. - Some variability is observed when data from the late-1990s drop out of the sample, affecting responses of: - Oil supply, and - World aggregate demand. - Once data from the early 2000s are dropped from the sample, estimates mostly return to their previous levels. - Conclusion: while estimates differ slightly across subsamples, the evidence provides some degree of confidence in the model’s usefulness to understand oil market dynamics over the more recent period. ### Figure and interpretation notes - Figure 6: - Each panel shows the corresponding row of the estimate of the cumulative long-run matrix (A0−A1−A2)−1. - The date on the horizontal axis marks the starting date of the sample. - All samples end in December 2015. - The stability check focuses on cumulative long‑run effects rather than on individual coefficients because the VAR comprises nearly 30 coefficients and separate inspection would be infeasible. *Source: wp17104 - 4.1 Stability of estimates* ### References ### References ### Oil price dynamics and shocks - Aastveit, Knut Are, Hilde C. Bjørnland, and Leif Anders Thorsrud.2015. “What Drives Oil Prices? Emerging Versus Developed Economies.”Journal of Applied Econometrics, 30(7): 1013–1028. - Barsky, Robert B., and Lutz Kilian.2004. “Oil and the Macroeconomy Since the 1970s.”Journal of Economic Perspectives, 18(4): 115–134. - Bernanke, B., M. Gertler, and M. Watson.1997. “Systematic Monetary Policy and the Effect of Oil Price Shocks.”Brookings Papers on Economic Activity, 28(1): 91–157. - Hamilton, J.D.2003. “What Is an Oil Shock?”Journal of Economics, 113(2): 363–398. - Kilian, L.2009. “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market.”American Economic Review, 99(3): 1053–1069. - Kilian, L., and Daniel. P. Murphy.2014. “The Role of Inventories and Speculative Trading in the Global Market for Crude Oil.”Journal of Applied Econometrics, 29: 454–478. - Kilian, Lutz.2014. “Oil Price Shocks: Causes and Consequences.”Annual Review of Resource Economics, 6: 133–154. - Kilian, Lutz.2016. “The Impact of the Shale Oil Revolution in U.S. Oil and Gasoline Prices.”mimeo. - Mohaddes, Kamiar, and M. Hashem Pesaran.2016a. “Country-specific oil supply shocks and the global economy: A counterfactual analysis.”Energy Economics, 59: 382–399. - Mohaddes, Kamiar, and M. Hashem Pesaran.2016b. “Oil Prices and the Global Economy: Is it Different this Time Around?”USC-INET Research Paper, 16: 1–28. - Beidas-Strom, Samia, and Andrea Pescatori.2014. “Oil Price Volatility and the Role of Speculation.”IMF Working Paper, 14/218: 1–33. - Cashin, Paul, Kamiar Mohaddes, Maziar Raissi, and Mehdi Raissi.2012. “The Differential Effects of Oil Demand and Supply Shocks on the Global Economy.”Cambridge Working Papers in Economics, CWPE(1249). ### Commodity prices, cycles, and macroeconomic implications - Arezki, R., and O. Blanchard.2014. “Seven Questions About the Recent Oil Price Slump.”iMFdirect blog. - Baffes, John, M.A. Kose, Franziska Ohnsorge, and Marc Stocker.2015. “The Great Plunge in Oil Prices: Causes, Consequences, and Policy Responses.”World Bank Policy Research Note. - Erten, Bilge, and Jose Antonio Ocampo.2013. “Super Cycles of Commodity Prices Since the Mid-Nineteenth Centuryl.”World Development, 44: 14–30. - Gruss, Bertrand.2014. “After the BoomCommodity Prices and Economic Growth in Latin America and the Caribbean.”IMF Working Paper, 14/154. - Jacks, David S.2013. “From Boost to Bust: A Typology of Real Commodity Prices in the Long Run.”NBER Working Paper, 18874: 1–86. ### IMF publications, staff notes, and policy discussion - Husain, Aasim M., Rabah Arezki, Peter Breuer, Vikram Haksar, Thomas Helbling, Paulo Medas, and Martin Sommer.2015. “Global Implications of Lower Oil Prices.”IMF Staff Discussion Note, 15: 1–41. - IMF.2015a. “External Sector Report.”International Monetary Fund. - IMF.2015b. “World Economic Outlook.”International Monetary Fund, April. - IMF.2016. “World Economic Outlook.”International Monetary Fund, April. - Obstfeld, Maurice, Gian Maria Milesi-Ferretti, and Rabah Arezki.2016. “Oil Prices and the Global Economy: It’s Complicated.”iMFdirect Blog, March. - Papageorgiou, Chris, and Nikola Spatafora.2012. “Economic Diversification in LICs: Stylized Facts and Macroeconomic Implications.”IMF Staff Discussion Note, 12/13. - Aslam, Aqib, Samya Beidas-Strom, Rudolfs Bems, Oya Celasun, Sinem Kikic Celik, and Zsoka Koczan.2016. “Trading on Their Terms? Commodity Exporters in the Aftermath of the Commodity Boom.”IMF Working Paper, 16/27. - Blanchard, O., and J. Gali.2007. “The Macroeconomic Effects of Oil Price Shocks: Why are the 2000s so different from the 1970s?”International Dimensions of Monetary Policy, 373–421. *Source: wp17104 - References* --- _Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17104.pdf_