Asymmetric Responses of Economic Growth to Daily Oil Price Changes: New Global Evidence from Mixed-data Sampling Approach
-
Aktham Maghyereh
,
Osama Sweidan
Abstract
Our paper inspects empirically the asymmetric impact of daily oil price shocks on the quarterly real domestic product in eight countries during the period (1983–2016). We employ two methodologies Ordinary Least Squares (OLS) and Asymmetric Mixed Data Sampling (AMIDAS). The OLS technique shows that the positive oil price shocks have a statistically significant negative effect on economic growth in all the countries and vice versa. In addition, it reveals that this relationship could be either symmetric or asymmetric in all the countries. On the contrary, the AMIDAS gives more important details and proves that all the relationships in our sample data are asymmetric. Thus, we think that the AMIDAS technique leads to more accurate results which enhances a better insightful of an energy policy. The policy implication of our paper demonstrates that the energy policies are significant procedures to improve economic performance.
References
Andreou, E., Ghysels, E., and Kourtellos, A. (2010). Regression models with mixed sampling frequencies. J. Econom. 158: 246–261, https://doi.org/10.1016/j.jeconom.2010.01.004.Suche in Google Scholar
Andreou, E., Ghysels, E., Kourtellos, A., and Gupta, R. (2013). Should macroeconomic forecasters use daily financial data and how?. Journal of Business & Economic Statistics 31, https://doi.org/10.1080/07350015.2013.767199.Suche in Google Scholar
Apergis, N., Aslan, A., Aye, G.C., and Gupta, R. (2015). The asymmetric effect of oil price on growth across US States. Energy Explor. Exploit. 33: 575–590, https://doi.org/10.1260/0144-5987.33.4.575.Suche in Google Scholar
Barsky, R.B. and Kilian, L. (2004). Oil and the macroeconomy since the 1970s. J. Econ. Perspect. 18: 115–134, https://doi.org/10.1257/0895330042632708.Suche in Google Scholar
Baumeister, C., Guérin, P. and Kilian, L. (2015). Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. Int. J. Forecast. 31: 238–252, https://doi.org/10.1016/j.ijforecast.2014.06.005.Suche in Google Scholar
Bernanke, B. (1983). Irreversibility, uncertainty, and cyclical investment. Q. J. Econ. 98: 85–106, https://doi.org/10.2307/1885568.Suche in Google Scholar
Clements, M.P. and Galvão, A.B. (2008). Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States. J. Bus. Econ. Stat. 26: 546–554, https://doi.org/10.1198/073500108000000015.Suche in Google Scholar
Clements, M.P. and Galvão, A.B. (2009). Forecasting US output growth using leading indicators: an appraisal using MIDAS models. J. Appl. Econom. 24: 1187–1206, https://doi.org/10.1002/jae.1075.Suche in Google Scholar
Duarte, C., Rodrigues, P.M., and Rua, A. (2017). A mixed frequency approach to forecast private consumption with ATM/POS data. Int. J. Forecast. 33: 61–75, https://doi.org/10.1016/j.ijforecast.2016.08.003.Suche in Google Scholar
Edelstein, P. and Kilian, L. (2007). The response of business fixed investment to changes in energy prices: a test of some hypotheses about the transmission of energy price shocks. B E J. Macroecon. 7: 1–41, https://doi.org/10.2202/1935-1690.1607.Suche in Google Scholar
Edelstein, P. and Kilian, L. (2009). How sensitive are consumer expenditures to retail energy prices?. J. Monetary Econ. 56: 766–779, https://doi.org/10.1016/j.jmoneco.2009.06.001.Suche in Google Scholar
Elder, J. and Serletis, A. (2009). Oil price uncertainty in Canada. Energy Econ. 31: 852–856, https://doi.org/10.1016/j.eneco.2009.05.014.Suche in Google Scholar
Elder, J. and Serletis, A. (2010). Oil price uncertainty. J. Money Credit Bank. 42: 1137–1159, https://doi.org/10.1111/j.1538-4616.2010.00323.x.Suche in Google Scholar
Elder, J. and Serletis, A. (2011). Volatility in oil prices and manufacturing activity: an investigation of real options. Macroecon. Dyn. 15: 379–395, https://doi.org/10.1017/s1365100511000630.Suche in Google Scholar
Foroni, C., Ghysels, E., and Marcellino, M. (2013). Mixed-frequency vector autoregressive models. VAR models in macroeconomics—new developments and applications: essays in honor of Christopher A. Sims: Emerald Group Publishing Limited, pp. 247–272.10.1108/S0731-9053(2013)0000031007Suche in Google Scholar
Ghysels, E. and Qian, H. (2019). Estimating MIDAS regressions via OLS with polynomial parameter profiling, Econom. Stat. 9: 1–16.10.1016/j.ecosta.2018.02.001Suche in Google Scholar
Ghysels, E., Santa-Clara, P., and Valkanov, R. (2006a). Predicting volatility: getting the most out of return data sampled at different frequencies. J. Econom. 131: 59–95, https://doi.org/10.1016/j.jeconom.2005.01.004.Suche in Google Scholar
Ghysels, E., Sinko, A., and Valkanov, R. (2006b). MIDAS regressions: further results and new directions. Econom. Rev. 26: 53–90.10.1080/07474930600972467Suche in Google Scholar
Ghysels, E., Kvedaras, V., and Zemlys, V. (2016). Mixed frequency data sampling regression models: the R package midasr. J. Stat. Software 72: 1–35, https://doi.org/10.18637/jss.v072.i04.Suche in Google Scholar
Ghysels, E., Plazzi, A., and Valkanov, R. (2019). Why invest in emerging markets? the role of conditional return asymmetry. J. Financ. 71: 2145–2192.10.1111/jofi.12420Suche in Google Scholar
Hamilton, J.D. (1983). Oil and the macroeconomy since world war II. J. Polit. Econ. 91: 228–248, https://doi.org/10.1086/261140.Suche in Google Scholar
Hamilton, J.D. (1988). A neoclassical model of unemployment and the business cycle. J. Polit. Econ. 96: 593–617, https://doi.org/10.1086/261553.Suche in Google Scholar
Hamilton, J.D. (2009). Causes and consequences of the oil shock of 2007–08. Brookings Pap. Econ. Activ. 40: 215–261.10.3386/w15002Suche in Google Scholar
Hamilton, J.D. (2011). Nonlinearities and the macroeconomic effects of oil prices. Macroecon. Dyn. 15: 472–497, https://doi.org/10.1017/s1365100511000307.Suche in Google Scholar
Kilian, L. and Vigfusson, R. (2011a). Nonlinearities in the oil price-output relationship. Macroecon. Dyn. 15: 337–363, https://doi.org/10.1017/s1365100511000186.Suche in Google Scholar
Kilian, L. and Vigfusson, R.J. (2011b). Are the responses of the US economy asymmetric in energy price increases and decreases?. Quant. Econ. 2: 419–453, https://doi.org/10.3982/qe99.Suche in Google Scholar
Kuzin, V., Marcellino, M., and Schumacher, C. (2011). MIDAS vs. mixed-frequency VAR: nowcasting GDP in the euro area. Int. J. Forecast. 27: 529–542, https://doi.org/10.1016/j.ijforecast.2010.02.006.Suche in Google Scholar
Maghyereh, A., Awartani, B., and Sweidan, O. (2019). Oil price uncertainty and real output growth: new evidence from selected oil-importing countries in the Middle East. Empir. Econ. 56: 1601–1621.10.1007/s00181-017-1402-7Suche in Google Scholar
Monteforte, L. and Moretti, G. (2013). Real-time forecasts of inflation: the role of financial variables. J. Forecast. 32: 51–61, https://doi.org/10.1002/for.1250.Suche in Google Scholar
Mork, K. (1989). Oil and macroeconomy when prices go up and down: An extension of Hamiltons results. Journal of Political Economy 97: 740–744, https://doi.org/10.1016/j.eneco.2013.11.003.Suche in Google Scholar
Narayan, P.K., Sharma, S., Poon, W.C., and Westerlund, J. (2014). Do oil prices predict economic growth? new global evidence. Energy Econ. 41: 137–146, https://doi.org/10.1016/j.eneco.2013.11.003.Suche in Google Scholar
Pindyck, R. S. (1991). Irreversibility, uncertainty, and investment. J. Econ. Lit. 29: 1110–1148.10.3386/w3307Suche in Google Scholar
Rahman, S. and Serletis, A. (2011). The asymmetric effects of oil price shocks. Macroecon. Dyn. 15: 437–471, https://doi.org/10.1017/s1365100511000204.Suche in Google Scholar
Sadorsky, P. (1999). Oil price shocks and stock market activity. Energy Econ. 21: 449–469, https://doi.org/10.1016/s0140-9883(99)00020-1.Suche in Google Scholar
Schumacher, C. (2016). A comparison of MIDAS and bridge equations. International Journal of Forecasting 32: 257–270, https://doi.org/10.1016/j.ijforecast.2015.07.004.Suche in Google Scholar
Sinko, A. Sockin, M., and Ghysels, M. (2010). Matlab Toolbox for Mixed Sampling Frequency Data Analysis using MIDAS Regression Models. Working Paper. University of North Carolina.Suche in Google Scholar
Valadkhani, A. and Smyth, R. (2017). How do daily changes in oil prices affect US monthly industrial output?. Energy Econ. 67: 83–90, https://doi.org/10.1016/j.eneco.2017.08.009.Suche in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston
