Asymmetric Responses of Economic Growth to Daily Oil Price Changes: New Global Evidence from Mixed-data Sampling Approach

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.Search 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.Search 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.Search 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.Search 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.Search in Google Scholar

Bernanke, B. (1983). Irreversibility, uncertainty, and cyclical investment. Q. J. Econ. 98: 85–106, https://doi.org/10.2307/1885568.Search 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.Search 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.Search 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.Search 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.Search 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.Search 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.Search 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.Search 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.Search 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)0000031007Search 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.2018.02.001Search 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.Search in Google Scholar

Ghysels, E., Sinko, A., and Valkanov, R. (2006b). MIDAS regressions: further results and new directions. Econom. Rev. 26: 53–90.10.1080/07474930600972467Search 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.Search 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.12420Search 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.Search 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.Search 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/w15002Search 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.Search 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.Search 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.Search 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.Search 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/s00181-017-1402-7Search 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.Search 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.Search 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.Search in Google Scholar

Pindyck, R. S. (1991). Irreversibility, uncertainty, and investment. J. Econ. Lit. 29: 1110–1148.10.3386/w3307Search 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.Search 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.Search 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.Search 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.Search 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.Search in Google Scholar