Can debt ceiling and government shutdown predict US real stock returns? A bootstrap rolling window approach

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Peer-Reviewed Research
  • SDG 17
  • Abstract:

    This paper investigates the in-sample predictability of debt ceiling and government shutdown for real stock returns in the U.S, using rolling window Granger non-causality estimation. Causal links often evolve over time so the use of the bootstrap rolling window approach will account for potential time variations in the relationships. We use monthly time series data on measures of debt ceiling and government shutdown, and real stock returns, covering the period of 1985:M2 to 2013:M9. Since the debt ceiling and government shutdown variables under analysis are exogenous, the use of the in-sample predictability to analyse the relation-ship running from debt ceiling to real stock returns, as well as, from government shutdown to real stock returns will provide evidence of not only whether in-sample predictability exists, but also how predictability varies over time i.e. significance in episodes of high values of index. The full sample bootstrap non-Granger causality test results suggest existence of no in-sample predictability of debt ceiling or government shutdown for real stock returns in the U.S. economy. The stability tests show evidence of parameter instability in the estimated equations. Therefore, we make use of the bootstrap rolling window (24 months) approach to investigate the changes in the in-sample predictability of the relationship, and detect signifi-cant in-sample predictability of debt ceiling and government shutdown for real stock returns at different sub-periods, corresponding especially after the phases where there were sharp increases in the indexes of debt ceiling and government shutdown.