By Weiran Zeng
Prediction in economics is the focal point of debate for the future of economics, ever since economists were burdened with the failure to “predict” the 2008 Financial Crisis. This paper discusses positions held by philosophers and economic methodologists regarding what kinds of predictions there are and creates a taxonomy of prediction. Through evaluation of those positions, this paper presents different senses of prediction that can be expected of economics, and assess economists’ reflections according to those senses.
Advisor: Kevin Hoover | JEL Codes: B41, N1, G17
By Ying-te Huang
Essentially all US recessions have been preceded by oil price shocks and subsequently tighter monetary policies. (Bernanke, Gertler and Watson, 1997). Whereas some scholars, including Herrera and Hamilton (2001) claimed that such oil price shocks contributed to the recession that followed, others, including, Bernanke et al. (1997), believed that the Fed‘s endogenous reaction to the monetary policy, rather than oil price per se, led to the contraction of the economy. Which had a greater influence on gross domestic product (GDP) — oil price shocks or a change in monetary policy—has been debated for years. One of the most prominent debates is between Bernanke et al. (1997), and Herrera and Hamilton (2001). In the debate, Bernanke et al. and Herrera and Hamilton used the same model but with different lag lengths and came to different conclusions. In the current study, we contribute to the resolution of this issue by using a new methodology to examine the effects of monetary policy to the economy in response to oil price shocks. Specifically, we determine the contemporaneous causal order empirically in structural vector-autoregression (SVAR). We then examine the economic responses in counterfactual schemes where the Fed does not respond to the oil price shocks. Contrary to Bernanke et al.‘s finding, in which the economy would have done better had the Fed not held its interest rate constant during an oil price shock, we found that the Fed‘s response generates higher output but a less steady price level. This suggests that the results are dependent upon prior assumptions of the model specifications.
Advisor: Kevin Hoover
By Alex Wang
This thesis aims to show that explicit understanding of possible causal structures often aids in inferring the true causes from data. This is done by first understanding that causes are chains of counterfactual dependence. Insofar as experiments, active or natural are not perfect, data can easily support false counterfactuals. Even those tools especially designed to identify unbiased estimates, like instrumental variables, often fail. Causal structure explains the failure of these tools, but more importantly allows us to better identify which counter factuals to reject or accept.
Advisor: Kevin Hoover