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    Gold Discoveries During the US Classical Gold Standard Era: Monetary News Shocks and Adaptive Learning in a DSGE Model

    Hao, Gracie
    0000-0003-3280-3775
    : http://hdl.handle.net/1803/10114
    : 2020-05-19

    Abstract

    I study the real effect of gold discoveries as a form of monetary news shock during the classical gold standard era (1880-1914). I took advantage of a unique dataset of gold and silver deposits in the US that includes the date of discovery and the estimated total deposit size. From 1879 to 1913, the actual variance of output is 3.64\% and the variance of inflation is 3.06\%. The counterfactual practice using the baseline model predicts output and inflation variances would be 5.97\% and 6.57\% respectively. It demonstrates that if the silver were not demonetized, both of the volatilities in output and inflation would be much higher than the actual ones. I built a simple DSGE model with a gold production sector and found that even the monetary news shock is fully expected, economic agents still responded to this anticipated shock. It significantly affects money supply, interest rate, relative price level, and employment. Because the gold production only calibrated to constitute 1% of the aggregate production, gold discovery doesn't affect the output level very much. This paper also loosened up the "Rational Expectation (RE) Assumption" which is an important foundation of the modern DSGE model. I studied two types of adaptive learnings: Constant Gain Learning (CG) and Kalman Filter Learning (KF). Kalman filter learning is preferred by the data over the RE solution. It seems that the assumption that all agents know the true underlying DSGE model is a bit strict. There are also a lot of anecdotal records showed that it takes time for the news to spread. However, CG is the least favorable among these three. This result argues that if agents already understand the true economic model, i.e. the underlying MSV representations of DSGE model, then they are much more likely to form rational expectations rather than constantly learn the parameters.
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