Welcome to my website. I’m a fifth-year PhD candidate in economics at Duke University. My research interests are in macroeconomics and finance, with an emphasis on anticipating, preventing, and mitigating recessions.
Before beginning my PhD, I was a Research Assistant in the Financial Markets Department (Monetary Policy Analysis and Research Team) at the Bank of Canada. Prior to that, while studying economics as an undergraduate at the University of Toronto, I worked in an administrative role at the Bank of Montreal and as a freelance programmer for various organizations.
PhD in Economics, 2016–
MA in Economics (en route to PhD), 2019
BSc in Financial Economics, 2015
University of Toronto
I build a model that generates large and realistic marginal propensities to consume for constrained and unconstrained households. The main mechanism in the model is bounded intertemporal rationality. Households respond to irregular income shocks by re-optimizing over an optimally determined finite horizon. The optimal length of the horizon depends on the size and persistence of the shock relative to the household’s income and wealth. Estimated using data from the Economic Stimulus Act of 2008, the model explains the large consumption response of financially unconstrained households and generates a distribution of propensities to consume similar to the empirical distribution. The main implication for the design of stimulative policy is to target smaller payments to more households along the income distribution.
Using 14,800 forecasts of one-year S&P 500 returns made by Chief Financial Officers over a 12-year period, we track the individual executives who provide multiple forecasts to evaluate how they adapt and recalibrate in response to return realizations. We present a simple model of Bayesian learning which suggests that the evolution of beliefs should be impacted by return realizations, but that stronger priors yield a sluggish response. While CFOs’ forecasts are unbiased, their confidence intervals are far too narrow, implying a very strong conviction in their beliefs. Consistent with Bayesian learning, we find that when return realizations fall outside of ex-ante confidence intervals, CFOs’ subsequent confidence intervals become significantly wider. However, the magnitude of the updating is apparently dampened by the tightness of prior beliefs and, as a result, miscalibration persists.
Using detailed household-level data from the Survey of Income and Program Participation, the ratio of credit card debt to income is found to be the most important balance sheet item in determining household usage of stimulus funds in 2008, adding to existing evidence that borrowing constraints are functions of debt-to-income ratios. Borrowing constrained households, often predicted to be the group with the largest propensity to consume out of stimulus funds, were the most likely to use stimulus payments to repay debt instead of increase consumption. This behavior is consistent with the fact that household credit supply was tightening at the same time that stimulus payments were being distributed, forcing households, especially those near their borrowing constraints, to deleverage.
A large empirical literature documents that central bank monetary policy changes signal information about future economic fundamentals to the private sector. The canonical Gali (2008) model is modified to analyze this mechanism and understand the information effect of monetary policy. We assume the central bank observes a private signal of future economic fundamentals and uses the filtered information in its Taylor rule. As a result, the nominal interest rate serves an additional function as a noisy signal of future economic fundamentals and there is an information effect of monetary policy. We find that a contractionary monetary policy induces an expansionary information effect, but for reasonable calibrations, the net effect is contractionary.
As part of the CARES Act, the IRS distributed $300 billion in Economic Impact Payments (EIPs) directly to US households. In the Census Bureau’s Household Pulse Survey (HPS), almost 75% of households receiving an EIP reported mostly spending it. Conditioning on labor status, 63% of employed households reported mostly spending their EIPs, compared to 84% of unemployed households. Both types of households reported spending largely on consumption goods, but unemployed households tended to pay regular bills while employed households paid down debt or increased savings. The evidence suggests that in designing an untargeted stimulus program and trading off potential efficacy for timeliness, Economic Impact Payments were overall very effective in supporting consumer spending.
In 2016, one in three US households reported carrying monthly balances on their credit cards. The prevalence of credit card borrowing implies that summarizing a household’s liquidity using net liquid wealth masks important heterogeneity in joint holdings of liquid assets and debt. I show that credit card borrowing is an important predictor of household behavior in response to transitory income shocks. Holding fixed net liquid wealth, the propensity to consume and save decrease with more credit card debt and the propensity to repay debt increases. This finding has implications for the propagation of income shocks in the economy.
To analyze monetary policy implementation in a negative rate environment, we add the option to exchange central bank reserves for cash to the standard workhorse model of monetary policy implementation (Poole 1968). Importantly, we show that monetary policy can be constrained when the target overnight rate is below the yield on cash. At this point, the overnight rate equals the yield on cash instead of the target rate. Modifications to the implementation framework, such as a reserve requirement that varies with cash withdrawals, can help restore the implementation of monetary policy such that the overnight rate equals the target rate.
Formerly Bank of Canada Staff Working Paper 2017-25.