I am a 6th year Economics PhD student at Columbia University. My fields are macroeconomics and econometrics. I study the empirical implications of people’s information choices, as well as factor-based imputation methods for macroeconomic data. I am on the 2023/24 job market.
I received a BA in Economics from Princeton University in 2014 and subsequently worked at the Council of Economic Advisers and the Federal Reserve Bank of New York.
I explore how forecaster attention, or the degree to which new information is incorporated into forecasts, is reflected at the lower-dimensional factor representation of multivariate forecast data. When information is costly to acquire, forecasters may pay more attention to some sources of information and ignore others. How much attention they pay will determine the strength of the forecast correlation (factor) structure. Using a factor model representation, I show that a forecast made by a rationally inattentive agent will include an extra shrinkage and thresholding "attention matrix" relative to a full information benchmark, and propose an econometric procedure to estimate it. I show that the mapping from theoretical attention allocation to factor model representation is valid for a broad class of information cost functions. Differences in the degree of forecaster attentiveness can explain observed differences in empirical shrinkage in professional macroeconomic forecasts relative to a consensus benchmark. Better-performing forecasters have higher measured attention (lower shrinkage), than their poorly-performing peers. Measured multidimensional attention can largely be captured by heterogeneity in a single scalar cost parameter.
Constructing High Frequency Economic Indicators by Imputation (with Serena Ng)
Forthcoming, Econometrics Journal, https://arxiv.org/pdf/2303.01863.pdf
Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a low frequency diffusion index that is already available, and treat high frequency values as missing. We impute these values using multiple factors estimated from the high frequency data. In the empirical examples considered, static matrix completion that does not account for serial correlation in the idiosyncratic errors yields imprecise estimates of the missing values irrespective of how the factors are estimated. Single equation and systems-based dynamic procedures that account for serial correlation yield imputed values that are closer to the observed low frequency ones. This is the case in the counterfactual exercise that imputes the monthly values of consumer sentiment series before 1978 when the data was released only on a quarterly basis. This is also the case for a weekly version of the CFNAI index of economic activity that is imputed using seasonally unadjusted data. The imputed series reveals episodes of increased variability of weekly economic information that are masked by the monthly data, notably around the 2014-15 collapse in oil prices.
Graduate
Fall 2020/21 Teaching Fellow, Macroeconomic Analysis I (Master’s), for Ron Miller
Spring 2021/22 Teaching Fellow, Macroeconomic Analysis II (Master’s), for Irasema Alonso
Undergraduate
Fall 2019/23 Teaching Fellow, The American Economy, for Claudia Halbec
Spring 2022 Teaching Fellow, Behavioral Finance, for Harrison Hong
