Susannah Scanlan
Quantitative Researcher
I completed my PhD in Economics at Columbia University in 2024. My research fields were macroeconomics and econometrics. I studied the empirical implications of people's information choices, as well as factor-based imputation methods for macroeconomic data. The views expressed on this website are my own and do not reflect the views of any institution.
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 was a competitive fencer for about 10 years and won a bronze medal at the 2012 Olympic Games in team women's epee.
Research
Measuring Forecaster Attention
This paper was previously posted under the title "Attention Allocation and the Factor Structure of Forecasts"
I propose a novel way to measure forecaster attention. Theoretically, I show that the information structure, or attention allocation, of a rationally inattentive forecaster can be identified from the factor structure of their observed forecast data. Using this theoretical result, I propose an estimator to measure relative forecaster attention. I find that the attention measures of professional macroeconomic forecasters of different rank are consistent with the predicted optimal attention from a multivariate rational inattention model. Additionally, I show that professional forecasters have similar reduced form forecast models. Finally, I estimate the information cost models that best rationalize the estimated attention of professional forecasters. Only top-performing forecasters have estimated cost functions close to mutual information. As performance declines, estimated cost functions move toward Fisher information.
Constructing high frequency economic indicators by imputation, with Serena Ng
The Econometrics Journal, Volume 27, Issue 1, January 2024, Pages C1–C30, https://doi.org/10.1093/ectj/utad024
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 modelling 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 Chicago Fed National Activity 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 2014–2015 collapse in oil prices.