UCLA

The U.S. Federal Reserve Board goes to great lengths to make quarterly forecasts of GDPas accurate as possible, as these forecasts play a key role in informing monetary policy that affects millions of Americans. It is therefore not surprising that there is ongoing effort to refine and redefine existing GDP forecasting models so they are more accurate, especially in the event of random shocks to the economy. During the COVID-19 shock, few economic models were able to capture the full volatility of the GDP swings resulting from a once-in- a-lifetime global pandemic. Since then, a whole slew of models, from MIDAS regression to MF-VAR, have been used to forecast the economic impacts of COVID-19 more realistically. In this thesis, I propose a new approach – applying Principal Component Analysis (PCA) to a mix of monthly and quarterly economic variables – to forecast GDP per quarter from 1960 to 2023. To account for the mixed frequency nature of the data, I build PCA forecasts across three data organizations: 1) using only quarterly data, 2) using stacked data, and 3) using imputed data and select the best fit model. I then assess performance of my model against the most widely used times series methods. I find that my economic data is highly sensitive to overfitting and that the PCA model using only quarterly data performs the best at predicting GDP, including capturing the COVID-19 shocks.