UC San Diego

This dissertation aims to apply textual data into economic questions. In Chapter 1, I use politicians' tweets to examine the time-varying effects of political cheap talk on the foreign exchange market and on the stock market. I find that the markets buy the cheap talk stories only at the beginning. In Chapter 2, I design a measure of policy intention for China, and show two examples of the measure. It is found that the policy intention measure could forecast future economic variables. Chapter 3 includes a new measure of monetary shocks from Twitter data which is built with machine learning techniques Convolutional Neural Network. This measure uses the general public anticipation about the interest rate decisions of the Federal Reserve.