Book Description

In the first chapter, I quantify the welfare effect of eliminating the U.S. capital income tax under international financial integration. I employ a two-country, heterogeneous-agent incomplete markets model calibrated to represent the U.S. and the rest of the world. Short-run and long-run factor price dynamics are key: after the tax reform, post-tax interest rate increases less under financial openness relative to autarky. Therefore the wealth-rich households gain less. Post-tax wages also fall less, so the wealth-poor are hurt less. Hence, the fraction in favor of the reform increases, although the majority still prefers the status quo. Aggregate welfare effect to the U.S. is a permanent 0.2 % consumption equivalent loss under financial openness which is 85.5 % smaller than the welfare loss under autarky. The second chapter aims to answer two questions: What helps forecast U.S. inflation? What causes the observed changes in the predictive ability of variables commonly used in forecasting US inflation? In macroeconomic analysis and inflation forecasting, the traditional Phillips curve has been widely used to exploit the empirical relationship between inflation and domestic economic activity. Atkeson and Ohanian (2001), among others, cast doubt on the performance of Phillips curve-based forecasts of U.S. inflation relative to naive forecasts. This indicates a difficulty for policy-making and private sectorâs long term nominal commitments which depend on inflation expectations. The literature suggests globalization may be one reason for this phenomenon. To test this, we evaluate the forecasting ability of global slack measures under an open economy Phillips curve. The results are very sensitive to measures of inflation, forecast horizons and estimation samples. We find however, terms of trade gap, measured as HP-filtered terms of trade, is a good and robust variable to forecast U.S. inflation. Moreover, our forecasts based on the simulated data from a workhorse new open economy macro (NOEM) model indicate that better monetary policy and good luck (i.e. a remarkably benign sample of economic shocks) can account for the empirical observations on forecasting accuracy, while globalization plays a secondary role.