Constructing a Structural Macroeconomic Model Using Multiple Techniques

This project constructs a structural model of the United States Economy. This task is tackled in two separate ways: first econometric methods and then using a neural network, both with a structure that mimics the structure of the U.S. economy. The structural model tracks the performance of U.S. GDP rather well in a dynamic simulation, with an average error of just over 1 percent. The neural network performed well, but suffered from some theoretical, as well as some implementation issues.

Decomposing Consumer Wealth Effects: Evidence on the Role of Real Estate Assets Following the Wealth Cycle of 1990-2002

During the period of 1990-2002 US households experienced a dramatic wealth cycle, induced by a 369% appreciation in the value of real per capita liquid stock market assets followed by a 55% decline. However, consumer spending in real terms continued to rise throughout this period. Using data from 1990-2005, traditional life-cycle approaches to estimating macroeconomic wealth effects confront two puzzles: (i) econometric evidence of a stable cointegrating relationship among consumption, income, and wealth is weak at best; and (ii) life-cycle models that rely on aggregate measures of wealth cannot explain why consumption did not collapse when the value of stock market assets declined so dramatically. We address both puzzles by decomposing wealth according to the liquidity of household assets. We find that the significant appreciation in the value of real estate assets that occurred after the peak of the wealth cycle helped sustain consumer spending from 2001 to 2005.

The Determinants of Recovery Rates on Defaulted Corporate Securities: Why do Fallen Angels recover more than Original High Yield Issues?

Regression analysis has shown that recovery rates are determined by a variety of conditions at the time of default. These conditions can be broken into five major categories: (1) a security's seniority within the capital structure of the defaulting firm, (2) the type of default event, (3) firm-specific factors, (4) industry-specific factors, and (5) macroeconomic factors. Expectations of these inputs determine the expected recovery rate if default were to occur, thereby determining credit ratings and security prices. Although it is widely understood how recovery rate estimates influence credit rating assignments (the higher the expected recovery rate, the higher the assigned credit rating), no research, to the best of my knowledge, has investigated the reasons why higher rated securities recover more than lower rated securities in the event of default. Specifically, this paper will empirically investigate why securities originally rated investment grade, fallen angels, recover more than securities originally rated high yield in the event of default.