Essays in econometrics with applications in macroeconomic and financial modeling.

摘要

This dissertation is composed of three chapters. The first chapter, which is based on a paper jointly written with Professor Peter Phillips, develops an asymptotic theory for time series discrete choice models with explanatory variables generated as integrated processes and with multiple choices and threshold parameters determining the choices. The theory extends recent work by Park and Phillips (2000) on binary choice models. As in this earlier work, the maximum likelihood (ML) estimator is consistent and has a limit theory with multiple rates of convergence (n¾ and n¼) and mixture normal distributions where the mixing variates depend on Brownian local time as well as Brownian motion. An extended arc sine limit law is given for the sample proportions of the various choices.; In the second chapter, which is also based on a paper jointly written with Professor Peter Phillips, we apply the nonstationary discrete choice approach developed in Chapter One to model the empirical behavior of the Federal Reserve in changing the federal funds target rate. The model successfully predicts the majority of the target rate changes during the time period considered (1985–2001) and helps to explain strings of similar intervention decisions by the Fed. Based on the model-implied optimal interest rate, our findings suggest that there is a lag in the Fed's reaction to economic shocks and that the Fed is more conservative in raising interest rates than in lowering rates.; The third chapter considers a new methodology for studying dependence in multivariate time series analysis. Using the concept of a copula, this chapter shows how to estimate association across aggregate financial markets, with a focus on the structure of dependence rather than the degree of dependence. A mixed copula model is constructed so that it can capture various patterns of dependence structures. An inferential apparatus for this approach is developed and the methodology is applied to estimate the dependence in several major financial markets. The empirical findings are shown to have some implications that seem important for a wide range of studies including risk management, portfolio choice, asset pricing, and tests for contagion in financial markets.