dc.description.abstract | This dissertation aims at investigating the theory and application of semiparametric
econometrics. I first inspect the selection of optimal bandwidth using the
cross-validation method for the kernel estimation of cumulative distribution/survivor
functions. Then, I analyze the determination of the number of factors with the methods
of principal component and information criteria. I also show the application of
semiparametric methods to "purchasing power parity" puzzle.
Firstly, I propose a data-driven least squares cross-validation method to optimally
select smoothing parameters for the nonparametric estimation of cumulative distribution/
survivor functions. The general multivariate covariates can be continuous,
discrete/ordered categorical or a mix of either. I establish the asymptotic optimality
of least squares cross-validation method. Also, I show that the estimators of cumulative
distribution/survivor functions using the smoothing parameters selected by the
proposed method is asymptotically normally distributed. Monte Carlo simulation
verifies the finite-sample properties of the least squares cross-validation method.
Secondly, I provide some discussions on the econometric theory for factor models
of large dimensions where the number of factors (r) is allowed to increase as the two
dimensions, cross-sections (N) and time dimensions (T) increase. I mainly focus on
the determination of the number of factors. I extend the existing panel criteria to high
dimension case where r may be increasing with N or T. I show that the number of
factors can be consistently estimated using the criteria. Also, Monte-Carlo simulation
demonstrates the nite sample properties of the proposed estimating method.
Lastly, I consider an empirical application of semiparametric econometrics to
the problem of purchasing power parity (hereafter PPP) hypothesis test. Traditional linear cointegration tests of PPP hypothesis often lead to rejection of the PPP
hypothesis. More recent studies allowing for some sort of nonlinearity in econometric
modelings suggest mixed results and leave this problem as an unresolved issue.
Therefore, I analyze PPP hypothesis within a semiparametric framework using the
varying coe cient model with integrated variables, which can capture the nonlinearity
of the economic structures. Applying the semiparametric functional cointegration
test method, I conduct the cointegration test of PPP hypothesis between U.S. and
Canada, U.S. and Japan, and U.S. and U.K., respectively to test the PPP hypothesis.
In contrast to the usual ndings based on linear model PPP hypothesis testing,
the semiparametric model based tests provide supporting evidence of the PPP hypothesis. | en |