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Full-Text Articles in Social and Behavioral Sciences
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Research Collection School Of Economics
We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and sudden structural breaks with a possibly unknown number of breaks and unknown break dates in the factor loadings. No restrictions on the form of alternatives or trimming of boundary regions near the beginning or end of the sample period …
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We apply the factor approach to the correlation matrix to forecast large covariance matrix of asset returns using high-frequency data, using the principal component method to model the underlying latent factors of the correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. Our proposed method is found to perform better in reporting smaller forecast errors than some selected competitors. Empirical application to a portfolio of 100 NYSE and NASDAQ stocks shows that our method provides lower out-of-sample realized variance in selecting global minimum …
Essays On Heterogeneous Large Panel Data Models, Ke Miao
Essays On Heterogeneous Large Panel Data Models, Ke Miao
Dissertations and Theses Collection (Open Access)
This dissertation consists of three papers which contribute to the estimation and inference theory of the heterogeneous large panel data models. The first chapter studies a panel threshold model with interactive fixed effects. The least-squares estimators in the shrinking-threshold-effect framework are explored. The inference theory on both slope coefficients and the threshold parameter is derived, and a test for the presence of the threshold effect is proposed. The second chapter considers the least-squares estimation of a panel structure threshold regression (PSTR) model, where parameters may exhibit latent group structures. Under some regularity conditions, the latent group structure can be correctly …