Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Social and Behavioral Sciences
On Factor Models With Random Missing: Em Estimation, Inference, And Cross Validation, Sainan Jin, Ke Miao, Liangjun Su
On Factor Models With Random Missing: Em Estimation, Inference, And Cross Validation, Sainan Jin, Ke Miao, Liangjun Su
Research Collection School Of Economics
We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross validation-based method to determine the number of factors in factor models with or without missing values and justify its consistency. Simulations demonstrate that our cross validation method is robust to fat tails in the error distribution and …
On Factor Models With Random Missing: Em Estimation, Inference, And Cross Validation, Liangjun Su, Ke Miao, Sainan Jin
On Factor Models With Random Missing: Em Estimation, Inference, And Cross Validation, Liangjun Su, Ke Miao, Sainan Jin
Research Collection School Of Economics
We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross validation-based method to determine the number of factors in factor models with or without missing values and justify its consistency. Simulations demonstrate that our cross validation method is robust to fat tails in the error distribution and …
Determination Of Different Types Of Fixed Effects In Three-Dimensional Panels, Xun Lu, Ke Miao, Liangjun Su
Determination Of Different Types Of Fixed Effects In Three-Dimensional Panels, Xun Lu, Ke Miao, Liangjun Su
Research Collection School Of Economics
In this paper we propose a jackknife method to determine the type of fixed effects in three-dimensional panel data models. We show that with probability approaching 1, the method can select the correct type of fixed effects in the presence of only weak serial or cross-sectional dependence among the error terms. In the presence of strong serial correlation, we propose a modified jackknife method and justify its selection consistency. Monte Carlo simulations demonstrate the excellent finite sample performance of our method. Applications to two datasets in macroeconomics and international trade reveal the usefulness of our method.
Determining Individual Or Time Effects In Panel Data Models, Xun Lu, Liangjun Su
Determining Individual Or Time Effects In Panel Data Models, Xun Lu, Liangjun Su
Research Collection School Of Economics
In this paper we propose a jackknife method to determine individual and time e⁄ects in linear panel data models. We rst show that when both the serial and cross-sectional correlation among the idiosyncratic error terms are weak, our jackknife method can pick up the correct model with probability approaching one (w.p.a.1). In the presence of moderate or strong degree of serial correlation, we modify our jackknife criterion function and show that the modied jackknife method can also select the correct model w.p.a.1. We conduct Monte Carlo simulations to show that our new methods perform remarkably well in nite samples. We …
Cross-Validation In Nonparametric Regression With Outliers, Denis H. Y. Leung
Cross-Validation In Nonparametric Regression With Outliers, Denis H. Y. Leung
Research Collection School Of Economics
A popular data-driven method for choosing the bandwidth in standard kernel regression is cross-validation. Even when there are outliers ill the data, robust kernel regression can be used to estimate the unknown regression curve [Robust and Nonlinear Time Series Analysis. Lecture Notes in Statist. (1984) 26 163-184]. However, Under these Circumstances Standard cross-validation is no longer a satisfactory bandwidth selector because it is unduly influenced by extreme prediction errors caused by the existence of these Outliers. A more robust method proposed here is a cross-validation method that discounts the extreme prediction errors. In large samples the robust method chooses consistent …