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Full-Text Articles in Social and Behavioral Sciences
Refusal Bias In Hiv Data From The Demographic And Health Surveys: Evaluation, Critique And Recommendations, Oyelola A. Adegboye, Tomoki Fujii, Denis H. Y. Leung
Refusal Bias In Hiv Data From The Demographic And Health Surveys: Evaluation, Critique And Recommendations, Oyelola A. Adegboye, Tomoki Fujii, Denis H. Y. Leung
Research Collection School Of Economics
Non-response is a commonly encountered problem in many population-based surveys. Broadly speaking, non-response can be due to refusal or failure to contact the sample units. Although both types of non-response may lead to bias, there is much evidence to indicate that it is much easier to reduce the proportion of non-contacts than to do the same with refusals. In this article, we use data collected from a nationally representative survey under the Demographic and Health Surveys program to study non-response due to refusals to HIV testing in Malawi. We review existing estimation methods and propose novel approaches to the estimation …
Corrigendum To "On Time-Varying Factor Models: Estimation And Testing" [J. Econometrics 198 (2017) 84-101], Liangjun Su, Xia Wang
Corrigendum To "On Time-Varying Factor Models: Estimation And Testing" [J. Econometrics 198 (2017) 84-101], Liangjun Su, Xia Wang
Research Collection School Of Economics
We note that Su and Wang (2017, On Time-varying Factor Models: Estimation and Testing, Journal of Econometrics 198, 84-101) ignore the bias terms when estimating the time-varying factor models. In this note, we correct the theoretical results on the estimation of time-varying factor models. The asymptotic results for testing the correct specification of time invariant factor loadings are not affected.
Refusal Bias In Hiv Data From The Demographic And Health Surveys: Evaluation, Critique And Recommendations, Oyelola A. Adegboye, Tomoki Fujii, Denis H. Y. Leung
Refusal Bias In Hiv Data From The Demographic And Health Surveys: Evaluation, Critique And Recommendations, Oyelola A. Adegboye, Tomoki Fujii, Denis H. Y. Leung
Research Collection School Of Economics
Non-response is a commonly encountered problem in many population-based surveys. Broadly speaking, non-response can be due to refusal or failure to contact the sample units. Although both types of non-response may lead to bias, there is much evidence to indicate that it is much easier to reduce the proportion of non-contacts than to do the same with refusals. In this article, we use data collected from a nationally-representative survey under the Demographic and Health Surveys program to study non-response due to refusals to HIV testing in Malawi. We review existing estimation methods and propose novel approaches to the estimation of …
Indirect Inference In Spatial Autoregression, Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
Indirect Inference In Spatial Autoregression, Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
Research Collection School Of Economics
Ordinary least-squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). In this paper, we explore the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi-maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (a) the II estimator displays little bias …
Indirect Inference In Spatial Autoregression, Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
Indirect Inference In Spatial Autoregression, Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
Research Collection School Of Economics
Ordinary least-squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). In this paper, we explore the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi-maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (a) the II estimator displays little bias …
In-Fill Asymptotic Theory For Structural Break Point In Autoregression: A Unified Theory, Liang Jiang, Xiaohu Wang, Jun Yu
In-Fill Asymptotic Theory For Structural Break Point In Autoregression: A Unified Theory, Liang Jiang, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
This paper obtains the exact distribution of the maximum likelihood estimatorof structural break point in the OrnsteinñUhlenbeck process when a continuousrecord is available. The exact distribution is asymmetric, tri-modal, dependenton the initial condition. These three properties are also found in the önite sampledistribution of the least squares (LS) estimator of structural break point inautoregressive (AR) models. Motivated by these observations, the paper then developsan in-öll asymptotic theory for the LS estimator of structural break point inthe AR(1) coe¢ cient. The in-öll asymptotic distribution is also asymmetric, trimodal,dependent on the initial condition, and delivers excellent approximationsto the önite sample distribution. Unlike …
Bias In The Estimation Of Mean Reversion In Continuous-Time Levy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu
Bias In The Estimation Of Mean Reversion In Continuous-Time Levy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu
Research Collection School Of Economics
This paper develops the approximate bias of the ordinary least squares estimator of the mean reversion parameter in continuous-time Levy processes. Several cases are considered, depending on whether the long-run mean is known or unknown and whether the initial condition is fixed or random. The approximate bias is used to construct a bias corrected estimator. The performance of the approximate bias and the bias corrected estimator is examined using simulated data.
Halbert White Jr. Memorial Jfec Lecture: Pitfalls And Possibilities In Predictive Regression, Peter C. B. Phillips
Halbert White Jr. Memorial Jfec Lecture: Pitfalls And Possibilities In Predictive Regression, Peter C. B. Phillips
Research Collection School Of Economics
Financial theory and econometric methodology both struggle in formulating models that are logically sound in reconciling short-run martingale behavior for financial assets with predictable long-run behavior, leaving much of the research to be empirically driven. The present article overviews recent contributions to this subject, focusing on the main pitfalls in conducting predictive regression and on some of the possibilities offered by modern econometric methods. The latter options include indirect inference and techniques of endogenous instrumentation that use convenient temporal transforms of persistent regressors. Some additional suggestions are made for bias elimination, quantile crossing amelioration, and control of predictive model misspecification.
On The Effect And Remedies Of Shrinkage On Classification Probability Estimation, Zhengxiao Wu, Yufeng Liu, Zhengxiao Wu
On The Effect And Remedies Of Shrinkage On Classification Probability Estimation, Zhengxiao Wu, Yufeng Liu, Zhengxiao Wu
Research Collection School of Economics
Shrinkage methods have been shown to be effective for classification problems. As a form of regularization, shrinkage through penalization helps to avoid overfitting and produces accurate classifiers for prediction, especially when the dimension is relatively high. Despite the benefit of shrinkage on classification accuracy of resulting classifiers, in this article, we demonstrate that shrinkage creates biases on classification probability estimation. In many cases, this bias can be large and consequently yield poor class probability estimation when the sample size is small or moderate. We offer some theoretical insights into the effect of shrinkage and provide remedies for better class probability …
Bias In The Mean Reversion Estimator In Continuous-Time Gaussian And Lévy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu
Bias In The Mean Reversion Estimator In Continuous-Time Gaussian And Lévy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu
Research Collection School Of Economics
Continuous-time Levy processes have become increasingly popular in the asset pricing literature and estimation of the mean reversion parameter has attracted attention recently. This paper develops the approximate nite-sample bias of the ordinary least squares or quasi maximum likelihood estimator of the mean reversion parameter in continuous-time Levy processes. Simulations show that in general the approximate bias works well in capturing the true bias of the mean reversion estimator under difference scenarios. However, when the time span is small and the mean reversion parameter is approaching its lower bound, we find it more difficult to approximate well the nite-sample bias.
Bias In Estimating Multivariate And Univariate Diffusions, Xiaohu Wang, Peter C. B. Phillips, Jun Yu
Bias In Estimating Multivariate And Univariate Diffusions, Xiaohu Wang, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
Multivariate continuous time models are now widely used in economics and finance. Empirical applications typically rely on some process of discretization so that the system may be estimated with discrete data. This paper introduces a framework for discretizing linear multivariate continuous time systems that includes the commonly used Euler and trapezoidal approximations as special cases and leads to a general class of estimators for the mean reversion matrix. Asymptotic distributions and bias formulae are obtained for estimates of the mean reversion parameter. Explicit expressions are given for the discretization bias and its relationship to estimation bias in both multivariate and …
Improved Maximum-Likelihood Estimation For The Common Shape Parameter Of Several Weibull Populations, Zhenlin Yang, Dennis K. J. Lin
Improved Maximum-Likelihood Estimation For The Common Shape Parameter Of Several Weibull Populations, Zhenlin Yang, Dennis K. J. Lin
Research Collection School Of Economics
The biasness problem of the maximum-likelihood estimate (MLE) of the common shape parameter of several Weibull populations is examined in detail. A modified MLE (MMLE) approach is proposed. In the case of complete and Type II censored data, the bias of the MLE can be substantial. This is noticeable even when the sample size is large. Such a bias increases rapidly as the degree of censorship increases and as more populations are involved. The proposed MMLE, however, is nearly unbiased and much more efficient than the MLE, irrespective of the degree of censorship, the sample sizes, and the number of …
Bias In Dynamic Panel Estimation With Fixed Effects, Incidental Trends And Cross Section Dependence, Peter C. B. Phillips, Donggyu Sul
Bias In Dynamic Panel Estimation With Fixed Effects, Incidental Trends And Cross Section Dependence, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
Explicit asymptotic bias formulae are given for dynamic panel regression estimators as the cross section sample size N --> ∞. The results extend earlier work by Nickell [1981. Biases in dynamic models with fixed effects. Econometrica 49, 1417-1426] and later authors in several directions that are relevant for practical work, including models with unit roots, deterministic trends, predetermined and exogenous regressors, and errors that may be cross sectionally dependent. The asymptotic bias is found to be so large when incidental linear trends are fitted and the time series sample size is small that it changes the sign of the autoregressive …