• Learning is an experience. Everything else is just information.

    -Albert Einstein.

Shew Fan Liu

I have been a PhD Candidate of the School of Economics of Singapore Management University since 2011. My main research area of interest is Spatial Econometrics.

 

 

Skills, Awards and Experience

PHD CANDIDATE IN ECONOMICS, SINGAPORE MANAGEMENT UNIVERSITY.

Professional Experience
  • SMU, Presidential Doctoral Fellowship, 2015-2016

    SMU, Research Scholarship, 2011-2015

    LSE, PJD Wiles Scholarship, 2010-2011
  • Liu S. F., Yang Z., 2015. “Modified QML Estimation of Spatial Autoregressive Models with Unknown Heteroskedasticity and Normality,” Regional Science and Urban Economics, {52}, 50-70.

    Liu S. F., Yang Z., 2015. Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Error Dependence Model. Econometrics {3}, 376-411.

    Liu S. F., Yang Z., 2015. Improved inferences for spatial regression models. Regional Science and Urban Economics {55}, 55-67.

    Z. Yang, J. Yu, S. F. Liu, "Bias Correction and Refined Inferences for Fixed Effects Spatial Panel Data Models" forthcoming in Regional Science and Urban Economics.

  • “A General Method for Heteroskedasticity Robust Estimation for Spatial Econometric Models.”
  • "Asymptotic and Finite Sample Properties of QML Estimators for Spatial Error Dependence Models", presented at The Singapore Economic Review Conference, Singapore, August 6-8, 2013.
    "Asymptotic and Finite Sample Properties of QML Estimators for Spatial Error Dependence Models", presented at The Asian Meeting of the Econometric Society, Singapore, August 2-4, 2013.
    "Modified QML Estimation of Spatial Autoregressive Models with Unknown Heteroskedasticity and Nonnormality", presented at The 8th World Conference of the Spatial Econometric Association, Zurich, June 11-13, 2014.
    "Bias Correction and Refined Inferences for Fixed Effects Spatial Panel Data Models" presented at the 26th EC2 Conference – Theory and Practice of Spatial Econometrics, Edinburgh, December 18-19, 2015.
Education
  • PhD. Economics

    Singapore Management University, August 2016 (expected)

  • MSc. Econometrics & Mathematical Economics

    The London School of Economics and Political Science

  • BSc. Mathematics & Economics

    The University of London (International)

My Interest
Fields on interest
  • Econometric Theory
  • Spatial Econometrics

Research

My motivation for research is primarily driven by the passion to seek knowledge and gather around people who have the same intellectual curiosity. However, the work we do also go a long way in making a positive contribution to the society we live in and I wish to be a part of that community.

Bias Correction and Refined Inferences for Fixed Effects Spatial Panel Data Models

his paper first presents simple methods for conducting up to third-order bias and variance corrections for the quasi maximum likelihood (QML) estimators of the spatial parameter(s) in the fixed effects spatial panel data (FE-SPD) models. Then, it shows how the bias and variance corrections lead to refined t-ratios for spatial effects and for covariate effects. The implementation of these corrections depends on the proposed bootstrap methods of which validity is established. Monte Carlo results reveal that (i) the QML estimators of the spatial parameters can be quite biased, (ii) a second-order bias correction effectively removes the bias, and (iii) the proposed t-ratios are much more reliable than the usual t-ratios.

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Improved Inferences for Spatial Regression Models

The quasi-maximum likelihood (QML) method is popular in the estimation and inference for spatial regression models. However, the QML estimators (QMLEs) of the spatial parameters can be quite biased and hence the standard inferences for the regression coefficients (based on t-ratios) can be seriously a ffected. This issue, however, has not been addressed. The QMLEs of the spatial parameters can be bias-corrected based on the general method of Yang (2015b, J. of Econometrics 186, 178-200). In this paper, we demonstrate that by simply replacing the QMLEs of the spatial parameters by their bias-corrected versions, the usual t-ratios for the regression coecients can be greatly improved. We propose further corrections on the standard errors of the QMLEs of the regression coefficients, and the resulted t-ratios perform superbly, leading to much more reliable inferences.

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Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Error Dependence Model

In studying the asymptotic and finite-sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite-sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model as it leads potentially to much improved inferences for the regression coefficients. Contrary to the common perceptions, both the large and small sample behaviours of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe and the proposed bias correction procedure is very effective.

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Modified QML Estimation of Spatial Autoregressive Models with Unknown Heteroskedasticity and Nonnormality

In the presence of heteroskedasticity, Lin and Lee (2010) show that the quasi-maximum likelihood (QML) estimator of the spatial autoregressive (SAR) model can be inconsistent as a ‘necessary’ condition for consistency can be violated, and thus propose robust GMM estimators for the model. In this paper, we first show that this condition may hold in certain situations and when it does the regular QML estimator can still be consistent. In cases where this condition is violated, we propose a simple modified QML estimation method robust against unknown heteroskedasticity. In both cases, asymptotic distributions of the estimators are derived, and methods for estimating robust variances are given, leading to robust inferences for the model. Extensive Monte Carlo results show that the modified QML estimator outperforms the GMM and QML estimators even when the latter is consistent. The proposed methods are then extended to the more general SARAR models.

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Teaching

As the age old cliché goes, “catch me a fish I’ll eat for a day, teach me to fish I’ll eat for a lifetime”, my teaching philosophy revolves around the notion of leading students to the path of independent learning. I believe it is a teacher’s primary responsibility towards his/her students to ensure they experience the benefits of empowered learning.

Instructor
STAT101: Introductory Statistics, SMU, AY2015/16
Instructor
PhD. Economics "Math Camp", SMU, Aug 2013 and Aug 2014
Instructor
MSc. Applied Economics "Introductory Mathematics for Economists", SMU, Jul 2014.

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