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Dr Sebastian Kripfganz

Dr Sebastian Kripfganz

Senior Lecturer in Econometrics

2110

+44 (0) 1392 722110

1.23
Streatham Court, University of Exeter, Rennes Drive, Exeter, EX4 4PU, UK

Prior to joining the University of Exeter Business School in 2015 as a Lecturer in Economics, Sebastian Kripfganz completed his doctoral studies at Goethe University Frankfurt where he also held a position as a research and teaching assistant.

His international experience includes a research visit at Michigan State University on invitation by Professor Jeffrey Wooldridge and a short-term consultancy in the Poverty Reduction and Economic Management Network of The World Bank in Washington, DC.

He can also look back to a 19-months professional experience in the Economics Department of Bayerische Landesbank after he received his Economics diploma from the University of Mannheim. His research interests centre on dynamic panel data and spatial econometrics.

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Research clusters

Research interests

  • Panel Data Econometrics
  • Spatial Econometrics

Dr Kripfganz’ research topics are primarily in the area of panel data econometrics with a special focus on estimation and specification of dynamic panel models when the time horizon is short. His work includes the development and improvement of generalized method of moments (GMM) and maximum likelihood (ML) estimation procedures in models with incidental parameters and cross-sectional dependence. He also places special emphasis on the robustness of estimation and inference methods to model misspecification.

Besides his methodological work, Dr Kripfganz is interested in the application of these methods to the analysis of human well-being, both at an aggregate and disaggregate level. This includes the determinants of economic growth and regional convergence, the dynamics of individual and family income, as well as the analysis of social interactions and network effects.

Current research activity

The current research of Dr Kripfganz is concerned with the likelihood-based estimation of dynamic panel data models with cross-sectional spillover effects, the identification of peer effects in social networks, and the treatment of time-invariant variables in panel data models. Parallel to his research, Dr Kripfganz is also developing a set of comprehensive Stata commands that he is making available to the research community.

Key publications | Publications by category | Publications by year

Publications by category


Journal articles

Breitung J, Kripfganz S, Hayakawa K (2021). Bias-corrected method of moments estimators for dynamic panel data models. Econometrics and Statistics, 24, 116-132. Abstract. DOI.
Kiviet JF, Kripfganz S (2021). Instrument approval by the Sargan test and its consequences for coefficient estimation. Economics Letters, 205 Abstract. DOI.
Kripfganz S, Sarafidis V (2021). Instrumental-variable estimation of large-T panel-data models with common factors. Stata Journal, 21(3), 659-686. Abstract. DOI.
Kripfganz S, Kiviet JF (2021). kinkyreg: Instrument-free inference for linear regression models with endogenous regressors. Stata Journal, 21(3), 772-813. Abstract. DOI.
Kripfganz S, Schneider DC (2020). Response Surface Regressions for Critical Value Bounds and Approximate p‐values in Equilibrium Correction Models. Oxford Bulletin of Economics and Statistics, 82(6), 1456-1481. Abstract. DOI.
Kripfganz S, Schwarz C (2019). Estimation of linear dynamic panel data models with time-invariant regressors. Journal of Applied Econometrics, 34(4), 526-546. Abstract. DOI.
Kripfganz S (2016). Quasi–maximum likelihood estimation of linear dynamic short-T panel-data models. Stata Journal, 16(4), 1013-1038. Abstract. DOI.

Publications by year


In Press

Kripfganz S, Schneider DC (In Press). ARDL: Stata module to perform autoregressive distributed lag model estimation.  Abstract.
Kripfganz S (In Press). XTDPDGMM: Stata module to perform generalized method of moments estimation of linear dynamic panel data models.  Abstract.
Kripfganz S (In Press). XTDPDQML: Stata module to perform quasi-maximum likelihood linear dynamic panel data estimation.  Abstract.
Kripfganz S (In Press). XTSEQREG: Stata module to perform sequential estimation of linear panel data models.  Abstract.

2021

Breitung J, Kripfganz S, Hayakawa K (2021). Bias-corrected method of moments estimators for dynamic panel data models. Econometrics and Statistics, 24, 116-132. Abstract. DOI.
Kiviet JF, Kripfganz S (2021). Instrument approval by the Sargan test and its consequences for coefficient estimation. Economics Letters, 205 Abstract. DOI.
Kripfganz S, Sarafidis V (2021). Instrumental-variable estimation of large-T panel-data models with common factors. Stata Journal, 21(3), 659-686. Abstract. DOI.
Kripfganz S, Kiviet JF (2021). kinkyreg: Instrument-free inference for linear regression models with endogenous regressors. Stata Journal, 21(3), 772-813. Abstract. DOI.

2020

Kripfganz S, Schneider DC (2020). Response Surface Regressions for Critical Value Bounds and Approximate p‐values in Equilibrium Correction Models. Oxford Bulletin of Economics and Statistics, 82(6), 1456-1481. Abstract. DOI.

2019

Kripfganz S, Schwarz C (2019). Estimation of linear dynamic panel data models with time-invariant regressors. Journal of Applied Econometrics, 34(4), 526-546. Abstract. DOI.

2016

Kripfganz S (2016). Quasi–maximum likelihood estimation of linear dynamic short-T panel-data models. Stata Journal, 16(4), 1013-1038. Abstract. DOI.

The teaching interests of Dr Kripfganz are closely related to his research area. He enjoys teaching fundamental and state-of-the-art econometric methods, particularly concerning the analysis of panel data sets. In his courses, he aims to provide the students with a requisite know-how that will be of practical use for them irrespective of whether they want to follow the path of an academic or a professional career.

Modules

2023/24