Hostname: page-component-6898cfcb55-kwdml Total loading time: 0 Render date: 2026-11-11T11:31:22.509Z Has data issue: false hasContentIssue false
  • English
  • Français

THE ET INTERVIEW: BENEDIKT M. PÖTSCHER

Part of:ET Interviews

Published online by Cambridge University Press:  18 September 2024

Manfred DeistlerOpen the ORCID record for Manfred Deistler[Opens in a new window]
Manfred Deistler*
Affiliation:
University of Technology Vienna
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Information

Type
ET INTERVIEW
Information
Econometric Theory , Volume 41 , Issue 4 , August 2025, pp. 739 - 778
Copyright
© The Author(s), 2024. Published by Cambridge University Press

References

REFERENCES

Anderson, T. W. (1971). The statistical analysis of time series . Wiley.Google Scholar
Andrews, D. W. K. (1987). Consistency in nonlinear econometric models: A generic uniform law of large numbers. Econometrica , 55, 1465–1471.CrossRefGoogle Scholar
Andrews, D. W. K. (1992). Generic uniform convergence. Econometric Theory , 8, 241–257.CrossRefGoogle Scholar
Andrews, D. W. K., & Guggenberger, P. (2009). Hybrid and size-corrected subsampling methods. Econometrica , 77, 721–762.Google Scholar
Andrews, D. W. K., & Ploberger, W. (1996). Testing for serial correlation against an ARMA $\left(1,1\right)$ process. Journal of the American Statistical Association, 91, 1331–1342.Google Scholar
Bachoc, F., Leeb, H., & Pötscher, B. M. (2019). Valid confidence intervals for post-model-selection predictors. Annals of Statistics , 47, 1475–1504.CrossRefGoogle Scholar
Bates, C. E., & White, H. (1985). A unified theory of consistent estimation for parametric models. Econometric Theory , 1, 151–178.CrossRefGoogle Scholar
Bauer, P., Pötscher, B. M., & Hackl, P. (1988). Model selection by multiple test procedures. Statistics , 19, 39–44.CrossRefGoogle Scholar
Berk, R., Brown, L., Buja, A., Zhang, K., & Zhao, L. (2013). Valid post-selection inference. Annals of Statistics , 41, 802–837.CrossRefGoogle Scholar
Bickel, P. J. (1982). On adaptive estimation. Annals of Statistics , 10, 647–671.CrossRefGoogle Scholar
Birman, M., & Solomjak, M. Z. (1967). Piecewise polynomial approximations of functions of classes $W^{\alpha}_{p} $ . Matematicheskii Sbornik , 73(115), 331–355.Google Scholar
Blough, S. R. (1988). On the impossibility of testing for unit roots and cointegration in finite samples. Working Paper. Johns Hopkins University.Google Scholar
Bomze, I. M., & Pötscher, B. M. (1989). Game theoretical foundations of evolutionary stability . Lecture Notes in Economics and Mathematical Systems, 324. Springer-Verlag.CrossRefGoogle Scholar
Campbell, J. Y., & Mankiw, N. G. (1987a). Are output fluctuations transitory? Quarterly Journal of Economics , 102, 857–880.CrossRefGoogle Scholar
Campbell, J. Y., & Mankiw, N. G. (1987b). Permanent and transitory components in macroeconomic fluctuations. American Economic Review Papers and Proceeedings , 77, 111–117.Google Scholar
Christiano, L. J., & Eichenbaum, M. (1990). Unit roots in real GNP: Do we know and do we care? Carnegie-Rochester Conference Series on Public Policy , 32, 7–62.CrossRefGoogle Scholar
Cochrane, J. H. (1988). How big is the random walk in GNP? Journal of Political Economy , 96, 893–920.CrossRefGoogle Scholar
Dahlhaus, R., & Pötscher, B. M. (1989). Convergence results for maximum likelihood type estimators in multivariable ARMA models II. Journal of Multivariate Analysis , 30, 241–244.CrossRefGoogle Scholar
Deistler, M., Dunsmuir, W., & Hannan, E. J. (1978). Vector linear time series models: Corrections and extensions. Advances in Applied Probability , 10, 360–372.CrossRefGoogle Scholar
Deistler, M., & Pötscher, B. M. (1984). The behaviour of the likelihood function for ARMA models. Advances in Applied Probability , 16, 843–866.CrossRefGoogle Scholar
Diebold, F., & Rudebusch, G. (1989). Long memory and persistence in aggregate output. Journal of Monetary Economics , 24, 189–209.CrossRefGoogle Scholar
Domowitz, I., & White, H. (1982). Misspecified models with dependent observations. Journal of Econometrics , 20, 35–58.CrossRefGoogle Scholar
Dunsmuir, W., & Hannan, E. J. (1976). Vector linear time series models. Advances in Applied Probability , 8, 339–364.CrossRefGoogle Scholar
Ensor, K. B., & Newton, H. J. (1988). The effect of order estimation on estimating the peak frequency of an autoregressive spectral density. Biometrika , 75, 587–589.CrossRefGoogle Scholar
Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association , 96, 1348–1360.CrossRefGoogle Scholar
Faust, J. (1996). Near observational equivalence and theoretical size problems with unit root tests. Econometric Theory , 12, 724–731.CrossRefGoogle Scholar
Findley, D. F., Pötscher, B. M., & Wei, C.-Z. (2001). Uniform convergence of sample second moments of families of time series arrays. Annals of Statistics , 29, 815–838.CrossRefGoogle Scholar
Findley, D. F., Pötscher, B. M., & Wei, C.-Z. (2004). Modeling of time series arrays by multistep prediction or likelihood methods. Journal of Econometrics , 118, 151–187.CrossRefGoogle Scholar
Gach, F., & Pötscher, B. M. (2011). Nonparametric maximum likelihood density estimation and simulation-based minimum distance estimators. Mathematical Methods of Statistics , 20, 288–326.CrossRefGoogle Scholar
Hamilton, J. D. (1994). Time series analysis . Princeton University Press.CrossRefGoogle Scholar
Hannan, E. J. (1982). Testing for autocorrelation and Akaike’s criterion. Journal of Applied Probability , 19, 403–412.CrossRefGoogle Scholar
Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society, Series B , 41, 190–195.CrossRefGoogle Scholar
Hauser, M., Pötscher, B. M., & Reschenhofer, E. (1999). Measuring persistence in aggregate output: ARMA models, fractionally integrated ARMA models and nonparametric procedures. Empirical Economics , 24, 243–269.CrossRefGoogle Scholar
Hoadley, B. (1971). Asymptotic properties of maximum likelihood estimators for the independent not identically distributed case. Annals of Mathematical Statistics , 42, 1977–1991.CrossRefGoogle Scholar
Kabaila, P. (1983). Parameter values of ARMA models minimising the one-step-ahead prediction error when the true system is not in the model set. Journal of Applied Probability , 20, 405–408.CrossRefGoogle Scholar
Kimura, M. (1982). Molecular evolution, protein polymorphism and the neutral theory . Springer-Verlag.Google Scholar
Kolmogorov, A. N., & Tihomirov, V. M. (1961). $\varepsilon$ -entropy and $\varepsilon$ -capacity of sets in functional spaces. American Mathematical Society Translations , 17, 277–364.Google Scholar
Leeb, H., & Pötscher, B. M. (2003). The finite-sample distribution of post-model-selection estimators, and uniform versus non-uniform approximations. Econometric Theory , 19, 100–142.CrossRefGoogle Scholar
Leeb, H., & Pötscher, B. M. (2005). Model selection and inference: Facts and fiction. Econometric Theory , 21, 21–59.CrossRefGoogle Scholar
Leeb, H., & Pötscher, B. M. (2006a). Can one estimate the conditional distribution of post-model-selection estimators? Annals of Statistics , 34, 2554–2591.CrossRefGoogle Scholar
Leeb, H., & Pötscher, B. M. (2006b). Performance limits for estimators of the risk or distribution of shrinkage-type estimators, and some general lower risk-bound results. Econometric Theory , 22, 69–97. (Correction, ibid., 24, 581–583).CrossRefGoogle Scholar
Leeb, H., & Pötscher, B. M. (2008a). Can one estimate the unconditional distribution of post-model-selection estimators? Econometric Theory , 24, 338–376.CrossRefGoogle Scholar
Leeb, H., & Pötscher, B. M. (2008b). Sparse estimators and the oracle property, or the return of Hodges’ estimator. Journal of Econometrics , 142, 201–211.CrossRefGoogle Scholar
Leeb, H., & Pötscher, B. M. (2017). Testing in the presence of nuisance parameters: Some comments on tests post-model-selection and random critical values. In Ahmed, S. (Eds.), Big and complex data analysis (pp. 69–82). Contributions to Statistics. Springer.CrossRefGoogle Scholar
Leeb, H., Pötscher, B. M., & Ewald, K. (2015). On various confidence intervals post-model-selection. Statistical Science , 30, 216–227.CrossRefGoogle Scholar
Manski, C. F. (1984). Adaptive estimation of nonlinear regression models. Econometric Reviews , 3, 145–210.CrossRefGoogle Scholar
Nelson, C. R., & Plosser, C. I. (1982). Trends and random walks in macroeconomic time series: Some evidence and implications. Journal of Monetary Economics , 10, 139–162.CrossRefGoogle Scholar
Newey, W. K. (1991). Uniform convergence in probability and stochastic equicontinuity. Econometrica , 59, 1161–1167.CrossRefGoogle Scholar
Nickl, R. (2003). Asymptotic distribution theory of post-model-selection maximum likelihood estimators. Master’s thesis, University of Vienna.Google Scholar
Nickl, R. (2007). Donsker-type theorems for nonparametric maximum likelihood estimators. Probability Theory and Related Fields , 138, 411–449. (Erratum, ibid., 141, 331–332).CrossRefGoogle Scholar
Nickl, R., & Pötscher, B. M. (2007). Bracketing metric entropy rates and empirical central limit theorems for function classes of Besov- and Sobolev-type. Journal of Theoretical Probability , 20, 177–199.CrossRefGoogle Scholar
Nickl, R., & Pötscher, B. M. (2010). Efficient simulation-based minimum distance estimation and indirect inference. Mathematical Methods of Statistics , 19, 327–364.CrossRefGoogle Scholar
Owen, G. (1968). Game theory . W. B. Saunders Co. Google Scholar
Perron, P., & Ren, L. (2011). On the irrelevance of impossibility theorems: The case of the long-run variance. Journal of Time Series Econometrics, 3, 3(3).CrossRefGoogle Scholar
Pötscher, B. M. (1982). Some results on ${\omega}_{\mu }$ -metric spaces. Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae. Sectio Mathematica, 25, 3–18. (Correction, ibid., 28, 283).Google Scholar
Pötscher, B. M. (1983). Order estimation in ARMA-models by Lagrangian multiplier tests. Annals of Statistics , 11, 872–885. (Correction, ibid., 12, 785).CrossRefGoogle Scholar
Pötscher, B. M. (1985a). The behaviour of the Lagrangian multiplier test in testing the orders of an ARMA-model. Metrika , 32, 129–150.CrossRefGoogle Scholar
Pötscher, B. M. (1985b). Moments and order statistics of extinction times in multitype branching processes and their relation to random selection models. Bulletin of Mathematical Biology , 47, 263–272.CrossRefGoogle Scholar
Pötscher, B. M. (1987a). Convergence results for maximum likelihood type estimators in multivariable ARMA models. Journal of Multivariate Analysis , 21, 29–52.CrossRefGoogle Scholar
Pötscher, B. M. (1987b). A generalization of Urysohn’s metrization theorem and its set-theoretic consequences. Studia Scientiarum Mathematicarum Hungarica , 22, 457–461.Google Scholar
Pötscher, B. M. (1989). Model selection under nonstationarity: Autoregressive models and stochastic linear regression models. Annals of Statistics , 17, 1257–1274.CrossRefGoogle Scholar
Pötscher, B. M. (1990). Estimation of autoregressive moving-average order given an infinite number of models and approximation of spectral densities. Journal of Time Series Analysis , 11, 165–179.CrossRefGoogle Scholar
Pötscher, B. M. (1991a). Effects of model selection on inference. Econometric Theory , 7, 163–185.CrossRefGoogle Scholar
Pötscher, B. M. (1991b). Noninvertibility and pseudo-maximum likelihood estimation of misspecified ARMA models. Econometric Theory , 7, 435–449. (Corrigendum, ibid., 10, 811).CrossRefGoogle Scholar
Pötscher, B. M. (2002). Lower risk bounds and properties of confidence sets for ill-posed estimation problems with applications to spectral density and persistence estimation, unit roots, and estimation of long memory parameters. Econometrica , 70, 1035–1065.CrossRefGoogle Scholar
Pötscher, B. M. (2006). The distribution of model averaging estimators and an impossibility result regarding its estimation. IMS Lecture Notes - Monograph Series , 52, 113–129.CrossRefGoogle Scholar
Pötscher, B. M. (2009). Confidence sets based on sparse estimators are necessarily large. Sankhya, 71-A, 1–18.Google Scholar
Pötscher, B. M., & Leeb, H. (2009). On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding. Journal of Multivariate Analysis , 100, 2065–2082.CrossRefGoogle Scholar
Pötscher, B. M., & Preinerstorfer, D. (2018). Controlling the size of autocorrelation robust tests. Journal of Econometrics , 207, 406–431.CrossRefGoogle Scholar
Pötscher, B. M., & Preinerstorfer, D. (2019). Further results on size and power of heteroskedasticity and autocorrelation robust tests, with an application to trend testing. Electronic Journal of Statistics , 13, 3893–3942.CrossRefGoogle Scholar
Pötscher, B. M., & Preinerstorfer, D. (2021). Valid heteroskedasticity robust testing. Econometric Theory. Published online by Cambridge University Press: 11 September 2023, 1–53.Google Scholar
Pötscher, B. M., & Preinerstorfer, D. (2023). How reliable are bootstrap-based heteroskedasticity robust tests? Econometric Theory , 39, 789–847.CrossRefGoogle Scholar
Pötscher, B. M., & Prucha, I. R. (1986a). A class of partially adaptive one-step $M$ -estimators for the nonlinear regression model with dependent observations. Journal of Econometrics , 32, 219–251.CrossRefGoogle Scholar
Pötscher, B. M., & Prucha, I. R. (1986b). Consistency in nonlinear econometrics: A generic uniform law of large numbers and some comments on recent results. Working Paper No 86-9. Department of Economics, University of Maryland.Google Scholar
Pötscher, B. M., & Prucha, I. R. (1989). A uniform law of large numbers for dependent and heterogeneous data processes. Econometrica , 57, 675–683.CrossRefGoogle Scholar
Pötscher, B. M., & Prucha, I. R. (1991a). Basic structure of the asymptotic theory in dynamic nonlinear econometric models. I. Consistency and approximation concepts. Econometric Reviews , 10, 125–216.CrossRefGoogle Scholar
Pötscher, B. M., & Prucha, I. R. (1991b). Basic structure of the asymptotic theory in dynamic nonlinear econometric models II. Asymptotic normality.. Econometric Reviews , 10, 253–357.CrossRefGoogle Scholar
Pötscher, B. M., & Prucha, I. R. (1994a). Generic uniform convergence and equicontinuity concepts for random functions: An exploration of the basic structure. Journal of Econometrics , 60, 23–63.CrossRefGoogle Scholar
Pötscher, B. M., & Prucha, I. R. (1994b). On the formulation of uniform laws of large numbers: A truncation approach. Statistics , 25, 343–360.CrossRefGoogle Scholar
Pötscher, B. M., & Prucha, I. R. (1997). Dynamic nonlinear econometric models: Asymptotic theory . Springer-Verlag.CrossRefGoogle Scholar
Pötscher, B. M., & Schneider, U. (2009). On the distribution of the adaptive LASSO estimator. Journal of Statistical Planning and Inference , 139, 2775–2790.CrossRefGoogle Scholar
Pötscher, B. M., & Schneider, U. (2010). Confidence sets based on penalized maximum likelihood estimators in Gaussian regression. Electronic Journal of Statistics , 4, 334–360.CrossRefGoogle Scholar
Pötscher, B. M., & Schneider, U. (2011). Distributional results for thresholding estimators in high-dimensional Gaussian regression models. Electronic Journal of Statistics , 5, 1876–1934.CrossRefGoogle Scholar
Pötscher, B. M., & Srinivasan, S. (1994). A comparison of order estimation procedures for ARMA models. Statistica Sinica , 4, 29–50.Google Scholar
Preinerstorfer, D. (2017). Finite sample properties of tests based on prewhitened nonparametric covariance estimators. Electronic Journal of Statistics , 11, 2097–2167.CrossRefGoogle Scholar
Preinerstorfer, D., & Pötscher, B. M. (2016). On size and power of heteroskedasticity and autocorrelation robust tests. Econometric Theory , 32, 261–358.CrossRefGoogle Scholar
Preinerstorfer, D., & Pötscher, B. M. (2017). On the power of invariant tests for hypotheses on a covariance matrix. Econometric Theory , 33, 1–68.CrossRefGoogle Scholar
Prucha, I. R., & Kelejian, H. H. (1984). The structure of simultaneous equation estimators: A generalization towcards nonnormal disturbances. Econometrica , 52, 721–736.CrossRefGoogle Scholar
Schuster, P., & Sigmund, K. (1984). Random selection – a simple model based on linear birth and death processes. Bulletin of Mathematical Biology , 46, 11–17.Google Scholar
Sen, P. K. (1979). Asymptotic properties of maximum likelihood estimators based on conditional specification. Annals of Statistics , 7, 1019–1033.CrossRefGoogle Scholar
Sewastjanow, B. A. (1974). Verzweigungsprozesse , vol. 34 of Mathematische Lehrbücher und Monographien, II. Abteilung: Mathematische Monographien . Akademie-Verlag, Berlin.Google Scholar
Tanaka, K., & Satchell, S. E. (1989). Asymptotic properties of the maximum-likelihood and nonlinear least-squares estimators for noninvertible moving average models. Econometric Theory , 5, 333–353.CrossRefGoogle Scholar
White, H. (1980). Nonlinear regression on cross-section data. Econometrica , 48, 721–746.CrossRefGoogle Scholar
White, H., & Domowitz, I. (1984). Nonlinear regression with dependent observations. Econometrica , 52, 143–161.CrossRefGoogle Scholar
Willard, S. (1970). General topology. Addison-Wesley Publishing Co., Reading, Mass.-London-Don Mills, Ont.Google Scholar