Forschung

Forschungsbereiche

Semi- und nicht-parametrische Verfahren, Forecasting in nicht-parametrischen Modellen, Stochastische Prozesse unter Strukturbrüchen, Zeitreihenökonometrie, computergestützte Statistik, hedonische Regressionsverfahren insbesondere deren Anwendung auf Immobilien, Schätzung der Konsumnachfrage, Markt-Timing, staatenübergreifender Vergleich von Lebensstandards, Dynamische Modellierung von langfristigen Investitionsrisiken, Machine-Learning und Big-Data

Forschungsprojekte

2017 - 2020 Price Indices and Imputed Prices for Austrian Residential Real Estate - Hedonic Models based on Micro-Level Data (with R. J. Hill), FFG (Österr. Forschungsförderungsgesellschaft)
2016 - 2019 Minimizing Longevity and Investment Risk while Optimizing Future Pension Plans. Institution: Institute and Faculty of Actuaries, UK; Chairs: J.P. Nielsen, C. Donnelly, and T. Carstensen.
2012 - 2016 140 000 EUR Research Grant of the Austrian National Bank - Jubiläumsfondprojekt 14947: Forecasting House Prices (with R. J. Hill)

Publikationen

  • Kyriakou, Mousavi, Nielsen, and Scholz (2019),  Forecasting Benchmarks of Long-Term Stock Returns via Machine Learning, Annals of Operations Research.
  • Hill, Scholz, Shimizu, and Steurer (2018), An Evaluation of the Hedonic Methods Used by European Countries to Compute their Official House Price Indices, Economie et Statistique, 500-501-502, 221–238.
  • Hill and Scholz (2018), Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines, Review of Income and Wealth , 64(4), 737–756.
  • Guevara, Hill, and Scholz (2017), Hedonic Indexes for Public and Private Housing in Costa Rica: Prices, Quality and Government Policy, International Journal of Housing Markets and Analysis, 10(1), 140–155.
  • Scholz, Sperlich, and Nielsen (2016), Nonparametric Long-Term Prediction of Stock Returns With Generated Bond Yields, Insurance: Mathematics and Economics, 69, 82–96.
  • Scholz, Nielsen, and Sperlich (2015), Nonparametric Prediction of Stock Returns Based on Yearly Data. The Long Term View, Insurance: Mathematics and Economics, 65, 143–155.
  • Hill and Scholz (2015), What Does “Location, Location, Location” Mean In The Context Of House Price Indexes, Proceedings of the OeNB Workshop Nr. 19
  • Scholz (2011), Semiparametric Structure Guided by Prior Knowledge with Applications in Economics, Göttingen, Univ., Diss.
  • Ferger and Scholz (2011), Limit Distributions of V- and U- Statistics in Terms of Multiple Stochastic Wiener-Type Integrals, Journal of Multivariate Analysis, 102(2), 306-314.
  • Pendakur, Scholz, and Sperlich (2010), Semiparametric Indirect Utility and Consumer Demand, Computational Statistics and Data Analysis, 54, 2763-2775.
  • Scholz (2010), Verteilungskonvergenz für V- und U-Statistiken basierend auf multiplen stochastischen Integralen vom Wienertyp, Dresdner Schriften zur Mathematischen Stochastik 2/2010.

Submissions / Working Papers / Work in Progress

  • (With J. de Haan and R. Hendriks) A Comparison of Weighted Time-Product Dummy and Time Dummy Hedonic Indexes (submitted).
  • (With R. J. Hill and A. Rambaldi) Weekly Hedonic House Price Indexes: An Imputation Approach with Geospatial Splines and Kalman Filters (submitted).
  • (Witjh J. P. Nielsen, I. Kyriakou, und P. Mousavi)  Machine Learning for Forecasting Excess Stock Returns – The Five-Year View (working paper).
  • (With M. Wagner) Large Initial Values and Time Series Tests of the Convergence Hypothesis (working paper).
  • (With R. J. Hill and Ch. Shimizu) Weekly Hedonic House Price Indices and the Rolling Time Dummy Method: An Application to Sydney and Tokyo (working paper).
  • (With R. J. Hill und A. Rambaldi) Combining House Price Indices in Temporal Hierarchies (working paper).
  • (With S. Sperlich, J. P. Nielsen, and E. Mammen) Market-Timing in Practice (working paper)
  • Price-Rent Ratios and Expected Capital Gains – A Hedonic Spatio-Temporal Approach (working paper).
  • (With R. J. Hill and A. Rambaldi) Real-Time Annualized Indices of Capital Asset Prices.
  • (With H. Friedl, R. J. Hill und S. Peterl) Smooth Spatial and Time Effect Models to Forecast Price to Rent Ratios in Sydney.
  • (With H. Manner) Forecasting in Hedonic Automated Valuation Models with Machine Learning Algorithms and Semi-Parametric Regressions.
  • (With A. Hansak , I. Kyriakou, G. Sermpinis) Nonparametric Predictive Regression for the Equity Risk Premium with Technical Indicators.
  • A Nonparametrically Improved Sharpe Ratio Guided by Prior Knowledge.
  • A local-polynomial Chow-type test.
  • Bias Reduction in Nonparametric Estimation in Presence of both Categorical and Continuous Data.
  • A Note on: “The Millenium Peak in Club Convergence: A New Look at Distributional Changes in the Wealth of Nations” by Krause (2016, J. Appl. Econ.)