My name is Christian Leschinski and I am a data scientist at SPRING Media, where I mostly work on use cases in programmatic advertising. Before that I was an assistant professor at the University of Hannover in Germany, where I spent my time with time series econometrics – especially with long memory and structural change models.

I love to learn new things and I love to share what I learn. At firstdifferences I write about things that I came across and want to share.

If you agree or disagree with the views expressed here, or if you feel the need to comment in any other way, feel free to get in touch.

Here you can find me on LinkedIn, Twitter and Facebook.

Publications

• Kruse, R., Leschinski, C., and Will, M. (2019): Comparing Predictive Accuracy under Long Memory, With an Application to Volatility Forecasting. Journal of Financial Econometrics,  17 (2), pp. 180 – 228.
• Leschinski, C. (2017): On the memory of products of long range dependent time series. Economics Letters, 153: 72 – 76.
• Leschinski, C. and Bertram, P. (2017): Time varying contagion in EMU government bond spreads. Journal of Financial Stability, 29: 72 – 91.
• Leschinski, C. and Sibbertsen, P. (2019): Model order selection in periodic long memory models. Econometrics and Statistics, 1: 78-94.
• Sibbertsen, P., Leschinski, C., and Busch, M. (2018): A multivariate test against spurious long memory. Journal of Econometrics, 203(1): 33 – 49.
• Voges, M., Leschinski, C., and Sibbertsen, P. (2019): Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks. In: Chevallier, J., editor, Routledge Advances in Applied Financial Econometrics, Vol. 1, International Financial Markets, Routledge, Oxon.
• Wenger, K., Leschinski, C.: Fixed-bandwidth CUSUM tests under long memory. Econometrics and Statistics, (forthcoming).
• Wenger, K., Leschinski, C., and Sibbertsen, P. (2019): Change-in-mean tests in long-memory time series: A review of recent developments. Advances in Statistical Analysis, 103: 237 – 256.
• Wenger, K., Leschinski, C., and Sibbertsen, P. (2018b): The memory of volatility. Quantitative Finance and Economics, 2(1): 622 – 644.
• Wenger, K., Leschinski, C., and Sibbertsen, P. (2018c): A simple test on structural change in long-memory time series. Economics Letters, 163: 90 – 94.
• Wingert, S., Leschinski, C., and Sibbertsen, P. (2020): Seasonality robust local whittle estimation. Applied Economics Letters, (forthcoming).

Working Papers

• Becker, J. and Leschinski, C. (2018a): Directional predictability of daily stock returns. Technical Report 624, University of Hannover.
• Becker, J. and Leschinski, C. (2018b): Estimating the volatility of asset pricing factors. Technical Report 631, University of Hannover.
• Becker, J. and Leschinski, C. (2018c): The bias of realized volatility. Technical Report 642, University of Hannover.
• Leschinski, C. and Sibbertsen, P. (2018): The periodogram of spurious long-memory processes. Technical Report 632, University of Hannover.
• Leschinski, C., Voges, M. and Sibbertsen, P. (2019): A Comparison of Semiparametric Tests for Fractional Cointegration, Technical Report 651, University of Hannover.
• Leschinski, C., Voges, M., and Sibbertsen, P. (2018): Integration and disintegration of EMU government bond markets. Technical Report 625, University of Hannover.
• Rinke, S., Busch, M., and Leschinski, C. (2017): Long memory, breaks, and trends: On the sources of persistence in inflation rates. Technical Report 584, University of Hannover.

R Packages

• Leschinski, C. (2017): MonteCarlo – R package for simulation studies.
• Leschinski, C. (2019): LongMemoryTS – a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems. Available on CRAN.