David Zenz

WHO I AM

Statistics is the grammar of science

I am an Economist working as a Statistician / Data Scientist and R Shiny Application Developer at The The Vienna Institute for International Economic Studies (wiiw).

My primary interests lie in Time Series Econometrics and Forecasting, Data Science and Data Engineering, working with Large and Big Data, Machine Learning, and R Shiny Application Development. #rstats

In addition to my professional activities, I enjoy drinking espresso, playing music (especially live), running (a lot), cooking (and eating), football (the European one), bouldering, snowboarding, and photography.

MUSIC

In Numbers

250+

Concerts

3

Albums

24

Videos

13

Songs

RUNNING

Personal Bests

2:54:57

Marathon

1:19:53

Half Marathon

36:40

10k

17:32

Featured

Featured Publications

Wrapping input–output multipliers in confidence intervals

Input–output multipliers are typically calculated as point estimates of the Leontief quantity model. From the previous literature, they can also be estimated (and their confidence intervals) directly from establishments’/industries’ inputs and outputs data by running an appropriate econometric regression. However, previous contributions relied on analyses carried out for 1 single year, one country/region and a reduced number of degrees of freedom. This paper tests the robustness of the previous literature by using instead a time series of large-scale inter-country supply, use and input–output database with more than one country and more than 1 year, as well as panel data econometrics.

Die Vernetzung Wiens mit den Städten Europas

Wir stellen ein Maß für die Beziehung zwischen zwei Städten/Regionen basierend auf Suchanfragen
vor, ausgehend von Merkmalen der Suchanfragen-Zeitreihen nach Zerlegung der Zeitreihe mittels Komponentenzerlegung mittels lokaler linearer Kernregression. Grundlage für das Maß sind einerseits
die Eigenschaft ‘Trendstärke’, welches die Stärke des zugrundeliegenden Trends (egal ob steigend oder
fallend) der Zeitreihe beschreibt, sowie das Feature ‘linearity’ der letzten fünf Jahre, welches uns die
Richtung des Trends gibt. Die Kombination aus diesen Features der beiden Richtungen der
Suchanfragen gibt uns ein Maß, welches für die Analyse der Entwicklung des vorgestellten
Beziehungsmaßes über den Beobachtungszeitraum 2004-2020 in unterschiedlichen Suchkategorien
zwischen zwei Städte/Regionen verwendet werden kann

Introducing the wiiw COMECON Dataset

This paper introduces the historical dataset with economic time series of socialist Bulgaria, Czechoslovakia (CSSR), the German Democratic Republic (GDR), Hungary, Poland, Romania, the Soviet Union (USSR) and Yugoslavia from 1944 to 1993 as well as a new dataset on Albania created as part of this project. The paper explains the dataset’s structure and gaps as well as the harmonisation efforts and accounting methodologies adopted in the member countries of the Council of Mutual Economic Assistance (CMEA or COMECON) during that period. The paper serves as a companion for the users of the wiiw COMECON Dataset.