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  • Fritsch, Markus (4)
  • Haupt, Harry (2)
  • Schnurbus, Joachim (2)
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Data for modeling nitrogen dioxide concentration levels across Germany (2021)
Fritsch, Markus
The described secondary data provide a comprehensive basis for modeling conditional mean nitrogen dioxide (NO2) concentration levels across Germany. Besides concentration levels, meta data on monitoring sites from the German air quality monitoring network, geocoordinates, altitudes, and data on land use and road lengths for different types of roads are provided. The data are based on a grid of resolution 1 × 1 km, which is also included. The underlying raw data are open access and were retrieved from different sources. The statistical software R was used for (pre-)processing the data and all codes are provided in an online repository. The data were employed for modeling mean annual NO2 concentration levels in the paper "Agglomeration and infrastructure effects in land use regression models for air pollution - Specification, estimation, and interpretations" by Fritsch and Behm (2021).
Quantile Trend Regression and Its Application to Central England Temperature ()
Haupt, Harry ; Fritsch, Markus
The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-standard distributions of the underlying time series. Quantile regressions are considered an important modeling technique for such analyses because of their rich interpretation and their broad insensitivity to extreme distributions. This paper provides an asymptotic justification of quantile trend regression in terms of unknown heterogeneity and dependence structure and the corresponding interpretation. An empirical application sheds light on the relevance of quantile regression modeling for analyzing monthly Central England temperature anomalies and illustrates their various heterogenous trends. Our results suggest the presence of heterogeneities across the considered seasonal cycle and an increase in the relative frequency of observing unusually high temperatures.
Efficiency of poll-based multi-period forecasting systems for German state elections (2024)
Fritsch, Markus ; Haupt, Harry ; Schnurbus, Joachim
Election polls are frequently employed to reflect voter sentiment with respect to a particular election (or fixed-event). Despite their widespread use as forecasts and inputs for predictive algorithms, there is substantial uncertainty regarding their efficiency. This uncertainty is amplified by judgment in the form of pollsters applying unpublished weighting schemes to ensure the representativeness of the sampled voters for the underlying population. Efficient forecasting systems incorporate past information instantly, which renders a given fixed-event unpredictable based on past information. This results in all sequential adjustments of the fixed-event forecasts across adjacent time periods (or forecast revisions) being martingale differences. This paper illustrates the theoretical conditions related to weak efficiency of fixed-event forecasting systems based on traditional least squares loss and asymmetrically weighted least absolute deviations (or quantile) loss. Weak efficiency of poll-based multi-period forecasting systems for all German federal state elections since the year 2000 is investigated. The inefficiency of almost all considered forecasting systems is documented and alternative explanations for the findings are discussed.
Teaching advanced topics in econometrics using introductory textbooks : the case of dynamic panel data methods (2024)
Fritsch, Markus ; Pua, Andrew Adrian ; Schnurbus, Joachim
We show how to use the introductory econometrics textbook by Stock and Watson (2019) as a starting point for teaching and studying dynamic panel data methods. The materials are intended for undergraduate students taking their second econometrics course, undergraduate students in seminar-type courses, independent study courses, capstone, or thesis projects, and beginning graduate students in a research methods course. First, we distill the methodological core necessary to understand dynamic panel data methods. Second, we design an empirical and a theoretical case study to highlight the capabilities, downsides, and hazards of the method. The empirical case study is based on the cigarette demand example in Stock and Watson (2019) and illustrates that economic and methodological issues are interrelated. The theoretical case study shows how to evaluate current empirical practices from a theoretical standpoint. We designed both case studies to boost students’ confidence in working with technical material and to provide instructors with more opportunities to let students develop econometric thinking and to actively communicate with applied economists. Although we focus on Stock and Watson (2019) and the statistical software R, we also show how to modify the material for use with another introductory textbook by Wooldridge (2020) and Stata, and highlight some possible further pathways for instructors and students to reuse and extend our materials.
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