This document introduces the R library estimateW to estimate spatial weight matrices for Bayesian spatial econometric panel models. The approach focuses on spatial weights that are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori...

Selecting the relevant covariates in high-dimensional panel data remains a central challenge in applied econometrics. Conventional fixed effects and random effects models are not designed for systematic variable selection under model uncertainty. In addition, many existing models such as LASSO...

This paper provides novel empirical evidence on the impact of monetary policy on innovation investment using unique firm-level data. First, we document the ef- fect of a large, systematic monetary tightening (ECB rate increases from 0% to 4.5% during 2022-23), with average firm-level innovation...

This paper provides novel empirical evidence on the impact of monetary policy on innovation investment using unique firm-level data. First, we document the effect of a large, systematic monetary tightening (ECB rate increases from 0% to 4.5% during 2022-23), with average firm-level innovation...

This paper provides novel empirical evidence on the impact of monetary policy on innovation investment using unique firm-level data. First, we document the effect of a large, systematic monetary tightening (ECB rate increases from 0% to 4.5% during 2022-23), with average firm-level innovation...

Determining potential output and the output gap-two inherently unobservable variables-is a major challenge for macroeconomists. This paper presents the R package sectorgap, which features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying...

The rapid development of artificial intelligence (AI) has significantly transformed digital marketing enhancing its effectiveness and raising new ethical and privacy concerns. This study investigates the ethical implications of AI-based digital marketing, particularly focusing on user privacy....

Researchers may want to know whether an observed statistical relationship is either meaningfully negative, meaningfully positive, or small enough to be considered practically equivalent to zero. Such a question can not be addressed with standard null hypothesis significance testing, nor with...

Researchers may want to know whether an observed statistical relationship is either meaningfully negative, meaningfully positive, or small enough to be considered practically equivalent to zero. Such a question can not be addressed with standard null hypothesis significance testing, nor with...

This reprint concerns methods of data analysis for risk management in economics, finance, and business. The presented papers contain research on data analysis methods, including classical statistical methods, and machine learning methods that have emerged from statistics and are being...

Determining potential output and the output gap-two inherently unobservable variables-is a major challenge for macroeconomists. This paper presents the R package sectorgap, which features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying...

The rapid development of artificial intelligence (AI) has significantly transformed digital marketing enhancing its effectiveness and raising new ethical and privacy concerns. This study investigates the ethical implications of AI-based digital marketing, particularly focusing on user privacy....

Multimodal empirical distributions arise in many fields like Astrophysics, Bioinformatics, Climatology and Economics due to the heterogeneity of the underlying populations. Mixture processes are a popular tool for accurate approximation of such distributions and implied mode detection. Using...

Protean and boundaryless careers have emerged as new ways of handling careers in organizations. This concept is gaining a significant place in career studies. In line with this, the current study examines the relationship between the underlying dimensions of protean (self-direction and...

Multimodal empirical distributions arise in many fields like Astrophysics, Bioinformatics, Climatology and Economics due to the heterogeneity of the underlying populations. Mixture processes are a popular tool for accurate approximation of such distributions and implied mode detection. Using...

Protean and boundaryless careers have emerged as new ways of handling careers in organizations. This concept is gaining a significant place in career studies. In line with this, the current study examines the relationship between the underlying dimensions of protean (self-direction and...

Chapter 1. Statistics and Empirical Research -- Chapter 2. From Disarray to Dataset -- Chapter 3. Univariate Data Analysis -- Chapter 4. Bivariate Association -- Chapter 5. Classical Measurement Theory -- Chapter 6. Calculating Probability -- Chapter 7. Random Variables and Probability...

This book will take readers from foundational concepts to practical applications, enabling them to transform raw data into meaningful insights. It covers key skills such as data collection, cleaning, organization, exploration, analysis, and impactful presentation core competencies for navigating...

Einführung -- Daten bearbeiten und strukturieren mit tidyverse -- Daten visualisieren mit ggplot -- Weiterführende Visualisierungen mit ggplot, gganimate, stargazer und sf für Geodaten -- Einführung in simulationsbasierte Inferenzstatistik und "Machine Learning" mit R -- Dokumentation und...

Introduction and Excel Basics -- Blockchain (I): Numbers and Alphanumeric -- Blockchain (II): ASCII and Sha256 -- Ledge and Distributed Ledger -- Cryptography Basics: public and private keys -- Advanced Cryptography -- Cryptocurrency and Ganache -- Crypto markets, risk and risk-return tradeoff...

Choice under Uncertainty -- Modern Portfolio Theory -- The Capital Asset Pricing Model -- Empirical Analysis of the CAPM -- The Consumption CAPM -- Arbitrage Pricing Theory and Multi-factor Models -- Empirical Cross-Sectional Asset Pricing -- The Black-Litterman Model -- Event-Study Analysis.

The neutral real rate of interest (r*) is a key variable for assessing the tightness of monetary policy. The neutral real interest rate has by all accounts fallen substantially over the past three decades, amid slowing productivity growth, an ageing population, increased inequality and increased...

The credit scoring industry has a long tradition of using statistical models for loan default probability prediction. Since this time methodology has strongly evolved, and most of the current research is dedicated to modern machine learning algorithms which contrasts with common practice in the...

New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great...

The credit scoring industry has a long tradition of using statistical models for loan default probability prediction. Since this time methodology has strongly evolved, and most of the current research is dedicated to modern machine learning algorithms which contrasts with common practice in the...

New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great...

The neutral real rate of interest (r*) is a key variable for assessing the tightness of monetary policy. The neutral real interest rate has by all accounts fallen substantially over the past three decades, amid slowing productivity growth, an ageing population, increased inequality and increased...

Benefits of Machine Learning and Artificial Intelligence -- Machine Learning -- Best Practices -- Setting Up the R Development Environment -- Fundamentals of the R Programming Language -- Machine Learning with R -- Application of Machine Learning with R -- Outlook.