Jahrbücher für Nationalökonomie und Statistik

Open Access Published by De Gruyter Oldenbourg

Volume 238 Issue 3-4 - Special Issue: Big Data

Issue of Jahrbücher für Nationalökonomie und Statistik

Contents
Journal Overview

Publicly Available July 5, 2018

Editorial

July 4, 2018

Introduction

Einführung

Harry J. Paarsch, Karl Schmedders

Page range: 183-187

Abstract

By making gathering large samples of data (Big Data) almost trivial, the Information Revolution has changed fundamentally how many scientists now conduct empirical research. The explosion in the variety and volume of information that is Big Data has in many cases altered both the questions asked and how those questions are answered. In this special issue devoted to Big Data, we have collected five papers from the social sciences, particularly economics, but business as well. The main goal of the issue is to introduce economists to the different ways that Big Data can and have been used in business and economic research, in the hope that this will spur additional innovative research in those fields.

Original Articles

July 4, 2018

Studying Firm Growth Distributions with a Large Administrative Employment Database

Jay Dixon, Robert Petrunia, Anne-Marie Rollin

Page range: 189-221

Abstract

This paper uses business tax administrative data to describe the annual firm growth rate distribution in Canada over the 2000–2009 period. This administrative tax database provides a unique lense to study firm growth as it allows us to look at the universe of Canadian employer firms and investigate the firm growth distribution across different dimensions. A non-normal, fat-tailed shape for the firm growth distributions holds across years, industries, regions, as well as firm size and age classes. The results show that the distributions of employment growth rates in Canada have more density in both the center and tails than a normal distribution. The evidence paints a picture of firm growth dynamics whereby most firms change very little each year, while a nontrivial amount also markedly grow or decline. A final finding is that young firms, aged four or less, represent a special case with an upwardly skewed distribution and a median growth rate greater than zero.

February 13, 2018

Randomization in Online Experiments

Konstantin Golyaev

Page range: 223-241

Abstract

Most scientists consider randomized experiments to be the best method available to establish causality. On the Internet, during the past twenty-five years, randomized experiments have become common, often referred to as A/B testing. For practical reasons, much A/B testing does not use pseudo-random number generators to implement randomization. Instead, hash functions are used to transform the distribution of identifiers of experimental units into a uniform distribution. Using two large, industry data sets, I demonstrate that the success of hash-based quasi-randomization strategies depends greatly on the hash function used: MD5 yielded good results, while SHA512 yielded less impressive ones.

July 3, 2018

OLS and 2SLS in Randomized and Conditionally Randomized Experiments

Jason Ansel, Han Hong, and Jessie Li

Page range: 243-293

Abstract

We investigate estimation and inference of the (local) average treatment effect parameter when a binary instrumental variable is generated by a randomized or conditionally randomized experiment. Under i.i.d. sampling, we show that adding covariates and their interactions with the instrument will weakly improve estimation precision of the (local) average treatment effect, but the robust OLS (2SLS) standard errors will no longer be valid. We provide an analytic correction that is easy to implement and demonstrate through Monte Carlo simulations and an empirical application the interacted estimator’s efficiency gains over the unadjusted estimator and the uninteracted covariate adjusted estimator. We also generalize our results to covariate adaptive randomization where the treatment assignment is not i.i.d., thus extending the recent contributions of Bugni, F., I.A. Canay, A.M. Shaikh (2017a), Inference Under Covariate-Adaptive Randomization. Working Paper and Bugni, F., I.A. Canay, A.M. Shaikh (2017b), Inference Under Covariate-Adaptive Randomization with Multiple Treatments. Working Paper to allow for the case of non-compliance.

July 3, 2018

Strategic Timing of Investment over the Business Cycle: Machine Replacement in the US Rental Industry

John McClelland, John Rust

Page range: 295-351

Abstract

We analyze a data set containing rental revenues, maintenance costs, and sale prices of five different types of rental machines to econometrically estimate key relationships needed to implement a dynamic programming model of the optimal timing of replacement of rental equipment owned by a large multi-location firm in the equipment rental industry. The model reveals significant potential to improve rental company profitability by improving the strategic timing of equipment replacement. The gains from the optimal replacement strategy come from exploiting seasonal variation in rental demand and the timing of the business cycle due to their effects on rental revenues and the cost of replacement. For some machines we find the optimal replacement strategy is procyclical, but for others we find that a countercyclical replacement strategy –- where replacements are concentrated in slow periods of the business cycle –- can significantly increase firm profits.

June 27, 2018

Tail Risk in a Retail Payments System

Leonard Sabetti, David T. Jacho-Chávez, Robert Petrunia, Marcel C. Voia

Page range: 353-369

Abstract

In this paper, we study a credit risk (collateral) management scheme for the Canadian retail payment system designed to cover the exposure of a defaulting member. We estimate ex ante the size of a collateral pool large enough to cover exposure for a historical worst-case default scenario. The parameters of the distribution of the maxima are estimated using two main statistical approaches based on extreme value models: Block-Maxima for different window lengths (daily, weekly and monthly) and Peak-over-Threshold. Our statistical model implies that the largest daily net debit position across participants exceeds roughly $1.5 billion once a year. Despite relying on extreme-value theory, the out of sample forecasts may still underestimate an actual exposure given the absence of observed data on defaults and financial stress in Canada. Our results are informative for optimal collateral management and system design of pre-funded retail-payment schemes.

About this journal

The Journal of Economics and Statistics (Jahrbücher für Nationalökonomie und Statistik) is a scientific journal published in Germany since 1863. The journal publishes papers in all fields of economics and applied statistics. A specific focus is on papers combining theory with empirical analyses. Papers providing conclusions for economic policy in Europe are particularly welcome. Nevertheless, distinguished papers dealing exlusively with economic theory, empirical models, or economic history, will not be excluded from consideration.

The editors invite replication studies focusing on the robustness of empirical findings. The section Data Observer presents articles on data sets available for empirical research and institutions providing research data. The journal also publishes special issues, short comments, and book reviews. All freely accessible contributions, including the Data Observer articles, can be found via the website's article search functionality.

Journal of Economics and Statistics now also at Twitter.

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Starting 2023, the Journal of Economics and Statistics will be transferred to Diamond Open Access on a year-by-year basis. All articles will thus immediately appear under the Creative Commons license CC-BY. There will be no publication costs for the authors.
The Open Access transformation is based on Subscribe-to-Open, an alternative model that enables the full Open Access transformation of journals through the continuation of existing subscriptions. The prerequisite for successful transformation is that subscriptions are continued to the same extent as before. The editors of the Journal of Economics and Statistics and the publisher De Gruyter would therefore like to thank all subscribers for their support, which has made the transformation to Open Access possible.

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Call for Papers

Special Issue on "Sports Economics"

Guest Editors: Bernd Frick, Paderborn University; Thomas Peeters, Erasmus University Rotterdam; Rob Simmons, Lancaster University

Timeline: Submission of full papers November 30, 2023

Further details

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Special Issue on "Advancing Agent-Based Economics"

Guest Editors: Michael Neugart, Technical University of Darmstadt) and Frank Westerhoff, University of Bamberg

Timeline: Submission of full papers December 31, 2023

Further details

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Special Issue on "Central Bank Digital Currency"

Guest Editors: Katrin Assenmacher, European Central Bank and Michael Frenkel, WHU

Timeline: Submission of full papers January 08, 2024

Further details

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Volume 238 Issue 3-4 - Special Issue: Big Data

Issue of Jahrbücher für Nationalökonomie und Statistik

Frontmatter

Introduction

Einführung

Abstract

Studying Firm Growth Distributions with a Large Administrative Employment Database

Abstract

Randomization in Online Experiments

Abstract

OLS and 2SLS in Randomized and Conditionally Randomized Experiments

Abstract

Strategic Timing of Investment over the Business Cycle: Machine Replacement in the US Rental Industry

Abstract

Tail Risk in a Retail Payments System

Abstract