Early-Stage Business Formation: An Analysis of Applications for Employer Identification Numbers
Kimberly Bayard, Emin Dinlersoz, Timothy Dunne, John Haltiwanger, Javier Miranda, John Stevens
NBER Working Paper,
Nr. 24364,
2018
Abstract
This paper reports on the development and analysis of a newly constructed dataset on the early stages of business formation. The data are based on applications for Employer Identification Numbers (EINs) submitted in the United States, known as IRS Form SS-4 filings. The goal of the research is to develop high-frequency indicators of business formation at the national, state, and local levels. The analysis indicates that EIN applications provide forward-looking and very timely information on business formation. The signal of business formation provided by counts of applications is improved by using the characteristics of the applications to model the likelihood that applicants become employer businesses. The results also suggest that EIN applications are related to economic activity at the local level. For example, application activity is higher in counties that experienced higher employment growth since the end of the Great Recession, and application counts grew more rapidly in counties engaged in shale oil and gas extraction. Finally, the paper provides a description of new public-use dataset, the “Business Formation Statistics (BFS),” that contains new data series on business applications and formation. The initial release of the BFS shows that the number of business applications in the 3rd quarter of 2017 that have relatively high likelihood of becoming job creators is still far below pre-Great Recession levels.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Do We Need New Modelling Approaches in Macroeconomics?
Claudia M. Buch, Oliver Holtemöller
Financial Cycles and the Real Economy: Lessons for CESEE Countries,
2014
Abstract
The economic and financial crisis that emerged in 2008 also initiated an intense discussion on macroeconomic research and the role of economists in society. The debate focuses on three main issues. Firstly, it is argued that economists failed to predict the crisis and to design early warning systems. Secondly, it is claimed that economists use models of the macroeconomy which fail to integrate financial markets and which are inadequate to model large economic crises. Thirdly, the issue has been raised that economists invoke unrealistic assumptions concerning human behaviour by assuming that all agents are self-centred, rationally optimizing individuals. In this paper, we focus on the first two issues. Overall, our thrust is that the above statements are a caricature of modern economic theory and empirics. A rich field of research developed already before the crisis and picked up shortcomings of previous models.
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Do We Need New Modelling Approaches in Macroeconomics?
Claudia M. Buch, Oliver Holtemöller
IWH Discussion Papers,
Nr. 8,
2014
Abstract
The economic and financial crisis that emerged in 2008 also initiated an intense discussion on macroeconomic research and the role of economists in society. The debate focuses on three main issues. Firstly, it is argued that economists failed to predict the crisis and to design early warning systems. Secondly, it is claimed that economists use models of the macroeconomy which fail to integrate financial markets and which are inadequate to model large economic crises. Thirdly, the issue has been raised that economists invoke unrealistic assumptions concerning human behaviour by assuming that all agents are self-centred, rationally optimizing individuals. In this paper, we focus on the first two issues. Overall, our thrust is that the above statements are a caricature of modern economic theory and empirics. A rich field of research developed already before the crisis and picked up shortcomings of previous models.
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The Impact of Preferences on Early Warning Systems - The Case of the European Commission's Scoreboard
Tobias Knedlik
European Journal of Political Economy,
2014
Abstract
The European Commission’s Scoreboard of Macroeconomic Imbalances is a rare case of a publicly released early warning system. It allows the preferences of the politicians involved to be analysed with regard to the two potential errors of an early warning system – missing a crisis and issuing a false alarm. These preferences might differ with the institutional setting. Such an analysis is done for the first time in this article for early warning systems in general by using a standard signals approach, including a preference-based optimisation approach, to set thresholds. It is shown that, in general, the thresholds of the Commission’s Scoreboard are set low (resulting in more alarm signals), as compared to a neutral stand. Based on political economy considerations the result could have been expected.
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Modelling Macroeconomic Risk: The Genesis of the European Debt Crisis
Gregor von Schweinitz
Hochschulschrift, Juristische und Wirtschaftswissenschaftliche Fakultät der Martin-Luther-Universität Halle-Wittenberg,
2013
Abstract
Diverging European sovereign bond yields after 2008 are the most visible sign of the European debt crisis. This dissertation examines in a first step, to which extent the development of yields is driven by credit and liquidity risk, and how it is influenced by general uncertainty on financial markets. It can be shown that yields are driven to a significant degree by a flight towards bonds of high liquidity in times of high market uncertainty. In a second step, high yields are interpreted as a sign of an existing crisis in the respective country. Using the signals approach, the early-warning capabilities of four different proposals for the design of the scoreboard as part of the “Macroeconomic Imbalances Procedure” (introduced in December 2011 by the European Commission) are tested, advocating a scoreboard including a variety of many different indicators. In a third step, the methodology of the signals approach is extended to include also results on significance.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
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The European Commission’s Scoreboard of Macroeconomic Imbalances – The impact of preferences on an early warning system
Tobias Knedlik
Externe Publikationen,
2013
Abstract
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Predicting Financial Crises: The (Statistical) Significance of the Signals Approach
Makram El-Shagi, Tobias Knedlik, Gregor von Schweinitz
Journal of International Money and Finance,
Nr. 35,
2013
Abstract
The signals approach as an early-warning system has been fairly successful in detecting crises, but it has so far failed to gain popularity in the scientific community because it cannot distinguish between randomly achieved in-sample fit and true predictive power. To overcome this obstacle, we test the null hypothesis of no correlation between indicators and crisis probability in three applications of the signals approach to different crisis types. To that end, we propose bootstraps specifically tailored to the characteristics of the respective datasets. We find (1) that previous applications of the signals approach yield economically meaningful results; (2) that composite indicators aggregating information contained in individual indicators add value to the signals approach; and (3) that indicators which are found to be significant in-sample usually perform similarly well out-of-sample.
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