HIP, RIP, and the Robustness of Empirical Earnings Processes
Florian Hoffmann
Quantitative Economics,
Nr. 3,
2019
Abstract
The dispersion of individual returns to experience, often referred to as heterogeneity of income profiles (HIP), is a key parameter in empirical human capital models, in studies of life‐cycle income inequality, and in heterogeneous agent models of life‐cycle labor market dynamics. It is commonly estimated from age variation in the covariance structure of earnings. In this study, I show that this approach is invalid and tends to deliver estimates of HIP that are biased upward. The reason is that any age variation in covariance structures can be rationalized by age‐dependent heteroscedasticity in the distribution of earnings shocks. Once one models such age effects flexibly the remaining identifying variation for HIP is the shape of the tails of lag profiles. Credible estimation of HIP thus imposes strong demands on the data since one requires many earnings observations per individual and a low rate of sample attrition. To investigate empirically whether the bias in estimates of HIP from omitting age effects is quantitatively important, I thus rely on administrative data from Germany on quarterly earnings that follow workers from labor market entry until 27 years into their career. To strengthen external validity, I focus my analysis on an education group that displays a covariance structure with qualitatively similar properties like its North American counterpart. I find that a HIP model with age effects in transitory, persistent and permanent shocks fits the covariance structure almost perfectly and delivers small and insignificant estimates for the HIP component. In sharp contrast, once I estimate a standard HIP model without age‐effects the estimated slope heterogeneity increases by a factor of thirteen and becomes highly significant, with a dramatic deterioration of model fit. I reach the same conclusions from estimating the two models on a different covariance structure and from conducting a Monte Carlo analysis, suggesting that my quantitative results are not an artifact of one particular sample.
Artikel Lesen
Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models
Sebastian Giesen, Rolf Scheufele
Journal of Macroeconomics,
June
2016
Abstract
In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameter estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.
Artikel Lesen
Evidence for the Existence of Downward Real-Activity Earnings Management
Bill Francis, Iftekhar Hasan, Lingxiang Li
Journal of Accounting, Auditing and Finance,
Nr. 2,
2016
Abstract
Prior studies of real-activity earnings management (REM) focus on earnings-inflating abnormal activities. We seek to establish the existence of downward REM by investigating several corporate events in which managers have incentives to temporarily deflate market valuations. Specifically, we focus on, and find downward REM before, share repurchases, management buyouts (MBOs), and CEO option awards. Large-sample evidence of downward REM is also found in our general analysis of earnings smoothing. Downward REM becomes much smaller or nonexistent when there is a lack of managerial incentives in those events, such as non-carry-through repurchases, incomplete MBOs, and unexpected option awards. Following the research design of Zang, we find that various REM and accrual-based earnings management (AEM) cost factors consistently influence the magnitude of downward REM and AEM around the three corporate events.
Artikel Lesen
Spinoffs in Germany: Characteristics, Survival, and the Role of their Parents
Daniel Fackler, A. Schmucker, Claus Schnabel
Small Business Economics,
Nr. 1,
2016
Abstract
Using a 50 % sample of all private sector establishments in Germany, we report that spinoffs are larger, initially employ more skilled and more experienced workers, and pay higher wages than other startups. We investigate whether spinoffs are more likely to survive than other startups, and whether spinoff survival depends on the quality and size of their parent companies, as suggested in some of the theoretical and empirical literature. Our estimated survival models confirm that spinoffs are generally less likely to exit than other startups. We also distinguish between pulled spinoffs, where the parent company continues after they are founded, and pushed spinoffs, where the parent company stops operations. Our results indicate that in western and eastern Germany and in all sectors investigated, pulled spinoffs have a higher probability of survival than pushed spinoffs. Concerning the parent connection, we find that intra-industry spinoffs and spinoffs emerging from better-performing or smaller parent companies are generally less likely to exit.
Artikel Lesen
Bank Market Power, Factor Reallocation, and Aggregate Growth
R. Inklaar, Michael Koetter, Felix Noth
Journal of Financial Stability,
2015
Abstract
Using a unique firm-level sample of approximately 700,000 firm-year observations of German small and medium-sized enterprises (SMEs), this study seeks to identify the effect of bank market power on aggregate growth components. We test for a pre-crisis sample whether bank market power spurs or hinders the reallocation of resources across informationally opaque firms. Identification relies on the dependence on external finance in each industry and the regional demarcation of regional banking markets in Germany. The results show that bank markups spur aggregate SME growth, primarily through technical change and the reallocation of resources. Banks seem to need sufficient markups to generate the necessary private information to allocate financial funds efficiently.
Artikel Lesen
Executive Compensation Structure and Credit Spreads
Stefano Colonnello, Giuliano Curatola, Ngoc Giang Hoang
Abstract
We develop a model of managerial compensation structure and asset risk choice. The model provides predictions about how inside debt features affect the relation between credit spreads and compensation components. First, inside debt reduces credit spreads only if it is unsecured. Second, inside debt exerts important indirect effects on the role of equity incentives: When inside debt is large and unsecured, equity incentives increase credit spreads; When inside debt is small or secured, this effect is weakened or reversed. We test our model on a sample of U.S. public firms with traded CDS contracts, finding evidence supportive of our predictions. To alleviate endogeneity concerns, we also show that our results are robust to using an instrumental variable approach.
Artikel Lesen
Estimating Monetary Policy Rules when the Zero Lower Bound on Nominal Interest Rates is Approached
Konstantin Kiesel, M. H. Wolters
Kiel Working Papers, No. 1898,
2014
Abstract
Monetary policy rule parameters estimated with conventional estimation techniques can be severely biased if the estimation sample includes periods of low interest rates. Nominal interest rates cannot be negative, so that censored regression methods like Tobit estimation have to be used to achieve unbiased estimates. We use IV-Tobit regression to estimate monetary policy responses for Japan, the US and the Euro area. The estimation results show that the bias of conventional estimation methods is sizeable for the inflation response parameter, while it is very small for the output gap response and the interest rate smoothing parameter. We demonstrate how IV-Tobit estimation can be used to study how policy responses change when the zero lower bound is approached. Further, we show how one can use the IV-Tobit approach to distinguish between desired policy responses, that the central bank would implement if there was no zero lower bound, and the actual ones and provide estimates of both.
Artikel Lesen
Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models
Sebastian Giesen, Rolf Scheufele
Abstract
In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameters estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.
Artikel Lesen
Testing for Structural Breaks at Unknown Time: A Steeplechase
Makram El-Shagi, Sebastian Giesen
Computational Economics,
Nr. 1,
2013
Abstract
This paper analyzes the role of common data problems when identifying structural breaks in small samples. Most notably, we survey small sample properties of the most commonly applied endogenous break tests developed by Brown et al. (J R Stat Soc B 37:149–163, 1975) and Zeileis (Stat Pap 45(1):123–131, 2004), Nyblom (J Am Stat Assoc 84(405):223–230, 1989) and Hansen (J Policy Model 14(4):517–533, 1992), and Andrews et al. (J Econ 70(1):9–38, 1996). Power and size properties are derived using Monte Carlo simulations. We find that the Nyblom test is on par with the commonly used F type tests in a small sample in terms of power. While the Nyblom test’s power decreases if the structural break occurs close to the margin of the sample, it proves far more robust to nonnormal distributions of the error term that are found to matter strongly in small samples although being irrelevant asymptotically for all tests that are analyzed in this paper.
Artikel Lesen
Distance Functions for Matching in Small Samples
Eva Dettmann, Christian Schmeißer, Claudia Becker
Computational Statistics & Data Analysis,
Nr. 5,
2011
Abstract
The development of ‘standards’ for the application of matching algorithms in empirical evaluation studies is still an outstanding goal. The first step of the matching procedure is the choice of an appropriate distance function. In empirical evaluation situations often the sample sizes are small. Moreover, they consist of variables with different scale levels which have to be considered explicitly in the matching process. A simulation is performed which is directed towards these empirical challenges and supplements former studies in this respect. The choice of the analysed distance functions is determined by the results of former theoretical studies and recommendations in the empirical literature. Thus, two balancing scores (the propensity score and the index score) and the Mahalanobis distance are considered. Additionally, aggregated statistical distance functions not yet used for empirical evaluation are included. The matching outcomes are compared using non-parametric scale-specific tests for identical distributions of the characteristics in the treatment and the control groups. The simulation results show that, in small samples, aggregated statistical distance functions are the better choice for summarising similarities in differently scaled variables compared to the commonly used measures.
Artikel Lesen