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We estimate a semiparametric single-risk discrete-time duration model to assess the effect of vocational training on the duration of unemployment spells. The data basis used in this study is the German Socio-Economic-Panel (GSOEP) for West Germany for the period from 1986 to 1994. To take into account a possible selection bias actual participation in vocational training is instrumented using estimates of a randomeffects probit model for the participation in qualification measures. Our main results show that training does have a significant short term effect of reducing unemployment duration but that this effect does not persist in the long run. JEL classifications: C41, J20, J64
On July 4, 2013 the ECB Governing Council provided more specific forward guidance than in the past by stating that it expects ECB interest rates to remain at present or lower levels for an extended period of time. As explained by ECB President Mario Draghi this expectation is based on the Council’s medium-term outlook for inflation conditional on economic activity and money and credit. Draghi also stressed that there is no precise deadline for this extended period of time, but that a reasonable period can be estimated by extracting a reaction function. In this note, we use such a reaction function, namely the interest rate rule from Orphanides and Wieland (2013) that matches past ECB interest rate decisions quite well, to project the rate path consistent with inflation and growth forecasts from the survey of professional forecasters published by the ECB on August 8, 2013. This evaluation suggests an increase in ECB interest rates by May 2014 at the latest. We also use the Eurosystem staff projection from June 6, 2013 for comparison. While it would imply a longer period of low rates, it does not match past ECB decisions as well as the reaction function with SPF forecasts.
In this study, we develop a technique for estimating a firm’s expected cost of equity capital derived from analyst consensus forecasts and stock prices. Building on the work of Gebhardt/Lee/-Swaminathan (2001) and Easton/Taylor/Shroff/Sougiannis (2002), our approach allows daily estimation, using only publicly available information at that date. We then estimate the expected cost of equity capital at the market, industry and individual firm level using historical German data from 1989-2002 and examine firm characteristics which are systematically related to these estimates. Finally, we demonstrate the applicability of the concept in a contemporary case study for DaimlerChrysler and the European automobile industry.
We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
The authors propose a new method to forecast macroeconomic variables that combines two existing approaches to mixed-frequency data in DSGE models. The first existing approach estimates the DSGE model in a quarterly frequency and uses higher frequency auxiliary data only for forecasting. The second method transforms a quarterly state space into a monthly frequency. Their algorithm combines the advantages of these two existing approaches.They compare the new method with the existing methods using simulated data and real-world data. With simulated data, the new method outperforms all other methods, including forecasts from the standard quarterly model. With real world data, incorporating auxiliary variables as in their method substantially decreases forecasting errors for recessions, but casting the model in a monthly frequency delivers better forecasts in normal times.
Causality is a widely-used concept in theoretical and empirical economics. The recent financial economics literature has used Granger causality to detect the presence of contemporaneous links between financial institutions and, in turn, to obtain a network structure. Subsequent studies combined the estimated networks with traditional pricing or risk measurement models to improve their fit to empirical data. In this paper, we provide two contributions: we show how to use a linear factor model as a device for estimating a combination of several networks that monitor the links across variables from different viewpoints; and we demonstrate that Granger causality should be combined with quantile-based causality when the focus is on risk propagation. The empirical evidence supports the latter claim.
Effort estimates are of utmost economic importance in software development projects. Estimates bridge the gap between managers and the invisible and almost artistic domain of developers. They give a means to managers to track and control projects. Consequently, numerous estimation approaches have been developed over the past decades, starting with Allan Albrecht's Function Point Analysis in the late 1970s. However, this work neither tries to develop just another estimation approach, nor focuses on improving accuracy of existing techniques. Instead of characterizing software development as a technological problem, this work understands software development as a sociological challenge. Consequently, this work focuses on the question, what happens when developers are confronted with estimates representing the major instrument of management control? Do estimates influence developers, or are they unaffected? Is it irrational to expect that developers start to communicate and discuss estimates, conform to them, work strategically, hide progress or delay? This study shows that it is inappropriate to assume an independency of estimated and actual development effort. A theory is developed and tested, that explains how developers and managers influence the relationship between estimated and actual development effort. The theory therefore elaborates the phenomenon of estimation fulfillment.
Markets are central to modern society, so their failures can have devastating effects. Here, we examine a prominent failure: price bubbles. We propose that bubbles are affected by ethnic homogeneity in the market and can be thwarted by diversity. Using experimental markets in Southeast Asia and North America, we find a marked difference: Market prices fit true values 58% better in diverse markets. In homogenous markets, overpricing is higher and traders’ errors are more correlated than in diverse markets. The findings suggest that price bubbles arise not only from individual errors or financial conditions, but also from the social context of decision making. Informing public discussion, our findings suggest that diversity facilitates friction that enhances deliberation and upends conformity.
This working paper suggests to analyse agencification as a double process of institutional and policy centralisation. To that end, it develops a categorisation of agencies that incorporates these two dimensions. More specifically, it is argued that mixed outcomes where the levels of institutional and policy centralisation diverge can be expected to be the rule rather than the exception, in line with the hybrid nature of EU agencies as inbetweeners. Moreover, the fiduciary setting hits important legal constraints given the limits to delegation in the EU context. Against this backdrop a process whereby institutional centralisation develops incrementally and remains limited, yet is accompanied by a process of substantial policy centralisation, appears as the most promising path for EU agencification. A fiduciary setting, where a strong agency enjoys a high degree of independence and operates in a centralised policy space, by contrast, should be the exception. The comparative study of the process of agencification in the energy and banking sector is insightful in the light of these expectations. The incremental nature of institutional change in energy exemplifies the usual path of agencification, which is conducive to a weak agency operating in a relatively centralised policy space. Agencification in banking, by contrast, has led to a rather unusual outcome where the strong agency model combines with a fragmented policy context.