Cross-Border Mergers & Acquisitions Policy in Service Markets

We provide facts showing that in service markets: (i) restrictions on foreign direct investment (FDI) are under reform, (ii) cross-border Mergers & Acquisitions dominate as the entry mode of FDI, and (iii) there is often a high market concentration. Based on these facts, we present a model for analyzing cross-border merger and acquisition policy in liberalized service markets taking into account efficiency and market power effects. Our findings suggest that a merger policy, but not a discriminatory policy towards foreigners, seems warranted. Moreover, policies ensuring competition for domestic target firms seem warranted. In this vein, harmonization of the EU takeover regulations may particularly benefit assets owners in countries with many target firms.     

A sequential purchasing power parity test for panels of large cross-sections and implications for investors

In this paper we use monthly time series data for not less than 64 countries and a new sequential approach to test for purchasing power parity (PPP). The results are strong in that the evidence in favor of PPP is very weak. In fact, for the US-dollar-based exchange rates the evidence is basically non-existent. In order to eliminate the effect of the base currency, we also apply the sequential PPP test to all pairs of exchange rates, and find similarly weak evidence of PPP. However, for those rates where evidence is found, using a technical trading rule, we find evidence of significant profits. The predictability of the stationary pairs is therefore important for investors.     

A modified LLC panel unit root test of the PPP hypothesis

In a recent study, Westerlund (Empir Econ 37:517–531, 2009) shows that the performance of the popular LLC (Levin et al., J Econ 108:1–24, 2002) panel unit root test depends critically on the choice of lag truncation used when correcting for serial correlation, and that it is only when this parameter is set as a function of time that the power raises above size. The purpose of the current paper is to propose a modified test that does not suffer from this drawback. The new test is not only simpler to compute but also superior in terms of small-sample performance, which is illustrated using an example purchasing power parity for less developed countries.     

The duration of trade revisited

The recent literature on the duration of trade has predominantly analyzed the determinants of trade flow durations using Cox proportional hazards models. The purpose of this article is to show why it is inappropriate to analyze the duration of trade with continuous-time models such as the Cox model, and to propose alternative discrete-time models which are more suitable for estimation. In brief, the Cox model has three major drawbacks when applied to large trade data sets. First, it faces problems in the presence of many tied duration times, leading to biased coefficient estimates and standard errors. Second, it is difficult to properly control for unobserved heterogeneity, which can lead to parameter bias and bias in the estimated survivor function. Third, the Cox model imposes the restrictive and empirically questionable assumption of proportional hazards. In contrast, with discrete-time models there is no problem handling ties; unobserved heterogeneity can be controlled for without difficulty; and the restrictive proportional hazards assumption can easily be bypassed. By replicating an influential study by Besede? and Prusa (J Int Econ 70:339?C358, 2006b), but employing discrete-time models as well as the original Cox model, we find empirical support for each of these arguments against the Cox model. Moreover, when comparing estimation results obtained from a Cox model and our preferred discrete-time specification, we find significant differences in both the predicted survivor functions and the estimated effects of explanatory variables on the hazard. In other words, the choice between models affects the economic conclusions that can be drawn.     

Managing the front-end phase of process innovation under conditions of high uncertainty

Using a fuzzy-set qualitative comparison analysis, we identify and examine key conditions for success in early front-end phases of process innovation projects. We focus on the very initial stage of the innovation process—the front-end phase—and a selection of conditions for managing highly uncertain process innovation projects. In high-uncertainty projects coupled with high levels of equivocality, formalizing roles and processes should be consistently avoided; however, idea screening is beneficial. Moreover, in successful innovation projects formalized processes but not formalized roles are beneficial. The findings suggest that formalizing roles and processes, a success factor in prior studies of innovation, does not result in success of process innovation projects.     

Testing for Predictability in panels with General Predictors

The difficulty of predicting returns has recently motivated researchers to start looking for tests that are either more powerful or robust to more features of the data. Unfortunately, the way that these tests work typically involves trading robustness for power or vice versa. The current paper takes this as its starting point to develop a new panel‐based approach to predictability that is both robust and powerful. Specifically, while the panel route to increased power is not new, the way in which the cross‐section variation is exploited also to achieve robustness with respect to the predictor is. The result is two new tests that enable asymptotically standard normal and chi‐squared inference across a wide range of empirically relevant scenarios in which the predictor may be stationary, moderately non‐stationary, nearly non‐stationary, or indeed unit root non‐stationary. The type of cross‐section dependence that can be permitted in the predictor is also very general, and can be weak or strong, although we do require that the cross‐section dependence in the regression errors is of the strong form. What is more, this generality comes at no cost in terms of complicated test construction. The new tests are therefore very user‐friendly. Copyright © 2016 John Wiley & Sons, Ltd.     

CCE in fixed‐T panels

The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor‐augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross‐section averages of the observables, and then applying ordinary least squares conditional on the first‐step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross‐section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large‐T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously.     

Sickness Absence and Local Benefit Cultures

In many countries, sickness absence financed by generous insurance benefits is an important concern in the policy debate. There are strong variations in absence behavior among local geographical areas. Such variations are difficult to explain in terms of observable socioeconomic factors. In this paper, we investigate whether such variations are related to group effects in the form of social interaction among individuals within neighborhoods. Well‐known methodological problems arise when trying to answer this question. A special feature of our efforts to deal with these problems is that we adopt several alternative approaches to identify group effects. Our study is based on a rich set of Swedish panel data, and we find indications of group effects in each of our approaches.