MiFID II Unbundling and Sell Side Analyst Research

We examine the effect of MiFID II, which mandated the unbundling and separate pricing of analyst research in Europe beginning in 2018. We find that the requirements of MiFID II were associated with a reduction in analyst following for European firms relative to US firms, with decreases in coverage greatest for firms that were larger, older and less volatile, and had greater coverage and more accurate consensus forecasts. Remaining analysts follow fewer firms and issue fewer forecasts, consistent with increased focus, and appear to increase their efforts on the firms they continue to cover. In particular, forecasts become more accurate, are more likely to be disaggregated and include recommendations, and are accompanied by larger stock price reactions. Consistent with increased effort to curry favor with management, analysts issue more optimistic recommendations and beatable earnings forecasts. While individual forecasts are more informative, the overall information environment for the average firm tends to deteriorate, with less aggregate information conveyed by analyst forecasts, a greater proportion of information delayed to earnings announcements and higher average bid-ask spreads. Taken as a whole, results are consistent with a reduction in analyst following mitigated by an increase in focus and effort by remaining analysts, but with an overall negative effect on the information environment

Semiparametric Fixed-Effects Estimator

This paper describes the Stata implementation of Baltagi and Li's (2002) series estimator of partially linear panel data models with fixed effects. After a brief description of the estimator itself, we describe the new command xtsemipar. We then simulate data to show that this estimator performs better than a fixed effect estimator if the relationship between two variables is unknown or quite complex.xtsemipar, Semiparametric estimations

The fixed effects estimator of technical efficiency

Firms and organizations, public or private, often operate on markets characterized by non-competitiveness. For example agricultural activities in the western world are heavily subsidized and electricity is supplied by firms with market power. In general it is probably more difficult to find firms that act on highly competitive markets, than firms that are not. To measure different types of inefficiencies, due to this lack of competitiveness, has been an ongoing issue, since at least the 1950s when several definitions of inefficiency was proposed and since the late 1970s as stochastic frontier analysis. In all three articles presented in this thesis the stochastic frontier analysis approach is considered. Furthermore, in all three articles focus is on technical inefficiency. The ways to estimate technical inefficiency, based on stochastic frontier models, are numerous. However, focus in this thesis is on fixed effects panel data estimators. This is mainly for two reasons. First, the fixed effects analysis does not demand explicit distributional assumptions of the inefficiency and the random error of the model. Secondly, the analysis does not require the random effects assumption of independence between the firm specific inefficiency and the inputs selected by the very same firm. These two properties are exclusive for fixed effects estimation, compared to other stochastic frontier estimators. There are of course flaws attached to fixed effects analysis as well, and the contribution of this thesis is to probe some of these flaws, and to propose improvements and tools to identify the worst case scenarios. For example the fixed effects estimator is seriously upward biased in some cases, i.e. inefficiency is overestimated. This could lead to false conclusions, like e.g. that subsidies in agriculture lead to severely inefficient farmers even if these farmers in reality are quite homogenous. In this thesis estimators to reduce bias as well as mean square error are proposed and statistical diagnostics are designed to identify worst case scenarios for the fixed effects estimator as well as for other estimators. The findings can serve as important tools for the applied researcher, to obtain better approximations of technical inefficiency

Understanding fixed effects in human well-being

In studies of subjective well-being, economists and other researchers typically use a fixed or random effect estimation to control for unobservable heterogeneity across individuals. Such individual heterogeneity, although substantially reducing the estimated effect of many characteristics, is little understood. This paper shows that personality measures can account for 20% of this heterogeneity and a further 13% can be accounted for by other observable between-person information. This paper then demonstrates that the use of personality measures, in a new technique developed by [Plumper, T., Troeger, V.E. (2007). Efficient estimation of time-invariant and rarely changing variables in finite sample panel analyses with unit fixed effects, Political Analysis, 15(2), 124-139.], can help researchers obtain improved estimates for important characteristics such as marital status, disability and income. The paper argues that this has important practical implications

Accounting for Unobserved Country Heterogeneity in Happiness Research: Country Fixed Effects versus Region Fixed Effects

Many empirical studies are ambiguous about whether good formal institutions are conducive to subjective well-being or not. Possibly, this ambiguity is caused by cross-section models that do not account for unobserved cultural and institutional effects. Using the World Value Survey 1980-2005, this paper supports a positive relation in a country panel framework that accounts for unobserved, time-invariant country heterogeneity. This study also shows that using supra-national region dummies (by geography or language) in a country-random effects model appears to be a sufficient substitution for omitted country fixed effects.Happiness; life satisfaction; subjective well-being; quality of life; institutions; democracy; rule of law; political constraints; policy implications; panel econometrics

Spatial Fixed Effects and Spatial Dependence

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification re- moves spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effects may be spurious when the true DGP takes the form of a spatial lag or spatial error dependence. In addition, we also show that only in the special case where the dependence is group-wise, with all observations in the same group as neighbors of each other, do spatial fixed effects correctly remove spatial correlation.spatial autocorrelation, spatial econometrics, spatial externalities, spatial fixed effects, spatial interaction, spatial weights

HAC estimation in spatial panels

© 2012 Elsevier B.V. All rights reservedWe propose a HAC estimator for the covariance matrix of the fixed effects estimator in a panel data model with unobserved fixed effects and errors that are both serially and spatially correlated.conomic and Social Research Council (grant RES-061-25-0317)