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We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

We generalise the spectral EM algorithm for dynamic factor models in Fiorentini, Galesi, and Sentana (2014) to bifactor models with pervasive global factors complemented by regional ones. We exploit the sparsity of the loading matrices so that researchers can estimate those models by maximum likelihood with many series from multiple regions. We also derive convenient expressions for the spectral scores and information matrix, which allows us to switch to the scoring algorithm near the optimum. We explore the ability of a model with a global factor and three regional ones to capture inflation dynamics across 25 European countries over 1999–2014.

Equilibrium job search models allow for labor markets with homogeneous workers and firms to yield nondegenerate wage densities. However, the resulting wage densities do not accord well with empirical regularities. Accordingly, many extensions to the basic equilibrium search model have been considered (e.g., heterogeneity in productivity, heterogeneity in the value of leisure, etc.). It is increasingly common to use nonparametric forms for these extensions and, hence, researchers can obtain a perfect fit (in a kernel smoothed sense) between theoretical and empirical wage densities. This makes it difficult to carry out model comparison of different model extensions. In this paper, we first develop Bayesian parametric and nonparametric methods which are comparable to the existing non-Bayesian literature. We then show how Bayesian methods can be used to compare various nonparametric equilibrium search models in a statistically rigorous sense.

This chapter estimates a regime switching Taylor Rule for the European Central Bank (ECB) in order to investigate some potential nonlinearities in the forward-looking policy reaction function within a real-time framework. In order to compare observed and predicted policy behavior, the chapter estimates Actual and Perceived regime switching Taylor Rules for the ECB. The former is based on the refi rate set by the Governing Council while the latter relies on the professional point forecasts of the refi rate performed by a large investment bank before the upcoming policy rate decision. The empirical evidence shows that the Central Bank’s main policy rate has switched between two regimes: in the first one the Taylor Principle is satisfied and the ECB stabilizes the economic outlook, while in the second regime the Central Bank cuts rates more aggressively and puts a higher emphasis on stabilizing real output growth expectations. Second, the results point out that the professional forecasters have broadly well predicted the actual policy regimes. The estimation results are also robust to using consensus forecasts of inflation and real output growth. The empirical evidence from the augmented Taylor Rules shows that the Central Bank has most likely not responded to the growth rates of M3 and the nominal effective exchange rate and the estimated regimes are robust to including these additional variables in the regressions. Finally, after the bankruptcy of Lehman Brothers the policy rate has switched to a crisis regime as the ECB has focused on preventing a further decline in economic activity and on securing the stability of the financial system.

This study examines the rarely investigated association between institutional ownership and income smoothing. The results support the predicted positive association between institutional ownership and the likelihood of firms smoothing earnings towards their earnings trend in general. However, this association is not systematic across all firms. The positive association is most evident among profit firms with pre‐managed earnings above their earnings trend. No significant association is found for profit firms with pre‐managed earnings below their earnings trend and loss firms in general. This study also finds that, in Australia, while institutional ownership has a non‐linear association with income increasing earnings management (Koh, 2003), such association manifests itself within the income smoothing framework. The results of this study highlight the complexities in the association between institutional ownership and earnings management strategies, and future research can benefit by explicitly examining the trade‐offs between alternative earnings management incentives and the factors that affect the relative strength of these incentive trade‐offs.

In the context of Dynamic Factor Models, we compare point and interval estimates of the underlying unobserved factors extracted using small- and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the maximum likelihood Kalman filter and smoother factor estimates are highly correlated with those obtained using hybrid procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.

This paper aims to examine regime switching behaviour of the nominal exchange rate in Uganda to shed light on the necessity (as well as efficacy) of the participation of the central bank market.

The homogenous two‐state Markov chain methodology was employed to investigate the possibility of regime changes in the nominal exchange rate. The maximum likelihood parameter estimates were obtained using the Broyden‐Fletcher‐Goldfarb‐Shanno iteration algorithm.

The results validate the expectation of the two distinct state spaces characterized as sharp and disruptive but short‐lived depreciations as well as small appreciations occurring through a long period. The central bank intervention actions are shown to be largely successful in mitigating the disruptive effects of the sharp depreciations.

The paper lends empirical support to the intervention actions of the Bank of Uganda. In face of the numerous disruptions to the short‐term exchange rate process, failure to intervene may cause rational panic and given the nature of investor behavior, this may quickly spread and even cause further disruptions. It is important for the central bank to send signals that these disruptions are temporary.

The homogenous Markov chain specification employed in this study makes it possible to avoid the pitfalls that may arise by attempting to specify a structural model for the exchange rate. In addition, inference about the different possible state spaces is made on the basis of all available information.