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Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak σ-convergence tests, have recently been developed (Kong, Phillips, & Sul, 2019) to evaluate such trends in dispersion in panel data using simple linear trend regressions. To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present chapter examines the behavior of these convergence tests when heteroskedastic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are reflected in finite sample performance in numerical exercises. The explanation is that weak σ-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.

The authors analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measurements. The authors study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators (MLE) of the dynamics parameters and reductions in the precision of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. The authors also develop an R² measure of common trend observability that determines the severity of misspecification. Finally, the authors apply their framework to US quarterly data on GDE and GDI, obtaining an improved aggregate output measure.

White (1980).

An overview of the cointegration approach to econometric specification and estimation is provided. A non‐technical approach is adopted, and is intended to serve as an entry into this important new literature for the reader with no background knowledge of the subject but with some limited knowledge of econometrics. Particular emphases are given to the rationale for using cointegration techniques in the estimation of economic relationships, to providing intuitive explanations of the concepts and techniques, and to demonstrating their applications in practice. Reference is made throughout to other articles which explain particular methods or recent developments more formally and fully than is possible here. Finally, a simple application of cointegration techniques to the estimation of the consumption function is provided.

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

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.

Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

The aim of this paper is twofold: first, it aims to examine the relevance of earnings and book values on stock prices, and second, to test for the effect of speculative intensity on the relevance of accounting information between 1995 and 2004.

The data were collected from a sample of 101 non‐financial firms listed at the Athens Stock Exchange over the period 1996‐2004 and were analyzed using OLS regression models.

Results indicated that book values are not relevant when they are considered solely, but both earnings and book values are more relevant when they are simultaneously included in the model. Finally, taking into consideration the effect of speculative intensity, we observed that it has a positive and significant effect on stock prices yet the value relevance of earnings and book values has not changed even after controlling for speculation.

The findings can be used by analysts and capital market researchers since both must bear in mind the fact that the quality of the capital market and the quality of the stock prices are not constant over time. Consequently, the classical valuation models are misspecified and could yield significant bias if speculative intensity is not taken under consideration.

The findings provide a further insight on the issue of accounting relevance within the context of an emerging capital market like Greece. According to our knowledge this is the first study which concerns speculation as an important factor of accounting value relevance in an European country.