This paper develops two instrumental variable (IV) estimators for dynamic panel data models with exogenous covariates and a multifactor error structure when both crosssectional and time series dimensions, N and T respectively, are large. The main idea is to project out the common factors from the exogenous covariates of the model, and construct instruments based on defactored covariates. For models with homogeneous slope coefficients, we propose a two-step IV estimator: in the first step, the model is estimated consistently by employing defactored covariates as instruments. In the second step, the entire model is defactored based on estimated factors extracted from the residuals of the first step estimation; subsequently, an IV regression is implemented using the same instruments as in step one. For models with heterogeneous slope coefficients, we propose a mean-group type estimator, which involves averaging of first-step IV estimates of cross-section specific slopes. The proposed estimators do not need to seek for instrumental variables outside the model. Furthermore, these estimators are linear, thereby computationally robust and inexpensive. Notably, they require no bias correction. The finite sample performances of the proposed estimators and associated statistical tests are investigated, and the results show that the estimators and the tests perform well even for small N and T . * This paper is dedicated to the memory of our wonderful colleague Guowei Cui, who passed away while this paper was being peer-reviewed. We are grateful to Artūras Juodis, Mervyn Silvapulle, three anonymous referees and an Associate Editor, Professor Tom Wansbeek, for comments and suggestions that led to significant improvements.

Summary This paper analyses the instrumental variables (IV) approach put forward by Norkute et al. (2021), in the context of static linear panel data models with interactive effects present in the error term and the regressors. Instruments are obtained from transformed regressors, thereby it is not necessary to search for external instruments. We consider a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis reveals that: (i) the $\sqrt{\textit {NT}}$-consistent 2SIV estimator is free from asymptotic bias that may arise due to the estimation error of the interactive effects, while (ii) existing estimators can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as existing estimators that eliminate interactive effects jointly in the regressors and the error, while (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results.

The ladybird beetle Coccinella septempunctata (L.) is an important biocontrol agent of pests such as various aphid species. Despite being one of the most studied coccinellid species, many aspects of its foraging behavior are still not completely understood. This study focuses on the diel foraging behavior of C. septempunctata, investigating their olfactory orientation toward aphid-infested plants, walking activity on plants and on the soil, and feeding rates. In the scotophase the ladybird beetles were significantly more attracted to the odor of aphid-infested plants, on which they also showed considerably higher walking activity then on uninfested controls. Females were more prone to utilize olfactory cues when searching for prey and fed at higher rates than males; this shows that they are better adapted to nocturnal activity, as they require higher food intake. Coccinella septempunctata have the same feeding rate during the scotophase as in the photophase. Our study shows that C. septempunctata has the potential to forage in the scotophase if prey is abundant. The results support the hypothesis that volatiles of aphid-infested plants can attract or arrest foraging adult ladybird beetles, even in the darkness, which makes a considerable contribution to efficient prey search and enhances feeding capacity.

Most empirical evidence suggests that the efficient futures market hypothesis, henceforth referred to as EFMH, stating that spot and futures prices should cointegrate with a unit slope on futures prices, does not hold, a finding at odds with many theoretical models. This article argues that these results can be attributed in part to the low power of univariate tests, and that the use of panel data can generate more powerful tests. The current article can be seen as a step in this direction. In particular, a newly developed factor analytical approach is employed, which is very general and, in addition, free of the otherwise so common incidental parameters bias in the presence of fixed effects. The approach is applied to a large panel covering 17 commodities between March 1991 and August 2012. The evidence suggests that the EFMH cannot be rejected once the panel evidence has been taken into account. © 2014 Wiley Periodicals, Inc. Jrl Fut Mark 35:357–370, 2015