We review the changes in modelling strategy and econometric methodology when estimating a firm-level investment equation on panel data during the past twenty years, in order to assess which of these changes result from new estimation methods and changes in the practice of panel data econometrics, and which are ”real” and due to the evolution of the economy. Thus our paper consists of a series o...

The spatial autoregressive model (SAR) is a standard tool to analyze data with spatial correlation. Conventional estimation methods rely on the key assumption that the spatial weight matrix W is strictly exogenous, which is likely to be violated in empirical analyses. This paper presents the speci…cation and estimation of the SAR model with an endogenous spatial weight matrix. The outcome equat...

Using a panel of developed and developing countries, we investigate whether a country’s investment and economic growth rate are negatively related to its violence level. Using GMM and Arelano-Bond procedures we found that violence is a strong deterrent to investment and thus hinders economic growth. Furthermore, we use a broader measure of violence that is able to account for civil war and viol...

The GMM (generalized min-max) kernel was recently proposed [5] as a measure of data similarity and was demonstrated effective in machine learning tasks. In order to use the GMM kernel for large-scale datasets, the prior work resorted to the (generalized) consistent weighted sampling (GCWS) to convert the GMM kernel to linear kernel. We call this approach as “GMM-GCWS”. In the machine learning l...

Real-time learning requires on-line complexity estimation. Expectation-maximisation (EM) and sampling techniques are presented that enable simultaneous estimation of the complexity and continuous parameters of Gaussian mixture models (GMMs) which can be used for density estimation, classiication and feature extraction. The solution is a maximum a posteriori probability (MAP) estimator that is c...

Real-time learning requires on-line complexity estimation. Expectation-maximisation (EM) and sampling techniques are presented that enable simultaneous estimation of the complexity and continuous parameters of Gaussian mixture models (GMMs) which can be used for density estimation, classi cation and feature extraction. The solution is a maximum a posteriori probability (MAP) estimator that is c...

We explored whether young children exhibit subtypes of behavioral sequences during sibling interaction. Ten-minute, free-play observations of over 300 sibling dyads were coded for positivity, negativity and disengagement. The data were analyzed using growth mixture modeling (GMM). Younger (18-month-old) children's temporal behavioral sequences showed a harmonious (53%) and a casual (47%) class....

This paper describes an algorithm for detection of nonlinguistic vocalisations, such as laughter or fillers, based on acoustic features. The algorithm proposed combines the benefits of Gaussian mixture models (GMM) and the advantages of support vector machines (SVMs). Three GMMs were trained for garbage, laughter, and fillers, and then an SVM model was trained in the GMM score space. Various ex...

In a general classification, the economy of any country is divided into two parts of official and invisible economies. Invisible activities drop outside the scope of the law and official economy and strongly affect socioeconomic development and the formal sector of all countries.These activities which are known under various titles including the shadow economy are influenced by various factors....