Econometrics

Summary

Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference". An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships". Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used today. A basic tool for econometrics is the multiple linear regression model. Econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods. Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, an

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https://en.wikipedia.org/wiki/Econometrics

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Robust Multivariate and Nonlinear Time Series Models

Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. Autocorrelation is one of the fundamental aspects of time series modeling. However, traditional linear models, that arise from a strong observed autocorrelation in many financial and econometric time series data, are at times unable to capture the rather nonlinear relationship that characterizes many time series data. This necessitates the study of nonlinear models in analyzing such time series. The class of bilinear models is one of the simplest nonlinear models. These models are able to capture temporary erratic fluctuations that are common in many financial returns series and thus, are of tremendous interest in financial time series analysis. Another aspect of time series analysis is homoscedasticity versus heteroscedasticity. Many time series data, even after differencing, exhibit heteroscedasticity. Thus, it becomes important to incorporate this feature in the associated models. The class of conditional heteroscedastic autoregressive (ARCH) models and its variants form the primary backbone of conditional heteroscedastic time series models. Robustness is a highly underrated feature of most time series applications and models that are presently in use in the industry. With an ever increasing amount of information available for modeling, it is not uncommon for the data to have some aberrations within itself in terms of level shifts and the occasional large fluctuations. Conventional methods like the maximum likelihood and least squares are well known to be highly sensitive to such contaminations. Hence, it becomes important to use robust methods, especially in this age with high amounts of computing power readily available, to take into account such aberrations. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of robust parameter estimation of some simple vector and nonlinear time series models. More precisely, we will briefly study some prominent linear and nonlinear models in the time series literature and apply the robust S-estimator in estimating parameters of some simple models like the vector autoregressive (VAR) model, the (0, 0, 1, 1) bilinear model and a simple conditional heteroscedastic bilinear model. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.

EPFL2010

The dissertation investigates the phenomenon of firms that make voluntary contributions to the stock of scientific knowledge. Such a firm behaviour appears counterintuitive from a traditional viewpoint, since no direct financial returns can be expected while the disclosure of research outcomes may lead to knowledge spillovers to competing firms. This raises the questions why firms engage in those boundary-spanning activities, how scientific disclosure strategies are implemented, and to what extent scientific disclosure strategies affect the firm performance. The dissertation provides empirical evidence on these questions using representative firm-level data and econometric analysis techniques. The first chapter of the dissertation reviews the relevant literature, underlines the relevance of doing research on this topic by providing descriptive evidence on the amounts of scientific contributions by firms, and includes some novel insights from interviews which I conducted at several large R&D performing firms. I discuss the potential benefits, costs and organizational challenges of scientific disclosure strategies and position scientific publications as an expression of openness in the light of alternative openness definitions prevalent in innovation research. The second chapter focuses on the question why firms publish in scientific journals using a cost-benefit framework. In particular, I analyse whether scientific contributions of firms are used as an instrument to get access to academic knowledge networks. Moreover, I explore environmental conditions by considering the effectiveness of appropriation instruments and the spillover levels in a sector that may impact the firms’ decision-making process. The analysis relies on data from the French Innovation survey and provides evidence for the predictions that firms show reciprocal behaviour in exchange of obtaining valuable academic knowledge. However, the propensity of firms to publish is rather sensitive to the spillover threats. The third chapter examines how are firms able to publish by testing the requirements and origins of scientific contributions by firms. I explicitly consider the heterogeneity of scientific outputs and provide a complementary view on the requirements for the creation of inventive outcomes. The requirements considered can be classified according to the research orientation and the demographic composition of the R&D labs. The analysis revealed that scientific disclosure strategies require specific resource allocations and capabilities, which are in part not necessary for the generation of inventive outcomes. The findings support the view that disclosure strategies can be costly and are not only a by-product of the “usual” R&D activities. The fourth chapter addresses the performance effects of scientific activities of firms. More specifically, I examine whether scientific publications stocks provide valuable information to the financial markets, potentially leading to different valuations. I consider the heterogeneity of scientific and inventive outcomes and found evidence for a positive impact of scientific contributions on the firm’s Tobin’s Q beyond the effects of R&D stocks, patent stocks and patent quality.

EPFL2013

What makes companies pursue an Open Science strategy?

Julio Diego Salvador Raffo, Markus Simeth

Whereas recent scholarly research has provided many insights about universities engaging in commercial activities, there is still little empirical evidence regarding the opposite phenomenon of companies disseminating scientific knowledge. Our paper aims to fill this gap and explores the motivations of firms that disclose research outcomes in a scientific format. Besides considering a dimension internal to the firm, we focus particularly on knowledge sourcing from academic institutions and the appropriability regime. We conduct an econometric analysis with firm-level data from the fourth edition of the French innovation survey (CIS) and matched scientific publications for a sample of 2512 R&D performing firms from all manufacturing sectors. This analysis provides evidence that firms are more likely to adopt academic principles if they need to access scientific knowledge that is considered important for their innovation development, whereas the mere existence of collaborative links with academic institutions is not a strong determinant. Furthermore, the results suggest that the inclination of firms to publish is sensitive to the level of knowledge spillovers in a sector and the effectiveness of legal appropriation instruments. (C) 2013 Elsevier B.V. All rights reserved.

Elsevier Science Bv2013

Robust Multivariate and Nonlinear Time Series Models

EPFL2010

EPFL2013