Chômage | EPFL Graph Search

Le chômage est l'état d'une personne qui n'a pas d'emploi mais souhaite travailler et recherche un emploi. Un phénomène économique important, la définition du chômage comme l'explication de ses causes et de ses remèdes font l'objet de nombreux débats et controverses théoriques et statistiques. Le chômage est souvent considéré comme résiduel et volontaire jusqu’au début du .

Lors de la Grande Dépression des années 1930, le chômage devient par son ampleur un des problèmes sociaux et économiques les plus centraux des pays développés. La détermination du niveau de l’emploi devient également avec cette crise économique une des questions les plus fondamentales de la réflexion économique : des économistes comme Keynes affirment que l'équilibre de plein emploi n'est pas spontanément garanti mais que l'État a les moyens de rétablir le plein emploi.
La réflexion sur la dynamique économique montre par ailleurs que le progrès économique et social résulte d'un « mouvement de destruction cr

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

Necessity entrepreneurship and industry choice in new firm creation

Marc Gruber, Argyro Nikiforou

Research Summary Research on necessity entrepreneurship has generated important insights, yet it views necessity entrepreneurs in developed countries as one encompassing group of unemployed individuals-ignoring that the level of need is not uniform but instead increases with time spent in unemployment. We begin to unpack the role of unemployment duration in necessity entrepreneurship by asking how it affects one of the most fundamental decisions in start-ups: "what business should I be in?" Analyzing primary data on 576 necessity entrepreneurs combined with three secondary data sets, we find that unemployment duration affects whether ventures are launched in "home" or in external industries, and moderates the extent to which founders' industry experience and the attractiveness of external opportunities relative to those in the "home" industry shape industry choice. Managerial Summary Necessity entrepreneurs-individuals who create new firms because they have no other options for work-represent a substantial proportion of world-wide entrepreneurial activity, and, in developed countries, often come from the ranks of the unemployed. We analyze these entrepreneurs by answering the question "what business should I be in?," a fundamental strategic decision that founders make. Our findings reveal that duration in unemployment is a key, hitherto unexamined factor that systematically affects the industry-choice decision in startups. Moreover, we find that duration of unemployment moderates the founder's industry experience and the attractiveness of external opportunities relative to those in the "home" industry, with a markedly different picture for the long-term unemployed-suggesting the need for customized government policies for formerly unemployed entrepreneurs.

WILEY2019

Quantifying the Benefits of Labor Mobility in a Currency Union

Unemployment differentials are bigger in Europe than in the United States. Migration responds to unemployment differentials, though the response is smaller in Europe. Mundell (1961) argued that factor mobility is a precondition for a successful currency union. We use a multi-country DSGE model with cross-border migration and search frictions to quantify the benefits of increased labor mobility in Europe and compare this outcome to a case of fully flexible exchange rates. Labor mobility and flexible exchange rates both work to reduce unemployment and per capita GDP differentials across countries provided that monetary policy is sufficiently responsive to national output.

2018

Robust Multivariate and Nonlinear Time Series Models

EPFL2010