Applied economics

Applied economics is the study as regards the application of economic theory and econometrics in specific settings. As one of the two sets of fields of economics (the other set being the core), it is typically characterized by the application of the core, i.e. economic theory and econometrics to address practical issues in a range of fields including demographic economics, labour economics, business economics, industrial organization, agricultural economics, development economics, education economics, engineering economics, financial economics, health economics, monetary economics, public economics, and economic history. From the perspective of economic development, the purpose of applied economics is to enhance the quality of business practices and national policy making. The process often involves a reduction in the level of abstraction of this core theory. There are a variety of approaches including not only empirical estimation using econometrics, input-output analysis or simulations but also case studies, historical analogy and so-called common sense or the "vernacular". This range of approaches is indicative of what Roger Backhouse and Jeff Biddle argue is the ambiguous nature of the concept of applied economics. It is a concept with multiple meanings. Among broad methodological distinctions, one source places it in neither positive nor normative economics but the art of economics, glossed as "what most economists do". The origin and meanings of applied economics have a long history going back to the writing of Say and Mill. Say wrote about "applying" the "general principles of political economy" to "ascertain the rule of action of any combination of circumstances presented to us". The full title of Mill's (1848) work is Principles of Political Economy with Some of Their Applications to Social Philosophy. John Neville Keynes was perhaps the first to use the phrase "applied economics".

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Dynamic inconsistency

In economics, dynamic inconsistency or time inconsistency is a situation in which a decision-maker's preferences change over time in such a way that a preference can become inconsistent at another point in time. This can be thought of as there being many different "selves" within decision makers, with each "self" representing the decision-maker at a different point in time; the inconsistency occurs when not all preferences are aligned.

Reduced form

In statistics, and particularly in econometrics, the reduced form of a system of equations is the result of solving the system for the endogenous variables. This gives the latter as functions of the exogenous variables, if any. In econometrics, the equations of a structural form model are estimated in their theoretically given form, while an alternative approach to estimation is to first solve the theoretical equations for the endogenous variables to obtain reduced form equations, and then to estimate the reduced form equations.

Innovation economics

Innovation economics is new, and growing field of economic theory and applied/experimental economics that emphasizes innovation and entrepreneurship. It comprises both the application of any type of innovations, especially technological, but not only, into economic use. In classical economics this is the application of customer new technology into economic use; but also it could refer to the field of innovation and experimental economics that refers the new economic science developments that may be considered innovative.

Les mathématiques financières robustes, le maritime et les FFA

Jérôme Reboulleau

Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development in mathematical finance. The market in shipping derivatives (FFA and FOSVA) has only been developed after 2000 and now exhibits impressive growth. Financial actors have entered the field, but it is still largely undiscovered by institutional investors. The first part of the work was to identify the characteristics of the market in shipping, i.e. the segmentation and the volatility. Because the shipping business is old-fashioned, even the leading actors on the world stage (ship owners and banks) are using macro-economic models to forecast the rates. If the macro-economic models are logical and make sense, they fail to predict. For example, the factor port congestion has been much cited during the last few years, but it is clearly very difficult to control and is simply an indicator of traffic. From our own experience it appears that most ship owners are in fact market driven and rather bad at anticipating trends. Due to their ability to capture large moves, we chose to consider Lévy processes for the underlying price process. Compared with the macro-economic approach, the main advantage is the uniform and systematic structure this imposed on the models. We get in each case a favorable result for our technology and a gain in forecasting accuracy of around 10% depending on the maturity. The global distribution is more effectively modelled and the tails of the distribution are particularly well represented. This model can be used to forecast the market but also to evaluate the risk, for example, by computing the VaR. An important limitation is the non-robustness in the estimation of the Lévy processes. The use of robust estimators reinforces the information obtained from the observed data. Because maximum likelihood estimation is not easy to compute with complex processes, we only consider some very general robust score functions to manage the technical problems. Two new class of robust estimators are suggested. These are based on the work of F. Hampel ([29]) and P. Huber ([30]) using influence functions. The main idea is to bound the maximum likelihood score function. By doing this a bias is created in the parameters estimation, which can be corrected by using a modification of the following type and as proposed by F. Hampel. The procedure for finding a robust estimating equation is thus decomposed into two consecutive steps : Subtract the bias correction and then Bound the score function. In the case of complex Lévy processes, the bias correction is difficult to compute and generally unknown. We have developed a pragmatic solution by inverting the Hampel's procedure. Bound the score function and then Correct for the bias. The price is a loss of the theoretical properties of our estimators, besides the procedure converges to maximum likelihood estimate. A second solution to for achieving robust estimation is presented. It considers the limiting case when the upper and lower bounds tend to zero and leads to B-robust estimators. Because of the complexity of the Lévy distributions, this leads to identification problems.

EPFL2008

Behavioural economics

Behavioral economics studies the effects of psychological, cognitive, emotional, cultural and social factors in the decisions of individuals or institutions, and how these decisions deviate from those implied by classical economic theory. Behavioral economics is primarily concerned with the bounds of rationality of economic agents. Behavioral models typically integrate insights from psychology, neuroscience and microeconomic theory. The study of behavioral economics includes how market decisions are made and the mechanisms that drive public opinion.

Experimental economics

Experimental economics is the application of experimental methods to study economic questions. Data collected in experiments are used to estimate effect size, test the validity of economic theories, and illuminate market mechanisms. Economic experiments usually use cash to motivate subjects, in order to mimic real-world incentives. Experiments are used to help understand how and why markets and other exchange systems function as they do. Experimental economics have also expanded to understand institutions and the law (experimental law and economics).

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.