In this chapter, we revisit the origins and genesis of the french school of proximity and its evolution trough time, in order to better understand how and why the small group of researchers who were the driving force of this new way of thinking were quickly able to get a real legitimacy and effective recognition. First of all, it was clear that the role of space in economic dynamics was too often the subject of confusion and abusive assertions. Asking this question in terms of coordination made it possible to consider non-spatial factors in the analysis. The notion of proximity as a polysemic concept therefore opened the way to understanding how space matters or not, together with these other factors thus a renewed approach of questions related to space and territories. But, even starting from issues of economic nature, such an approach could not remain limited to its economic dimension, the questions of coordination involving social individuals, located in geographical space but also embedded in bundles of relationships and in institutions. Thus, it had to broaden very quickly to other disciplines in social sciences which largely contributed to consolidate the bases of what became a multidisciplinary approach and to develop theoretical as well as empirical tools.
This paper estimates trade barriers in government procurement, a market that accounts for 12% of world GDP. Using data from inter-country input-output tables in a gravity model, we find that home bias in government procurement is significantly higher than in trade between firms. However, this difference has been shrinking over time. Results also show that trade agreements with provisions on government procurement increase cross-border flows of services, whereas the effect on goods is small and not different from that in private markets. Provisions containing transparency and procedural requirements drive the liberalizing effect of trade agreements.
Beta coefficients are the cornerstone of asset pricing theory in the CAPM and multiple factor models. This chapter proposes a review of different time series models used to estimate static and time-varying betas, and a comparison on real data. The analysis is performed on the USA and developed Europe REIT markets over the period 2009–2019 via a two-factor model. We evaluate the performance of the different techniques in terms of in-sample estimates as well as through an out-of-sample tracking exercise. Results show that dynamic models clearly outperform static models and that both the state space and autoregressive conditional beta models outperform the other methods.
The Pareto model is very popular in risk management, since simple analytical formulas can be derived for financial downside risk measures (value-at-risk, expected shortfall) or reinsurance premiums and related quantities (large claim index, return period). Nevertheless, in practice, distributions are (strictly) Pareto only in the tails, above (possible very) large threshold. Therefore, it could be interesting to take into account second-order behavior to provide a better fit. In this article, we present how to go from a strict Pareto model to Pareto-type distributions. We discuss inference, derive formulas for various measures and indices, and finally provide applications on insurance losses and financial risks.
This chapter presents a survey of some recent methods used in economics and finance to account for cyclical dependence and account for their multifaced dynamics: nonlinearities, extreme events, asymmetries, non-stationarity, time-varying moments. To circumvent the caveats of the standard spectral analysis, new tools are now used based on copula spectrum, quantile spectrum and Laplace periodogram in both non-parametric and parametric contexts. The chapter presents a comprehensive overview of both theoretical and empirical issues as well as a computational approach to explain how the methods can be implemented using the R Package.
This paper proposes new estimates of potential growth for 5 major industrialized countries. We use a state-space approach to obtain joint estimates of potential growth and the natural interest rates. The model is a reduced-form of a partial equilibrium model with a Phillips curve and an IS curve. In addition to the usual determinants of prices and business fluctuations, we consider financial variables as a determinant of the business cycle.
In this paper, we tackle a generic optimal regime switching problem where the decision-making process is not the same from one regime to another. Precisely, we consider a simple model of optimal switching from competition to cooperation. To this end, we solve a two-stage optimal control problem. In the first stage, two players engage in a dynamic game with a common state variable and one control for each player. We solve for open-loop strategies with a linear state equation and linear-quadratic payoffs. More importantly, the players may also consider the possibility to switch at finite time to a cooperative regime with the associated joint optimization of the sum of the individual payoffs. Using theoretical analysis and numerical exercises, we study the optimal switching strategy from competition to cooperation. We also discuss reverse switching.