Jean-François Richard

Economic Development, Legality and the Transplant Effect

This paper analyzes the determinants of effective legal institutions (legality) and their impact on economic development today using data from 49 countries. We show that the way the law was initially transplanted and received is a more important determinant than the supply of law from a particular legal family (i.e. English, French, German, or Scandinavian). Countries that have developed legal orders internally, adapted the transplanted law to local conditions, and/or had a population that was already familiar with basic legal principles of the transplanted law have more effective legality than ”transplant effect” countries that received foreign law without any similar pre-dispositions. Controlling for the supply of legal families, we find that legality is roughly one third lower in transplant effect countries. While the transplant effect has no direct impact on economic development, it has a strong indirect effect via its impact on legality. The strong path dependence between economic development, legality and the transplant effect helps explain why legal technical assistance projects that focus primarily on improving the laws on the books frequently have so little impact. Finally, our statistical methodology produces a legality index based on observed legality proxies that almost fully captures their interaction with the way in which the law was transplanted, the supply of particular legal families and economic development. *We would like to than Jan Kleinheisterkamp (Max Planck Institute, Hamburg), for his help with background information on Latin America and the coding of these countries. We are also grateful to the comments of seminar participants at the World Bank and the University of Wisconsin-Madison.

Univariate and multivariate stochastic volatility models: estimation and diagnostics

A Maximum Likelihood (ML) approach based upon an Efficient Importance Sampling (EIS) procedure is used to estimate several extensions of the standard Stochastic Volatility (SV) model for daily financial return series. EIS provides a highly generic procedure for a very accurate Monte Carlo evaluation of the marginal likelihood which depends upon high-dimensional interdependent integrals. Extensions of the standard SV model being analyzed only require minor modifications in the ML-EIS procedure. Furthermore, EIS can also be applied for filtering which provides the basis for several diagnostic tests. Our empirical analysis indicates that extensions such as a semi-nonparametric specification of the error term distribution in the return equation dominate the standard SV model. Finally, we also apply the ML-EIS approach to a multivariate factor model with stochastic volatility.

Bidder Collusion at Forest Service Timber Sales

Allegations of Bidder collusion at Forest Service timber sales in the Pacific Northwest were common in the 1970s. Of course, prices may be low for reasons other than collusion. We formulate an empirical model that allows for both bidder collusion and supply effects and in which we control for demand conditions. Noncooperative behavior in which a single unit is sold (the standard auction model) is a special case: it is found to be definitively outperformed by a model of collusion. We also find that supply effects are dominated by collusion in determining the winning bids in the market.

BAYESIAN ANALYSIS OF SIMULTANEOUS EQUATION SYSTEMS

Publisher Summary This chapter discusses the Bayesian inference and identification. A Bayesian analysis of the scanning electron microscope (SEM) proceeds along the same lines as any other Bayesian analysis. Thus, if the analyst has chosen to work in a given parameter space, a prior density on that space is defined and Bayes theorem is applied to revise this prior density in the light of available data. The resulting posterior density is then used to solve problems of decision and inference. Predictive densities for future observations can also be derived. The chapter discusses numerical methods for evaluating key characteristics of posterior and predictive density functions. For models with many parameters, such as most simultaneous equation models, analytical methods remain indispensable to evaluate these densities—either fully, or conditionally on a few parameters amenable to numerical treatment, or approximately to construct importance functions for Monte Carlo integration. The classes of prior densities permitting analytical evaluation of the posterior density are limited. In most Bayesian analyses they comprise essentially the so-called noninformative and natural-conjugate families.

Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models

In this paper, efficient importance sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate stochastic volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure for the Monte Carlo (MC) evaluation of high-dimensional interdependent integrals. It can be used to carry out ML-estimation of SV models as well as simulation smoothing where the latent volatilities are sampled at once. Based on this EIS simulation smoother, a Bayesian Markov chain Monte Carlo (MCMC) posterior analysis of the parameters of SV models can be performed.

Super-Experienced Bidders In First-Price Common-Value Auctions: Rules Of Thumb, Nash Equilibrium Bidding, And The Winner'S Curse

Super-experienced bidders have learned to overcome the winners curse but still earn less than 50 of Nash equilibrium profits. Subjects deviate from the complicated Nash strategy, employing piecewise-linear bid functions that are capable, in principle, of generating an equilibrium with average profits at or above the Nash benchmark. Thus, limited computational abilities alone cannot account for the reduced earnings. Further, subjects are far from best responding within this family of piecewise-linear bid functions. Alternative factors contributing to these reduced earnings are explored.

PHANTOM BIDDING AGAINST HETEROGENEOUS BIDDERS

Abstract If IPV bidders are distributionally heterogeneous then a revenue maximizing English auctioneer will, in general, find it optimal to use a non-constant reserve price that is a function of the observed bid sequence. An example is provided.

Improving MCMC, using efficient importance sampling

A generic Markov Chain Monte Carlo (MCMC) framework, based upon Efficient Importance Sampling (EIS) is developed, which can be used for the analysis of a wide range of econometric models involving integrals without analytical solution. EIS is a simple, generic and yet accurate Monte-Carlo integration procedure based on sampling densities which are global approximations to the integrand. By embedding EIS within MCMC procedures based on Metropolis-Hastings (MH) one can significantly improve their numerical properties, essentially by providing a fully automated selection of critical MCMC components, such as auxiliary sampling densities, normalizing constants and starting values. The potential of this integrated MCMC-EIS approach is illustrated with simple univariate integration problems, and with the Bayesian posterior analysis of stochastic volatility models and stationary autoregressive processes.

Likelihood Evaluation for Dynamic Latent Variables Models

We propose a general Monte Carlo simulation technique for evaluating the likelihood function of dynamic latent variables models, based on artificial factorizations of the sequential joint density of the observables and latent variables. The feasibility of the proposed technique is demonstrated by means of a pilot application to a one-parameter disequilibrium model. Extensions to models with weakly exogenous variables and the use of acceleration methods are discussed.

Stability of a U.K. Money Demand Equation: A Bayesian Approach to Testing Exogeneity

The paper analyses an M3 demand for money equation for the United Kingdom. Attention is paid to the policy change that occurred in 1971 with the introduction of the measure known as Competition and Credit Control. Classical and Bayesian single equation instrumental variables procedures are developed to investigate the exogeneity of the short-term interest rate and the constancy of the parameters of the underlying relationships. The parameters of the short-term equation have changed as well as the exogeneity status of the interest rate variable but the parameters of the long-term equation appear to be less affected by the policy change.