Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957, Annals of Mathematical Statistics 28, 181–187); Barnard (1963, Journal of the Royal Statistical Society, Series B 25, 294)] provides a simple method for building exact tests from statistics whose finite sample distribution is intractable but can be simulated (when no nuisance parameter is involved). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing it to statistics whose null distribution involves nuisance parameters [maximized MC (MMC) tests]. Simplified asymptotically justified versions of the MMC method are also proposed: these provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics.     

Short and long run causality measures: Theory and inference

The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. Beckenback, ed., The Theory of Prediction, McGraw-Hill, New York (Chapter 8)] and Granger [Granger, C. W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37, 424–459] is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at any given horizon h$h$ as well as tests for the corresponding non-causality [Dufour, J.-M., Renault, E., 1998. Short-run and long-run causality in time series: Theory. Econometrica 66, 1099–1125; Dufour, J.-M., Pelletier, D., Renault, É., 2006. Short run and long run causality in time series: Inference, Journal of Econometrics 132 (2), 337–362]. Instead of tests for non-causality at a given horizon, we study the problem of measuring causality between two vector processes. Existing causality measures have been defined only for the horizon 1, and they fail to capture indirect causality. We propose generalizations to any horizon h$h$ of the measures introduced by Geweke [Geweke, J., 1982. Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 304–313]. Nonparametric and parametric measures of unidirectional causality and instantaneous effects are considered. On noting that the causality measures typically involve complex functions of model parameters in VAR and VARMA models, we propose a simple simulation-based method to evaluate these measures for any VARMA model. We also describe asymptotically valid nonparametric confidence intervals, based on a bootstrap technique. Finally, the proposed measures are applied to study causality relations at different horizons between macroeconomic, monetary and financial variables in the US.     

Exact and asymptotic tests for possibly non-regular hypotheses on stochastic volatility models

We study the problem of testing hypotheses on the parameters of one- and two-factor stochastic volatility models (SV), allowing for the possible presence of non-regularities such as singular moment conditions and unidentified parameters, which can lead to non-standard asymptotic distributions. We focus on the development of simulation-based exact procedures–whose level can be controlled in finite samples–as well as on large-sample procedures which remain valid under non-regular conditions. We consider Wald-type, score-type and likelihood-ratio-type tests based on a simple moment estimator, which can be easily simulated. We also propose a C(α)-type test which is very easy to implement and exhibits relatively good size and power properties. Besides usual linear restrictions on the SV model coefficients, the problems studied include testing homoskedasticity against a SV alternative (which involves singular moment conditions under the null hypothesis) and testing the null hypothesis of one factor driving the dynamics of the volatility process against two factors (which raises identification difficulties). Three ways of implementing the tests based on alternative statistics are compared: asymptotic critical values (when available), a local Monte Carlo (or parametric bootstrap) test procedure, and a maximized Monte Carlo (MMC) procedure. The size and power properties of the proposed tests are examined in a simulation experiment. The results indicate that the C(α)-based tests (built upon the simple moment estimator available in closed form) have good size and power properties for regular hypotheses, while Monte Carlo tests are much more reliable than those based on asymptotic critical values. Further, in cases where the parametric bootstrap appears to fail (for example, in the presence of identification problems), the MMC procedure easily controls the level of the tests. Moreover, MMC-based tests exhibit relatively good power performance despite the conservative feature of the procedure. Finally, we present an application to a time series of returns on the Standard and Poor’s Composite Price Index.     

Mortality risks,education and child labor

In this paper, we investigate the role of young adult mortality on child labor and educational decisions. We argue that mortality risks are a major source of risks in returns to education in developing countries. We show that, in the absence of appropriate insurance mechanisms, the level of child labor is inefficient, but it can be too high or too low. It is too high when parents are not very altruistic and anticipate positive transfers from their children in the future. Uncertain returns to education, endogenous mortality or imperfect capital markets unambiguously increase child labor. When the level of child labor is inefficiently high, we also show that a cash transfer conditional on child's schooling can always restore efficiency regarding child labor.     

The adaptive efficiency of land use planning measured by the control of urban sprawl. The cases of the Netherlands,Belgium and Poland

The paper aims to position the concept of adaptive efficiency in planning theory, by providing insights on the conditions that explain why land use planning in some institutional contexts is able to adapt and improve more efficiently than in others. This is done by focusing on a specific planning goal: the control of residential sprawl. The paper presents a theoretical framework based on two models, one on the coordinating mechanisms regarding the use of land and one to explain institutional changes. This framework is exploited with empirical case studies where we compare the control of sprawl in the Netherlands, Belgium and Poland. The analysis diagnoses a duality of planning practices. Even though planning failures occur in the Netherlands as well, we provide evidence that Dutch land use planning has been able to efficiently adapt regulatory instruments and collaborative practices to societal changes. By contrast, Belgian and Polish planners face huge difficulties to incorporate new initiatives to control urban sprawl or, in more general terms, to reinforce their influence on land uses.     

Optimality of impulse harvesting policies

We explore the link between cyclical and smooth resource exploitation. We define an impulse control framework which can generate both cyclical solutions and steady-state solutions. Our model can admit convex and concave profit functions and allows the integration of different stock-dependent profit functions. We show that the strict concavity of the profit function is only a special case of a more general condition, related to submodularity, that ensures the existence of optimal cyclical policies. We then establish a link with the discrete-time models with cyclical solutions by Benhabib and Nishimura (J Econ Theory 35:284–306, 1985) and Dawid and Kopel (J Econ Theory 76:272–297, 1997). For the steady-state solution, we explore the relation to Clark’s (1976) continuous control model.     

Joint production under uncertainty and multifunctionality of agriculture: policy considerations and applied analysis

The impact of various policy instruments on the production ofenvironmental goods that are complementary to or competing withagricultural commodities is analysed in the light of the uncertaintyin output prices and farmers' risk aversion. Some theoreticalassessments are first summarised and then tested on a case studyconcerning beef and grassland biodiversity production in Montsdu Cantal, France. The results of simulations carried out bymathematical programming farm-level models show that joint commodityand non-commodity production is nearly independent of the degreeof farmers' risk aversion, and that commodity-linked policyinstruments are not suitable for the production of environmentalgoods even under uncertainty.