Testing of the mean reversion parameter in continuous time models

In this paper we use the approximate bias expressions developed in Yu (2012) and Bao et al. (2013) to improve the testing of the ordinary least squares or quasi-maximum likelihood estimator of the mean reversion parameter in continuous time models. We follow the approach given in Iglesias and Phillips (2005) and Chambers (2013), where if we bias correct the estimated mean reversion parameter, we can improve on the small sample properties of the testing procedure. Simulation results confirm the usefulness of this approach using a t$t$-statistic in this setting in the near unit root situation when the mean reversion parameter is approaching its lower bound. Therefore we always recommend bias correcting when applying a t$t$-statistic in practice in this context.     

On the equivalence of indirect inference and bootstrap bias correction for linear IV estimators

This note shows that two ways of simulation based bias correction–indirect inference and bootstrap bias correction–are equivalent for two-stage-least-squares, as well as k$k$-class estimators for the standard linear model with endogenous regressors.     

Cartel size and collusive stability with non-capitalistic players

A well established belief both in the game-theoretic IO and in policy debates is that market concentration facilitates collusion. We show that this piece of conventional wisdom relies upon the assumption of profit-seeking behaviour, for it may be reversed when firms pursue other plausible goals. To illustrate our intuition, we investigate the incentives to tacit collusion in an industry formed by labour-managed (LM$LM$) enterprises. We characterise the perfect equilibrium of a supergame in which LM$LM$ firms play an infinitely repeated Cournot game under grim trigger strategies. We show that the critical threshold of the discount factor above which collusion is stable (i) is lower in the LM$LM$ industry than in the capitalistic one; (ii) monotonically decreases with the number of firms.     

Partial identification in binary response models with nonignorable nonresponses

This study investigates the identification of parameters in semiparametric binary response models of the form y=1(x^′β+v+ε>0)$y=1(x^{\prime }\beta +v+\epsilon >0)$ when there are nonignorable nonresponses. We propose an estimation procedure for the identified set, the set of parameters that are observationally indistinguishable from the true value β$\beta$, based on the special regressor approach of Lewbel (2000). We show that the estimator for the identified set is consistent in the Hausdorff metric.     

The functional central limit theorem for ARMA–GARCH processes

In this paper, we study the functional central limit theorem for ARMA–GARCH processes. We prove that, under the finite second moment assumption, the stationary ARMA–GARCH process is geometrically_L2$L_2$-NED and that the functional central limit theorem holds.     

Estimating aggregate autoregressive processes when only macro data are available

The aggregation of individual random AR(1) models generally leads to an AR(∞$\infty$) process. We provide two consistent estimators of aggregate dynamics based on either a parametric regression or a minimum distance approach for use when only macro data are available. Notably, both estimators allow us to recover some moments of the cross-sectional distribution of the autoregressive parameter. Both estimators perform very well in our Monte-Carlo experiment, even with finite samples.     

A Hausman–Taylor instrumental variable approach to the penalized estimation of quantile panel models

This paper proposes an _?1${?}_1$ penalized quantile regression estimator which adapts the Hausman–Taylor instrumental variable approach in order to address the bias resulting from the shrinkage of the individual effects.     

The monetary utility premium and interpersonal comparisons

The utility premium is generally defined as the pain or reduction in expected utility caused by an n$n$th-degree risk increase, where n≥2$n\ge 2$. While it is a very useful concept in understanding a decision maker’s choice in uncertain situations, the utility premium is not interpersonally comparable. This note shows that the monetary utility premium–the utility premium divided by the expected marginal utility at the random starting wealth–is interpersonally comparable, and the comparison is characterized by Ross more risk aversion of the corresponding degree.     

The Debreu private ownership economy with complementary commodities and prices

Let E_p be a Debreu private ownership economy in which there are some complementary commodities. It means that all commodity bundles are contained in the proper subspace V   of commodity–space [R]l(l∈{1,2,…})${\mathfrak{[R]}}^l\left(l\in \left\{1,2,\dots \right\}\right)$. The production plans maximizing the producers' profits do not have to satisfy the dependency in the qualities of commodities seen in the consumers' plans. It may cause no-existence an equilibrium in economy E_p. The competitive leads, however, to adjustment the production plans to improve the consumers' satisfaction. The procedure of change the production system covering the consumers' requirements is presented. As a result, the model of the private ownership economy with complementary commodities and prices, being the simplification of the initial model, is elaborated. Consequently, the necessary condition for the existence of an equilibrium in the economy E_p is proved.     

Substituting one risk increase for another: A method for measuring risk aversion

This paper defines the rate of substitution of one stochastic change to a random variable for another. It then focuses on the case where one of these changes is an nth degree risk increase, and the other is an m  th degree risk increase, where n>m?1$n>m?1$. The paper shows that the rate of substitution for these two risk increases can be used to provide a broader definition and two additional characterizations of the nth degree Ross more risk averse partial order. The implications for local intensity measures of nth degree risk aversion are also examined. The analysis organizes the existing results as well as generates new ones.     

Mean-dispersion preferences and constant absolute uncertainty aversion

We axiomatize, in an Anscombe–Aumann framework, the class of preferences that admit a representation of the form V(f)=μ−ρ(d)$V(f)=\mu -\rho (d)$, where μ is the mean utility of the act f with respect to a given probability, d   is the vector of state-by-state utility deviations from the mean, and ρ(d)$\rho (d)$ is a measure of (aversion to) dispersion that corresponds to an uncertainty premium. The key feature of these mean-dispersion   preferences is that they exhibit constant absolute uncertainty aversion. This class includes many well-known models of preferences from the literature on ambiguity. We show what properties of the dispersion function ρ(⋅)$\rho (\cdot )$ correspond to known models, to probabilistic sophistication, and to some new notions of uncertainty aversion.     

Extreme value statistics and recurrence intervals of NYMEX energy futures volatility

Energy markets and the associated energy futures markets play a crucial role in global economies. It is of great theoretical and practical significance to gain a deeper understanding of extreme value statistics of the volatility of energy futures traded on the New York Mercantile Exchange (NYMEX). We investigate the statistical properties of the recurrence intervals of daily volatility time series of four NYMEX energy futures, which are defined as the waiting times τ between consecutive volatilities exceeding a given threshold q. We find that the recurrence intervals are distributed as a stretched exponential $P_q\left(\tau \right)\sim e^{{\left(\mathit{a\tau }\right)}^{-\gamma }}$, where the exponent γ decreases with increasing q, and there is no scaling behavior in the distributions for different thresholds q after the recurrence intervals are scaled with the mean recurrence interval $\overline{\tau }$. These findings are significant under the Kolmogorov–Smirnov test and the Cramér–von Mises test. We show that the empirical estimations are in nice agreement with the numerical integration results for the occurrence probability W_q(Δt|t) of a next event above the threshold q within a (short) time interval after an elapsed time t from the last event above q. We also investigate the memory effects of the recurrence intervals. It is found that the conditional distributions of large and small recurrence intervals differ from each other and the conditional mean of the recurrence intervals scale as a power law of the preceding interval $\overline{\tau }\left({\tau }_0\right)/\overline{\tau }\sim {\left({\tau }_0/\overline{\tau }\right)}^{\beta }$, indicating that the recurrence intervals have short-term correlations. Detrended fluctuation analysis and detrending moving average analysis further uncover that the recurrence intervals possess long-term correlations. We confirm that the “clustering” of the volatility recurrence intervals is caused by the long-term correlations well known to be present in the volatility. Our findings shed new lights on the behavior of large volatilities and have potential implications in risk management of energy futures.     

Maxmin mechanism in a simple common value auction

This paper models the data generating process of common value auctions in a parameter-estimation way, known as the classical approach in statistical inference. Viewing the true value of the object as a parameter that nobody ever knows, we let our value function be the average of the individual estimations (signals) of all agents, a robust estimation of the parameter. Under this simple value function, we select almost uniquely the mechanism that gives the seller the largest portion of the true value in the worst situation among all the direct mechanisms that are feasible, ex-post implementable and individual rational. Our Maxmin mechanism, which randomly assigns the object to one agent, provides the seller (n−1)/n$(n-1)/n$ of the true value when expected revenue is concerned, where n$n$ is the number of the agents.     

Higher order game dynamics

Continuous-time game dynamics are typically first order systems where payoffs determine the growth rate of the players? strategy shares. In this paper, we investigate what happens beyond first order by viewing payoffs as higher order forces of change, specifying e.g. the acceleration of the players? evolution instead of its velocity (a viewpoint which emerges naturally when it comes to aggregating empirical data of past instances of play). To that end, we derive a wide class of higher order game dynamics, generalizing first order imitative dynamics, and, in particular, the replicator dynamics. We show that strictly dominated strategies become extinct in n-th order payoff-monotonic dynamics n   orders as fast as in the corresponding first order dynamics; furthermore, in stark contrast to first order, weakly dominated strategies also become extinct for n?2$n?2$. All in all, higher order payoff-monotonic dynamics lead to the elimination of weakly dominated strategies, followed by the iterated deletion of strictly dominated strategies, thus providing a dynamic justification of the well-known epistemic rationalizability process of Dekel and Fudenberg [7]. Finally, we also establish a higher order analogue of the folk theorem of evolutionary game theory, and we show that convergence to strict equilibria in n-th order dynamics is n orders as fast as in first order.