Labor productivity forecasts based on a Beveridge–Nelson filter: Is there statistical evidence for a slowdown?

In this paper, I assess the evidence for a structural break in labor productivity growth in the years before the Great Recession with the use of out-of-sample forecasting exercises for the years 2010 to 2019 and the recently developed Beveridge–Nelson filter. Models based on a Beveridge–Nelson filter with no structural breaks outperform those allowing for a structural break, and there is statistically significant evidence that they outperform the random walk, though all models were too optimistic about labor productivity growth. Recently developed statistical tests do point to the presence of a structural break before the Great Recession, but uncertainty about the data-generating process for labor productivity growth or the timing and magnitude of the break may be too great to be helpful in forecast preparation.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Model-based control charts in phase 1 statistical process control

In this paper, a general method of constructing control charts for preliminary analysis of individual observations is presented, which is based on recursive score residuals. A simulation study shows that certain implementations of these charts are highly effective in detecting assignable causes.     

A New Approach to Random Utility Modeling using the Dirichlet Multinomial Distribution

In this paper we introduce a new econometricapproach to analyzing recreational site choicedata, the Dirichlet multinomial model. Thismodel, which nests the standard conditionalmultinomial logit model, can accommodateover-dispersed data and may provide moreefficient estimators of coefficients andconsequent welfare measures than the standardconditional logit model, which is so widelyused in the Random Utility Model approach torecreation demand. We illustrate thisDirichlet approach using a data set of rockclimbers in Scotland, and study the impacts onper-trip consumers surplus of alternativemanagement strategies for popular rock climbingsites. Results show that the Dirichletmultinomial approach produces coefficient andwelfare estimates having smaller samplingvariability in this case. We also compareclassical welfare measures with their posteriorequivalents, which allow for welfare changes tobe dis-aggregated.     

A simple random assignment problem with a unique solution

Summary. All agents have the same ordinal ranking over all objects, receiving no object (opting out) may be preferable to some objects, agents differ on which objects are worse than opting out, and the latter information is private. The Probabilistic Serial assignment, improves upon (in the Pareto sense) the Random Priority assignment, that randomly orders the agents and offers them successively the most valuable remaining object. We characterize Probabilistic Serial by efficiency in an ordinal sense, and envy-freeness. We characterize it also by ordinal efficiency, strategyproofness and equal treatment of equals. Received: October 5, 1999; revised version: December 20, 2000     

Probability of survival in a random exchange economy with dependent agents

Summary. In this paper I analyze the general equilibrium in a random Walrasian economy. Dependence among agents is introduced in the form of dependency neighborhoods. Under the uncertainty, an agent may fail to survive due to a meager endowment in a particular state (direct effect), as well as due to unfavorable equilibrium price system at which the value of the endowment falls short of the minimum needed for survival (indirect terms-of-trade effect). To illustrate the main result I compute the stochastic limit of equilibrium price and probability of survival of an agent in a large Cobb-Douglas economy. Received June 7, 2001; revised version: January 7, 2002 RID="*" ID="*" I would like to thank Mukul Majumdar and Thomas DiCiccio for helpful discussion and an anonymous referee for valuable comments and suggestions.     

Price discrimination and efficient matching

This paper considers the problem of a monopoly matchmaker that uses a schedule of entrance fees to sort different types of agents on the two sides of a matching market into exclusive meeting places, where agents randomly form pairwise matches. We make the standard assumption that the match value function exhibits complementarities, so that matching types at equal percentiles maximizes total match value and is efficient. We provide necessary conditions and sufficient conditions for the revenue-maximizing sorting to be efficient. These conditions require the match value function, modified to incorporate the incentive cost of eliciting private type information, to exhibit complementarities in types.JEL Clalssification Numbers C7, D4We thank Jonathan Levin, Tracy Lewis, and the seminar audience at University of California at Los Angeles, University of British Columbia, Duke University, and 2003 Econometric Society North American Summer Meeting for comments and suggestions. We are also grateful for helpful suggestions from the referees and the Editor     

既有结构可靠度的模糊算法

基于结构抗力和荷载效应的不确定性，提出了一种既有结构工程可靠度的模糊算法。该方法利用模糊算法可以反映客观事物因素的层次性，又便于比较和考虑各层次中诸因素作用的大小以决定可靠度。算例结果表明该方法简单实用，其模型可以很好地拟合真实的极限状态函数，是解决既有结构体系可靠度问题的有效方法。     

On the forecastability of share prices on the Viennese stock exchange

In this paper the Viennese stock exchange data are analysed by using ARMA and GARCH technology. After using AIC and BIC for estimating the linear structure of the time series, to the resulting innovations a GARCH(1,1) model is fit. The resulting residuals are then tested for serial independence and constancy of its distribution to check whether the models are reasonable. Main result is that the residuals of this ARMA-GARCH(1,1)-model are reasonably iid (which is checked by BDS and classical independence tests) for index data and significantly less well-behaved for stock data. Second, there is considerable autocorrelation in the data (especially in the Viennese indices WBK and ATX) which can be exploited even with 1.25% transaction costs (which is checked by a posteriori analysis of a strategy which exploits an underlying time-varying AR(1) model), however, much higher profit can be made with 0.5% transaction costs. Furthermore, the same techniques are applied to US Standard & Poor 500 index and the results for both data sets are compared giving the result that the US-market looks much more mature than the Viennese one.Financial Support by the Institute for Advanced Studies, Vienna, and the Fonds zur Förderung der wissenschaftlichen Forschung, Vienna, Grant P 9176 is gratefully acknowledged. This paper is a slightly abbreviated version of the Research Report No. 135 by the same authors (see References), which contains many detailed plots of the results.     

Money,random matching and endogenous growth: A quantitative analysis

In this study, we develop a search-and-matching monetary growth model to analyze the effects of inflation on economic growth and social welfare by introducing endogenous economic growth via capital externality into a two-sector search-and-matching model. We find that the channel through which inflation affects economic growth in the search-and-matching model is different from the traditional cash-in-advance model. To facilitate the calibration, we obtain an empirical estimate of the effects of inflation on economic growth using panel regressions. In the simulation analysis, we quantitatively evaluate the welfare effect of inflation in the search-and-matching endogenous growth model and compare it to a search-and-matching exogenous growth model. We find that the welfare effect of inflation is nonlinear in the endogenous growth model whereas it is linear in the exogenous growth model. Furthermore, we find that the welfare cost of inflation under endogenous growth is up to four times as large as the welfare cost of inflation under exogenous growth.