Evaluating Asset-Pricing Models Using The Hansen-Jagannathan Bound: A Monte Carlo Investigation

We conduct Monte Carlo experiments to examine whether the bound proposed by Hansen and Jagannathan (1991) is a useful device for evaluating asset pricing models. Specifically, we use recently developed statistical tests, which are based on a 'distance' between the model and the Hansen-Jagannathan bound, to compute the rejection rates of true models. We provide finite-sample critical values for asset pricing models with time separable preferences, and show how they depend upon nuisance parameters—risk aversion and the rate of time preference. Further, we show that the finite-sample distribution of the test statistic associated with the risk-neutral case is extreme, in the sense that critical values based on this distribution will deliver type I errors no larger than intended—regardless of risk aversion or the rate of time preference. Extending the analysis to accommodate other preferences, we show that in the state non-separable case, the small-sample distributions of the test statistics are influenced significantly by the degree of intertemporal substitution, but not by attitudes toward risk. For habit formation preferences, the small-sample distributions are strongly influenced by the habit parameter. However, the maximal-size critical values for time-separable preferences are appropriate for habit formation as well as state non-separable preferences. We conclude that with these critical values the HJ bound is indeed a useful evaluation device. We then use the critical values to evaluate three asset pricing models using U.S. data. We find evidence against the time-separable model and mixed evidence on the remaining two models.

Few are as Good as Many: An Ontology-Based Tweet Spam Detection Approach

Due to the high popularity of Twitter, spammers tend to favor its use in spreading their commercial messages. In the context of detecting twitter spams, different statistical and behavioral analysis approaches were proposed. However, these techniques suffer from many limitations due to (1) ongoing changes to Twitter\u2019s streaming API which constrains access to a user\u2019s list of followers/followees, (2) spammer\u2019s creativity in building diverse messages, (3) use of embedded links and new accounts, and (4) need for analyzing different characteristics about users without their consent. To address the aforementioned challenges, we propose a novel ontology-based approach for spam detection over Twitter during events by analyzing the relationship between ham user tweets vs. spams. Our approach relies solely on public tweet messages while performing the analysis and classification tasks. In this context, ontologies are derived and used to generate a dictionary that validates real tweet messages from random topics. Similarity ratio among the dictionary and tweets is used to reflect the legitimacy of the messages. Experiments conducted on real tweet data illustrate that message-to-message techniques achieved a low detection rate compared to our ontology based approach which outperforms them by approximately 200%, in addition to promising scalability for large data analysis

Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa

This paper designs and implements a Bayesian dynamic latent factor model for a vector of data describing the Iowa economy. Posterior distributions of parameters and the latent factor are analyzed by Markov Chain Monte Carlo methods, and coincident and leading indicators are computed by using posterior mean values of current and predictive distributions for the latent factor. JEL Codes: C11, C32, E32. Keywords: Markov chain, Monte Carlo, index model, latent dynamic factor Running Head: Bayesian Leading Indicators * Manuscript received September 1996; revised December 1997. 1 This paper was initially prepared for presentation at the July 1996 NBER/NSF Seminar on Forecasting and Empirical Methods in Macroeconomics. We thank Francis Diebold, Robert Engle, John Geweke, Beth Ingram, Thomas Sargent, Christopher Sims, James Stock, Ruey Tsay, Mark Watson, and three anonymous referees for helpful comments. Sid Chib graciously supplied us with GAUSS code for posterior analysis of regression mo..

Baynesian Leading Indicators: Measuring and Predicting Economic Conditions

This paper designs and implements a Bayesian dynamic latent factor model for a vector of data describing the Iowa economy. Posterior distributions of parameters and the latent factor are analyzed by Markov Chain Monte Carlo methods, and coincident and leading indicators are given by posterior mean values of current and predictive distributions for the latent factor.