Forced Choice Measures with an Answer Key: A Predictive Modelling Approach for Detecting Faking Behavior

Forced choice (FC) personality measures are increasingly popular in research and applied contexts. To date however, no method for detecting faking behavior on this format has been both proposed and empirically tested. We introduce a new methodology for faking detection on FC measures, based on the assumption that individuals engaging in faking try to approximate the ideal response on each block of items. Individuals’ responses are scored relative to the ideal using a model for rank-order data not previously applied to FC measures (Generalized Mallows Model). Scores are then used as predictors of faking in a regularized logistic regression. In Study 1, we test our approach using cross-validation, and contrast generic and job-specific ideal responses. Study 2 replicates our methodology on two measures matched and mismatched on item desirability. We achieved between 80 – 92% balanced accuracy in detecting instructed faking, and predicted probabilities of faking correlated with self-reported faking behavior. We discuss how this approach, driven by trying to capture the faking process, differs methodologically and theoretically to existing faking detection paradigms, and measure and context-specific factors impacting accuracy.

Putting a Compass on the Map of Elections

In their AAMAS 2020 paper, Szufa et al. presented a "map of elections" that visualizes a set of 800 elections generated from various statistical cultures. While similar elections are grouped together on this map, there is no obvious interpretation of the elections' positions. We provide such an interpretation by introducing four canonical “extreme” elections, acting as a compass on the map. We use them to analyze both a dataset provided by Szufa et al. and a number of real-life elections. In effect, we find a new parameterization of the Mallows model, based on measuring the expected swap distance from the central preference order, and show that it is useful for capturing real-life scenarios.

Forecasting the Returns of Cryptocurrency: A Model Averaging Approach

This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies’ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive distributed lag (ARDL) model using several popular penalized least squares estimators to explain the cryptocurrencies’ returns. We further introduce a novel model averaging approach or the shrinkage Mallows model averaging (SMMA) estimator for forecasting. First, we find that the returns for most cryptocurrencies are sensitive to volatilities from major financial markets. The returns are also prone to the changes in gold prices and the Forex market’s current and lagged information. Then, when forecasting cryptocurrencies’ returns, we further find that an ARDL(p,q) model estimated by the SMMA estimator outperforms the competing estimators and models out-of-sample.

OPTIMAL MULTISTEP VAR FORECAST AVERAGING

This article proposes frequentist multiple-equation least-squares averaging approaches for multistep forecasting with vector autoregressive (VAR) models. The proposed VAR forecast averaging methods are based on the multivariate Mallows model averaging (MMMA) and multivariate leave-h-out cross-validation averaging (MCVAh) criteria (with h denoting the forecast horizon), which are valid for iterative and direct multistep forecast averaging, respectively. Under the framework of stationary VAR processes of infinite order, we provide theoretical justifications by establishing asymptotic unbiasedness and asymptotic optimality of the proposed forecast averaging approaches. Specifically, MMMA exhibits asymptotic optimality for one-step-ahead forecast averaging, whereas for direct multistep forecast averaging, the asymptotically optimal combination weights are determined separately for each forecast horizon based on the MCVAh procedure. To present our methodology, we investigate the finite-sample behavior of the proposed averaging procedures under model misspecification via simulation experiments.