Social and Behavioral Sciences Commons^™

Macroconstants Of Development: A New Benchmark For The Strategic Development Of Advanced Countries And Firms, Andrey Bystrov, Vyacheslav Yusim, Tamilla Curtis

Dr. Tamilla Curtis

This research proposed a new indicator of countries’ development called “macroconstants of development”. The literature review indicates that the concept of "macroconstants of development" is not used at the moment in neither the theory nor the practice of industrial policy. Research of longitudinal data of total GDP, GDP per capita and their derivatives for most countries of the world was conducted. An analysis of statistical information has been done by employing econometric analyses.

Based on the analysis of the statistical data, which characterizes the development of large, technologically advanced countries in ordinary conditions, it was identified that the average acceleration …

Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar

Adrian Gepp

Financial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting - edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This …

The Gambler's Fallacy: A Test Of Football-Betting Market Efficiency, Ladd Kochman, Ravija Badarinathi

Ladd Kochman

Imaginary wagers placed on college football teams during the 2006-2010 seasons that were expected to beat the point spread following two games in which they lost both on the field and against the spread produced a wins-to-bets ratio that was statistically nonrandom but not profitable. However, when that rule was limited to the major conference schools, a significantly profitable W/B ratio emerged that challenges the efficiency of a competitive market.

Dogs No Longer Man's Best Friend: A Test Of Football Market Efficiency, Ladd Kochman

Ladd Kochman

The outcomes of wagers on underdogs in the National Football League for the 2003-2007 seasons indicated that what had been anomalous behavior no longer existed. The failure of underdogs to beat the spread in profitable or nonrandom fashion supports the argument that competitive markets are efficient and undermines the proposition that behavioral finance can illuminate exploitable betting patterns.

Revisiting The Streaking Teams Phenomenom: A Note, Ladd Kochman, Randy Goodwin

Ladd Kochman

In an effort to learn if systematic misperceptions by market participants can undermine efficient prices and create regular profit opportunities, Camerer (1989) and Brown and Sauer (1993) investigated whether participants in the basketball-betting market overbet streaking (or "hot") teams. The purpose of this note is determine whether streaking teams - both hot and cold-in college football alter point spreads to an exploitable degree. The pointwise outcomes of college football teams following 2-, 3-, 4-, 5-, 6-, 7-, 8-, and 9-game streaks during the 1996-2000 seasons. Streaks in the aggregate produced only breakeven results when used to predict the outcomes of …

An Introduction To Item Response Theory For Health Behavior Researchers, Russell Warne

Russell T Warne

OBJECTIVE:

To introduce item response theory (IRT) to health behavior researchers by contrasting it with classical test theory and providing an example of IRT in health behavior.

METHOD:

Demonstrate IRT by fitting the 2PL model to substance-use survey data from the Adolescent Health Risk Behavior questionnaire (n=1343 adolescents).

RESULTS:

An IRT 2PL model can produce viable substance use scores that differentiate different levels of substance use, resulting in improved precision and specificity at the respondent level.

CONCLUSION:

IRT is a viable option for health researchers who want to produce high-quality scores for unidimensional constructs. The results from our example-although not …

Estimating Confidence Intervals For Eigenvalues In Exploratory Factor Analysis, Ross Larsen, Russell Warne

Russell T Warne

Exploratory factor analysis (EFA) has become a common procedure in educational and psychological research. In the course of performing an EFA, researchers often base the decision of how many factors to retain on the eigenvalues for the factors. However, many researchers do not realize that eigenvalues, like all sample statistics, are subject to sampling error, which means that confidence intervals (CIs) can be estimated for each eigenvalue. In the present article, we demonstrate two methods of estimating CIs for eigenvalues: one based on the mathematical properties of the central limit theorem, and the other based on bootstrapping. References to appropriate …

The 1905 Einstein Equation In A General Mathematical Analysis Model Of Quasars, Byron E. Bell

Byron E. Bell

No abstract provided.

Mixture Of Random Effects For Individual Learning Curves, Sally Wood, Edward Cripps, Robert Wood

Sally Wood

In the pyschology literature individuals are often classified as entity theorists or incrementalists. In this paper we explore the different learning behaviours over time of these two groups. To assess learning an individual is assigned a task and their performance on the task is measured over a number of trials. Learning behaviour is modelled as a mixture of two random effects, where the random effects components of the mixture correspond to increased learning and spiralling behaviour. We find significant differences in the learning behaviours of the two groups. Specifically those individuals who are categorized as entity theorists are more likely …

Bayesian Identification, Selection And Estimation Of Functions In High-Dimensional Additive Models, Anastasios Panagiotelis, Michael Smith

Michael Stanley Smith

In this paper we propose an approach to both estimate and select unknown smooth functions in an additive model with potentially many functions. Each function is written as a linear combination of basis terms, with coefficients regularized by a proper linearly constrained Gaussian prior. Given any potentially rank deficient prior precision matrix, we show how to derive linear constraints so that the corresponding effect is identified in the additive model. This allows for the use of a wide range of bases and precision matrices in priors for regularization. By introducing indicator variables, each constrained Gaussian prior is augmented with a …

Bayesian Density Forecasting Of Intraday Electricity Prices Using Multivariate Skew T Distributions, Anastasios Panagiotelis, Michael Smith

Michael Stanley Smith

Electricity spot prices exhibit strong time series properties, including substantial periodicity, both inter-day and intraday serial correlation, heavy tails and skewness. In this paper we capture these characteristics using a first order vector autoregressive model with exogenous effects and a skew t distributed disturbance. The vector is longitudinal, in that it comprises observations on the spot price at intervals during a day. A band two inverse scale matrix is employed for the disturbance, as well as a sparse autoregressive coefficient matrix. This corresponds to a parsimonious dependency structure that directly relates an observation to the two immediately prior, and the …

A Mathematical Regression Of The U.S. Gross Private Domestic Investment 1959-2001, Byron E. Bell

Byron E. Bell

SUMMARY OF PROJECT What did I do? A study of the role the U.S. stock markets and money markets have possibly played in the Gross Private Domestic Investment (GPDI) of the United States from the year 1959 to the year 2001 and I created a Multiple Linear Regression Model (MLRM).

Foreign Exchange Intervention By The Bank Of Japan: Bayesian Analysis Using A Bivariate Stochastic Volatility Model, Michael Smith, Andrew Pitts

Michael Stanley Smith

A bivariate stochastic volatility model is employed to measure the effect of intervention by the Bank of Japan (BOJ) on daily returns and volume in the USD/YEN foreign exchange market. Missing observations are accounted for, and a data-based Wishart prior for the precision matrix of the errors to the transition equation that is in line with the likelihood is suggested. Empirical results suggest there is strong conditional heteroskedasticity in the mean-corrected volume measure, as well as contemporaneous correlation in the errors to both the observation and transition equations. A threshold model is used for the BOJ reaction function, which is …

Inferring Information Frequency And Quality, Douglas G. Steigerwald, John Owens

Douglas G. Steigerwald

We develop a microstructure model that, in contrast to previous models, allows one to estimate the frequency and quality of private information. In addition, the model produces stationary asset price and trading volume series. We find evidence that information arrives frequently within a day and that this information is of high quality. The frequent arrival of information, while in contrast to previous microstructure model estimates, accords with nonmodel-based estimates and the related literature testing the mixture-of-distributions hypothesis. To determine if the estimates are correctly reflecting the arrival of latent information, we estimate the parameters over half-hour intervals within the day. …

Bayesian Modelling And Forecasting Of Intra-Day Electricity Load, Remy Cottet, Michael Smith

Michael Stanley Smith

With the advent of wholesale electricity markets there has been renewed focus on intra-day electricity load forecasting. This paper employs a multi-equation regression model with a diagonal first order stationary vector autoregresson (VAR) for modeling and forecasting intra-day electricity load. The correlation structure of the disturbances to the VAR and the appropriate subset of regressors are explored using Bayesian model selection methodology. The full spectrum of finite sample inference is obtained using a Bayesian Markov chain Monte Carlo sampling scheme. This includes the predictive distribution of load and the distribution of the time and level of daily peak load, something …