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We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

Diagnosis of Huntington's disease (HD) is with genetic tests and predictive testing for HD has been available for almost two decades. In the age of advancing genetic techniques, the question arises as to how the predictive tests can affect a person, his or her family and relatives, life choices and future. The paper aims to discuss these issues.

A case study is presented demonstrating the complex issues surrounding genetic testing in HD. Relevant literature was then reviewed to further explore ethical issues linked to predictive testing for HD and also looked into findings on resolving this complex issue.

Predictive testing in HD gives rise to ethical issues in social, legal, economical and imperatively personal aspects of an individual and society. Education and dispersion of knowledge to general society, regarding the test, its impact and also the illness would be a starting point in an attempt to resolve these issues. Need for counselling and support for patients in this context is vital and hence the imperative need to ensure provisions for standardised training and supply of professionals in this setting. Universal and enforceable framework along the lines of International Huntington Association recommendation should be adopted nationally.

This paper presents a case study with significant value in demonstrating the challenges faced by genetic testing in HD, and provides insight in to this issue significant for all clinicians.

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

In this chapter we provide analytical and Monte Carlo evidence that Chow and Predictive tests can be consistent against alternatives that allow structural change to occur at either end of the sample. Attention is restricted to linear regression models that may have a break in the intercept. The results are based on a novel reparameterization of the actual and potential break point locations. Standard methods parameterize both of these locations as fixed fractions of the sample size. We parameterize these locations as more general integer-valued functions. Power at the ends of the sample is evaluated by letting both locations, as a percentage of the sample size, converge to 0 or 1. We find that for a potential break point function, the tests are consistent against alternatives that converge to 0 or 1 at sufficiently slow rates and are inconsistent against alternatives that converge sufficiently quickly. Monte Carlo evidence supports the theory though large samples are sometimes needed for reasonable power.

This chapter develops a predictive approach to Granger causality (GC) testing that utilizes k -fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post- sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on GC between inflation and unemployment rates.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.

Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.

This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.

The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.

Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.

This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.

The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.

There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.

With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.

Predictive genetic tests (PGTs) have greatly increased their presence in the market, and, much like their pharmaceutical peers, companies offering PGTs have increasingly used direct‐to‐consumer advertising as part of their promotional strategy. Given many PGTs are available without a prescription or physician order and the lack of empirical research examining the effects of PGT‐DTC, this paper seeks to examine consumer attitudes, intentions, and behavior in response to a PGT‐DTC ad with and without a prescription requirement.

A single factor, between subjects online survey design with the presence or absence of a prescription requirement as the experimental variable was used to evaluate consumers' attitudes, intentions, and behavior in response to a predictive genetic test DTC advertisement. A minimum sample size of 198 was determined a priori and 206 surveys were completed within five hours of deployment to 600 randomly selected general consumer participants for a response rate of 34.3 percent (206/600), with 106 in the prescription requirement group and 100 in the non‐prescription group. Descriptive statistics, t‐tests, and chi‐square techniques were used to examine the various dependent variables (consumer attitudes, behavioral intentions, and the pre‐defined behavior measure) and their differences.

Overall, consumers hold favorable attitudes to PGT‐DTC ads, but did not intend to engage in physician discussion, take the test or perform information search behavior. The effect of a prescription requirement was not significant, as no differences were seen with the attitude and behavioral intention dependent variables.

At this still relatively young point in the PGT cycle, consumers still seem to be skeptical about the value of predictive genetic tests and their associated DTC advertisements.