Okun’s law, asymmetries and jobless recoveries in the United States: A Markov-switching approach

This paper offers a new perspective on Markov regime-switching approaches to asymmetries in Okun’s law by modeling the existing approaches as special cases. Prevailing models assume either asymmetry between unemployment and output across regimes or asymmetry within a single regime. Our specification combines both approaches. Our empirical results give an insight into the apparent ‘jobless recovery’ experiences that began in the United States in 1991 and 2001

Business confidence and cyclical turning points: A Markov-Switching approach

Markov regime-switching analysis is used to consider the relationship between business confidence and the probability of turning points in cyclical GDP. We find, in an application to New Zealand, that confidence is related to both the deepness and duration of the business cycle and is asymmetric regarding the probability of the economy remaining in a given regime. Overall, the New Zealand business confidence series is a useful indicator of cyclical turning points

Beef feed storage from an engineering perspective

There is a large potential and actual feed loss in the beef industry. There are engineering solutions to some of the causes of feed loss but those solutions can have capital investment and annual cost requirements. To keep a beef operation economically viable, costs incurred must be recovered by savings. Table 1 can be used to estimate the value lost per ton of hay when an estimated loss of dry matter occurs. One way to use this table is to assume a certain practice can save 10 % dry matter loss when hay is valued at $150/ton. Under this condition,$15/ton of hay can be saved. Whatever system saves $15/ ton, it must not cost more than$15/ton to implement for it to be a viable alternative

A Monte Carlo Comparison of Robust MANOVA Test Statistics

Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform well with respect to Type I error and power. There are several alternative test statistics that can be considered including robust statistics and the use of the structural equation modeling (SEM) framework. This simulation study focused on comparing the performance of the P test statistics with fifteen other test statistics across seven manipulated factors. These statistics were evaluated across 12,076 different conditions in terms of Type I error and power. Results suggest that when assumptions were met, the standard MANOVA test functioned well. However, when assumptions were violated, it performed poorly, whereas several of the alternatives performed better. Discussion focuses on advice for selecting alternatives in practice. This study’s focus on all these in one simulation and the 3 group case should be helpful to the practitioner making methodological sections

Factorial Invariance Testing under Different Levels of Partial Loading Invariance within a Multiple Group Confirmatory Factor Analysis Model

Scalar invariance in factor models is important for comparing latent means. Little work has focused on invariance testing for other model parameters under various conditions. This simulation study assesses how partial factorial invariance influences invariance testing for model parameters. Type I error inflation and parameter bias were observed

Using Exploratory Factor Analysis for Locating Invariant Referents in Factor Invariance Studies

Model identification in multi-group confirmatory factor analysis (MCFA) requires an equality constraint of referent variables across groups. Invariance assumption violations make it difficult to locate parameters that actually differ. Suggested procedures for locating invariant referents are cumbersome, complex, and provide imperfect results. Exploratory factor analysis (EFA) may be an alternative because of its ease of use, yet empirical evaluation of its effectiveness is lacking. EFAs accuracy for distinguishing invariant from non-invariant referents was examined