scholarly journals On the Validity of Tests for Asymmetry in Residual-Based Threshold Cointegration Models

Econometrics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 12
Author(s):  
Karl-Heinz Schild ◽  
Karsten Schweikert
Keyword(s):  
Empirical Studies ◽  
Small Sample ◽  
Sufficient Evidence ◽  
Speed Of Adjustment ◽  
Conditional Least Squares ◽  
Long Run ◽  
Size Distortions ◽  
Small Sample Sizes ◽  
Validity Of Tests ◽  
Correct Size

This paper investigates the properties of tests for asymmetric long-run adjustment which are often applied in empirical studies on asymmetric price transmissions. We show that substantial size distortions are caused by preconditioning the test on finding sufficient evidence for cointegration in a first step. The extent of oversizing the test for long-run asymmetry depends inversely on the power of the primary cointegration test. Hence, tests for long-run asymmetry become invalid in cases of small sample sizes or slow speed of adjustment. Further, we provide simulation evidence that tests for long-run asymmetry are generally oversized if the threshold parameter is estimated by conditional least squares and show that bootstrap techniques can be used to obtain the correct size.

Empirical Studies of Transferability of Helsinki Metropolitan Area Travel Forecasting Models

1997 ◽  
Vol 1607 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Nina Karasmaa ◽  
Matti Pursula
Keyword(s):  
Metropolitan Area ◽  
Model Updating ◽  
Statistical Tests ◽  
Empirical Studies ◽  
Estimation Procedure ◽  
Small Sample ◽  
Distribution Models ◽  
Travel Forecasting ◽  
The Difference ◽  
Small Sample Sizes

The temporal transferability of mode choice and trip distribution models was studied by using the data based on traffic surveys in the Helsinki, Finland, metropolitan area in 1981 and 1988. The updating procedures examined were the Bayesian updating, combined transfer estimation, transfer scaling, and joint context estimation procedures. The results of model updating indicated that finding the correct method and sample size for each case is not an unambiguous task. The best method depends on the difference in model coefficients between the initial and the final stages as well as the quality of the data. According to the statistical tests, no differences could be discerned between the models at all. However, the sample enumeration test proved that the models’ ability to predict changes in behavior can vary greatly according to the method used. On the basis of this research the transfer scaling seems to be the method best suited for simple models. In particular, the method is quite useful if the transfer bias is large. The combined transfer estimation procedure performs best when there is a great number of observations and the transfer bias is small. With small sample sizes the Bayesian approach and the joint context estimation give the best results.

scholarly journals Including Data Analytical Stability in Cluster-based Inference

2019 ◽  
Author(s):  
Sanne P. Roels ◽  
Tom Loeys ◽  
Beatrijs Moerkerke
Keyword(s):  
Selection Process ◽  
Group Analysis ◽  
Testing Procedure ◽  
Small Sample ◽  
Sufficient Evidence ◽  
Analytical Stability ◽  
Human Connectome Project ◽  
Small Sample Sizes ◽  
Brain Data ◽  
Project Data

AbstractIn the statistical analysis of functional Magnetic Resonance Imaging (fMRI) brain data it remains a challenge to account for simultaneously testing activation in over 100.000 volume units or voxels. A popular method that reduces the dimensionality of this test problem is cluster-based inference. We propose a new testing procedure that allows to control the family-wise error (FWE) rate at the cluster level but improves cluster-based test decisions in two ways by (1) taking into account a measure for data analytical stability and (2) allowing a more voxel-based interpretation of the results. For each voxel, we define the re-selection rate conditional on a given FWE-corrected threshold and use this rate, which is a measure of stability, into the selection process. In our procedure, we set a more liberal and a more conservative FWE controlling threshold. Clusters that survive the liberal but not the conservative threshold are retained if sufficient evidence for voxelwise stability is available. Cluster that survive the conservative threshold are retained anyhow, and clusters that do not survive the liberal threshold are not further considered. Using the Human Connectome Project Data (Van Essen et al., 2012), we demonstrate how in a group analysis our method results not only in a higher number of selected voxels but also in a larger overlap between different test images. Additionally, we demonstrate the ability of our procedure to control the FWE, also in relatively small sample sizes.

scholarly journals Limits to Genomic Divergence Under Sexually Antagonistic Selection

2019 ◽  
Author(s):  
Katja R. Kasimatis ◽  
Peter L. Ralph ◽  
Patrick C. Phillips
Keyword(s):  
Genetic Model ◽  
Empirical Studies ◽  
Genetic Load ◽  
Small Sample ◽  
Sampling Variance ◽  
Antagonistic Selection ◽  
Genome Wide ◽  
Sexually Antagonistic Selection ◽  
Small Sample Sizes ◽  
The Relationship

AbstractSince the autosomal genome is shared between the sexes, sex-specific fitness optima present an evolutionary challenge. While sexually antagonistic selection might favor different alleles within females and males, segregation randomly reassorts alleles at autosomal loci between sexes each generation. This process of homogenization during transmission thus prevents between-sex allelic divergence generated by sexually antagonistic selection from accumulating across multiple generations. However, recent empirical studies have reported high male-female FST statistics. Here, we use a population genetic model to evaluate whether these observations could plausibly be produced by sexually antagonistic selection. To do this, we use both a single-locus model with nonrandom mate choice, and individual-based simulations to study the relationship between strength of selection, degree of between-sex divergence, and the associated genetic load. We show that selection must be exceptionally strong to create measurable divergence between the sexes and that the decrease in population fitness due to this process is correspondingly high. Individual-based simulations with selection genome-wide recapitulate these patterns and indicate that small sample sizes and sampling variance can easily generate substantial male-female divergence. We therefore conclude that caution should be taken when interpreting autosomal allelic differentiation between the sexes.

Internal migration in Italy. Long-run analysis of push and pull factors across regions and macro-areas of the country.

2016 ◽  
Vol 13 (3) ◽  
pp. 443-454
Author(s):  
Piras Romano
Keyword(s):  
Gravity Model ◽  
Internal Migration ◽  
Empirical Studies ◽  
Great Majority ◽  
Pull Factors ◽  
Push And Pull Factors ◽  
Long Run ◽  
Italian Regions ◽  
Migration Flows ◽  
Push And Pull

The great majority of empirical studies on internal migration across Italian regions either ignores the long-run perspective of the phenomenon or do not consider push and pull factors separately. In addition, Centre-North to South flows, intra-South and intra-Centre-North migration have not been studied. We aim to fill this gap and tackle interregional migration flows from different geographical perspectives. We apply four panel data estimators with different statistical assumptions and show that long-run migration flows from the Mezzogiorno towards Centre-Northern regions are well explained by a gravity model in which per capita GDP, unemployment and population play a major role. On the contrary, migration flows from Centre-North to South has probably much to do with other social and demographic factors. Finally, intra Centre-North and intra South migration flows roughly obey to the gravity model, though not all explicative variables are relevant.

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

2018 ◽  
Author(s):  
Christopher Chabris ◽  
Patrick Ryan Heck ◽  
Jaclyn Mandart ◽  
Daniel Jacob Benjamin ◽  
Daniel J. Simons
Keyword(s):  
Null Hypothesis ◽  
Small Sample ◽  
Sample Sizes ◽  
Double Blind ◽  
Bayesian Analyses ◽  
Physical Warmth ◽  
Small Sample Sizes ◽  
Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

scholarly journals Non-Invasive Monitoring of Adrenocortical Activity in Three Sympatric Desert Gerbil Species

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Álvaro Navarro-Castilla ◽  
Mario Garrido ◽  
Hadas Hawlena ◽  
Isabel Barja
Keyword(s):  
Small Sample ◽  
Post Injection ◽  
Control Groups ◽  
Treatment Groups ◽  
Adrenocortical Activity ◽  
Non Invasive ◽  
Endocrine Status ◽  
Corticosterone Metabolites ◽  
Small Sample Sizes ◽  
Fecal Corticosterone Metabolites

The study of the endocrine status can be useful to understand wildlife responses to the changing environment. Here, we validated an enzyme immunoassay (EIA) to non-invasively monitor adrenocortical activity by measuring fecal corticosterone metabolites (FCM) in three sympatric gerbil species (Gerbillus andersoni, G. gerbillus and G. pyramidum) from the Northwestern Negev Desert’s sands (Israel). Animals included into treatment groups were injected with adrenocorticotropic hormone (ACTH) to stimulate adrenocortical activity, while control groups received a saline solution. Feces were collected at different intervals and FCM were quantified by an EIA. Basal FCM levels were similar in the three species. The ACTH effect was evidenced, but the time of FCM peak concentrations appearance differed between the species (6–24 h post-injection). Furthermore, FCM peak values were observed sooner in G. andersoni females than in males (6 h and 18 h post-injection, respectively). G. andersoni and G. gerbillus males in control groups also increased FCM levels (18 h and 48 h post-injection, respectively). Despite the small sample sizes, our results confirmed the EIA suitability for analyzing FCM in these species as a reliable indicator of the adrenocortical activity. This study also revealed that close species, and individuals within a species, can respond differently to the same stressor.

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