How efficiently do scientific results make their way into the wider world? Applying multiple methods to the universe of hypothesis tests reported in three leading health journals between 2016 and 2022 we evidence the important role of statistical significance as a driver of popular attention to...

This paper focuses on the estimation and testing of multiple breaks that occur at unknown dates in multivariate long memory time series regression models, allowing for fractional cointegration. A likelihood-ratio based approach for estimating the breaks in the parameters and in the covariance of...

Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions. We introduce a novel inference method without any prior information about which conditioning instruments are weak or...

Publication practices in the social sciences act as a filter that favors statistically significant results over null findings. While the problem of selection on significance (SoS) is well-known in theory, it has been difficult to measure its scope empirically, and it has been challenging to...

We present the R package MSTest, which implements hypothesis testing procedures to determine the number of regimes in Markov switching models. These models have wide ranging applications in economics, finance, and many other fields. MSTest provides several testing frameworks, including Monte...

The key assumption of normally distributed error terms is usually not tested in empirical practice when using ordered probit models. Therefore, an artificial regression version of the LM test against the class of Pearson distributions is derived that can be implemented more easily than the...

Summarizing Data -- Populations, Samples, and Inference -- Modeling and Inference: A Big Picture -- Nonparametric Estimation of the CDF and the Plug-In Principle -- The Jackknife Method -- The Bootstrap Method -- The Method of Moments -- The Method of Maximum Likelihood -- Hypothesis Testing: A...

Bayesian decisions are observationally identical to decisions with judgment. Decisions with judgment test whether a judgmental decision is optimal and, in case of rejection, move to the closest boundary of the confidence interval, for a given confidence level. The resulting decisions condition...

We consider fully modified least squares estimation for systems of cointegrating polynomial regressions, i. e., systems of regressions that include deterministic variables, integrated processes and their powers as regressors. The errors are allowed to be correlated across equations, over time...

This paper focuses on the estimation and testing of multiple breaks that occur at unknown dates in multivariate long memory time series regression models, allowing for fractional cointegration. A likelihood-ratio based approach for estimating the breaks in the parameters and in the covariance of...

How efficiently do scientific results make their way into the wider world? Applying multiple methods to the universe of hypothesis tests reported in three leading health journals between 2016 and 2022 we evidence the important role of statistical significance as a driver of popular attention to...

How efficiently do scientific results make their way into the wider world? Applying multiple methods to the universe of hypothesis tests reported in three leading health journals between 2016 and 2022 we evidence the important role of statistical significance as a driver of popular attention to...

Forecasting natural gas demand is critical for enhancing energy efficiency, optimizing infrastructure planning, and supporting Tunisia's transition toward sustainable energy. As the fastest-growing fossil fuel and one of the cleanest among non-renewable energy sources, natural gas plays a...

This paper presents a distribution-free test, based on the permutation approach, on treatment effects with a multivariate categorical response variable. The motivating example is a typical case-control biomedical study, performed to investigate the effect of the treatment called "assisted motor...

In statistical analysis, evaluating the normality of large datasets is crucial for validating parametric tests, particularly in areas such as Global Navigation Satellite System (GNSS) measurements, where data often exhibit non-normal characteristics resulting from their variability and errors....

This paper studies the limiting behavior of the test for instrument exogeneity in linear models when there is uncertainty about the strength of the identification signal. We consider the test for conditional moment restrictions with an expanding set of constructed instruments. We establish the...

We develop a test for mean stationarity of latent volatility curves using high-frequency data. To derive the asymptotic test size and power, we establish a functional invariance principle for semimartingales under a strong mixing condition. The power properties are analyzed under alternatives...

In this paper, a beta transform of multivariate normal datasets is obtained. The phi divergence measure, DΦ(F,G) between two distributions F and G is used to obtain a goodnessof-fit test to multivariate normality (MVN) based on the theoretical density function of the beta transformed random...