The Long-Run Accuracy of Heuristics: Covariation Assessment, Bayesian Inference, and Hypothesis Testing

1991 ◽  
Author(s):  
Craig R. M. McKenzie
Keyword(s):  
Bayesian Inference ◽  
Hypothesis Testing ◽  
Long Run

Hypothesis testing with error correction models

2021 ◽  
pp. 1-9
Author(s):  
Patrick W. Kraft ◽  
Ellen M. Key ◽  
Matthew J. Lebo
Keyword(s):  
Hypothesis Testing ◽  
Error Correction ◽  
Null Hypothesis ◽  
Alternative Hypothesis ◽  
Correction Coefficient ◽  
Correction Model ◽  
Long Run ◽  
Independent Variable ◽  
The Right ◽  
General Error

Abstract Grant and Lebo (2016) and Keele et al. (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coefficient, $\alpha _1^\ast$ , to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on $\alpha _1^\ast$ is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x's. A significant $\alpha _1^\ast$ can occur when some I(1) regressors are not cointegrated and the equation is not balanced. Thus, the correct limiting distributions of the right-hand-side long-run coefficients may be unknown. We use simulations to demonstrate the problem and then discuss implications for applied examples.

scholarly journals Bayesian Inference and Testing Any Hypothesis You Can Specify

2018 ◽  
Vol 1 (2) ◽  
pp. 281-295 ◽  
Author(s):  
Alexander Etz ◽  
Julia M. Haaf ◽  
Jeffrey N. Rouder ◽  
Joachim Vandekerckhove
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Hypothesis Testing ◽  
Likelihood Ratio ◽  
Special Form ◽  
Null Hypothesis ◽  
Bayes Factor ◽  
Alternative Hypotheses ◽  
Competing Models

Hypothesis testing is a special form of model selection. Once a pair of competing models is fully defined, their definition immediately leads to a measure of how strongly each model supports the data. The ratio of their support is often called the likelihood ratio or the Bayes factor. Critical in the model-selection endeavor is the specification of the models. In the case of hypothesis testing, it is of the greatest importance that the researcher specify exactly what is meant by a “null” hypothesis as well as the alternative to which it is contrasted, and that these are suitable instantiations of theoretical positions. Here, we provide an overview of different instantiations of null and alternative hypotheses that can be useful in practice, but in all cases the inferential procedure is based on the same underlying method of likelihood comparison. An associated app can be found at https://osf.io/mvp53/ . This article is the work of the authors and is reformatted from the original, which was published under a CC-By Attribution 4.0 International license and is available at https://psyarxiv.com/wmf3r/ .

ASSESSING THE LINKAGES BETWEEN THE ELECTRICITY CONSUMPTION AND ECONOMIC FACTORS IN A LANDLOCKED ECONOMY: CASE STUDY OF LAO PDR

2020 ◽  
pp. 1-25
Author(s):  
NISIT PANTHAMIT ◽  
CHUKIAT CHAIBOONSRI
Keyword(s):  
Bayesian Inference ◽  
Lao Pdr ◽  
Energy Resource ◽  
Electricity Consumption ◽  
Energy System ◽  
Relationship Analysis ◽  
Explanatory Factors ◽  
Long Run ◽  
Short Run

This research paper aims to investigate linkages of electricity consumption representing energy security with estimated factors — GDP, population and foreign direct investment (FDI) during 1998–2018 for Laos People Democratic Republic (Lao PDR) by using ARDLbased Bayesian inference. This study provided empirical evidence on a long-run linear relationship analysis under ARDL-based Bayesian inference, which concludes that they have performed real relationships between electricity consumption, GDP, population and FDI. In addition, in the short-run, it was found that explanatory factors have both negative and positive impacts on Laos’ electricity consumption. The results confirm the hypothesis that although Lao PDR has access to domestic energy resources, only relying on one energy resource will make the energy system insecure. Thus, Lao PDR must develop substantial infrastructures and alternative renewable energies to support the campaign of Lao PDRs electricity security in the long-run.

scholarly journals An introductory tutorial on Bayesian inference using a simple pedagogical tool

2017 ◽  
Author(s):  
Guillermo CAMPITELLI
Keyword(s):  
Probability Theory ◽  
Bayesian Inference ◽  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Traditional Approach ◽  
Postgraduate Students ◽  
Data Table ◽  
Null Hypothesis Testing ◽  
Testing Approach ◽  
Hypothesis Testing Approach

This tutorial on Bayesian inference targets psychological researchers who are trained in the null hypothesis testing approach and use of SPSS software. There a number ofexcellent quality tutorials on Bayesian inference, but their problem is that, they assume mathematical knowledge that most psychological researchers do not possess. Thistutorial starts from the idea that Bayesian inference is not more difficult than the traditional approach, but before being introduced to probability theory notation is necessary for the newcomer to understand simple probability principles, which could be explained without mathematical formulas or probability notation. For this purpose in this tutorial I use a simple tool-the parameter-data table-to explain how probability theory can easily be used to make inferences in research. Then I compare the Bayesian and the null hypothesis testing approach using the same tool. Only after having introduced these principles I show the formulas and notations and explain how they relate to the parameter-data table. It is to be expected that this tutorial will increase the use of Bayesian inference by psychological researchers. Moreover, Bayesian researchers may use this tutorial to teach Bayesian inference to undergraduate or postgraduate students.

Bayesian Inference for Comparative Research

1994 ◽  
Vol 88 (2) ◽  
pp. 412-423 ◽  
Author(s):  
Bruce Western ◽  
Simon Jackman
Keyword(s):  
Regression Analysis ◽  
Comparative Analysis ◽  
Bayesian Inference ◽  
Statistical Inference ◽  
Bayesian Approach ◽  
Comparative Research ◽  
Data Sets ◽  
Explanatory Variables ◽  
Long Run

Regression analysis in comparative research suffers from two distinct problems of statistical inference. First, because the data constitute all the available observations from a population, conventional inference based on the long-run behavior of a repeatable data mechanism is not appropriate. Second, the small and collinear data sets of comparative research yield imprecise estimates of the effects of explanatory variables. We describe a Bayesian approach to statistical inference that provides a unified solution to these two problems. This approach is illustrated in a comparative analysis of unionization.

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