Research Methods

Steps in Vector Autoregressive (VAR) and Vector Error Correction (VEC) Models

Steps in Vector Autoregressive (VAR) and Vector Error Correction (VEC) Models Check co-integration using Johansen co-integration test If you find any co-integration equation, go for VEC model. However, VAR is fine too for simplicity. Estimation parameters do not differ drasticially. Decide on lag orders. Perform diagnostic check. If all good, perform out-sample forecast. Check accuracy of both in-sample and out-sample forecast.

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Checklist for Structural Equation Modelling

Checklist for Structural Equation Modelling Here I present a checklist for robust structural equation modeling (SEM) in research articles. 1. Data cleaning 2. Check normality of data 3. Check for response bias 4. Develop a measurement model 4.1. Exploratory factor analysis 4.2. Confirmatory factor analysis 4.3. Correlations among latent factors 4.4. Convergent and divergent validity checks 4.5. Reliability checks 4.6.

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Common Issues in Structural Equation Modelling (SEM) and their Solutions

Common Issues in Structural Equation Modelling (SEM) Hello everyone, This post presents some very common issues we face when doing Structural Equation Modelling (SEM). Most of the problems and their remedies are either taken from a book or academic social interactions, e.g. Research Gate. Courtesy to all sources are mentioned along with the respective post/suggestion.  Problem 1: What are the

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Bibliometric Citation Analysis: All You Need to Know

Download the HistCite software by clicking the option below! Published bibliometric studies by ResearchHUB Team: A review of the internationalization of Chinese enterprises A Review of Born globals A review of green supply chain management: From bibliometric analysis to a conceptual framework and future research directions Credit Risk Research: Review and Agenda A review of autonomous ship literature This post

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Structural Equation Modelling (SEM) and Multi-group SEM using R

Structural Equation Modeling (SEM) is a multivariate statistical analysis technique that is used to analyze structural relationships among variables. SEM is the combination of factor analysis and multiple regression analysis. Usually factors are created using multiple observed variables through factor analysis. Those factors are called latent variables. Thereafter, multiple regression analysis is performed on latent variables level, not in observed

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