Random Regressors and Moment Based Estimation by using EViews

1. THE INCONSISTENCY OF THE LEAST SQUARES ESTIMATOR To reproduce Figure 10.2, showing the positive correlation between the x and e generated by the Monte Carlo experiment discussed in the text, first we must “create” the true errors e. In a Monte Carlo world we know the true parameters, and that y is created

Simultaneous Equations Models by using EViews

1. EXAMINING THE DATA In this section, the text introduces a two-equation demand and supply model for truffles, a French gourmet mushroom delicacy. To estimate the truffles model in EViews, open the workfile truffles.wfl. Open a Group containing the data. While holding down the Ctrl-key, select P, Q, PS, DI and PF. The first

Nonstationary Time Series Data and Cointegration by using EViews

1. STATIONARY AND NONSTATIONARY VARIABLES Time-series data display a variety of behavior. The data shown in Figure 12.1 is stored in the EViews workfile usa.wfl. They are real gross domestic product (GDP), inflation rate (INF), Federal funds rate (F) and the 3-year Bond rate (B). The changes for real gross domestic product (DG), inflation

VEC and VAR Models by using EViews: An Introduction to Macroeconometrics

1. VEC AND VAR MODELS A VAR model describes a system of equations in which each variable is a function of its own lag and the lag of the other variables in the system. A VEC model is a special form of the VAR for 1(1) variables which are cointegrated. 2. ESTIMATING A VEC

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Time-Varying Volatility and ARCH Models by using EViews: An Introduction to Financial Econometrics

1. TIME-VARYING VOLATILITY In this chapter we are concerned with variances that change over time, i.e., time-varying variance processes. The model we focus on is called the AutoRegressive Conditional Heteroskedastic (ARCH) model. This is an example of an ARCH(l) model since the time varying variance ht is a function of a constant term (a0)

Panel Data Models by using EViews

1. GRUNFELD DATA: TWO EQUATIONS Panel data are data with two dimensions, a time dimension and a cross-section dimension. They typically comprise observations on a number of economic units, such as individuals or firms, over a number of time periods. The use of panel data involves new models, new econometric techniques and new ways

Qualitative and Limited Dependent Variables by using EViews

1. MODELS WITH BINARY DEPENDENT VARIABLES We will illustrate binary choice models using an important problem from transportation economics. How can we explain an individual’s choice between driving (private transportation) and taking the bus (public transportation) when commuting to work, assuming, for simplicity, that these are the only two alternatives? We represent an individual’s

Importing and Exporting with EViews

1. OBTAINING DATA FROM THE INTERNET Up to now we have taken you through various econometric methodologies and applications using already prepared EViews workfiles. In this chapter, we show you how to create a workfile and how to import data from an Excel spreadsheet. The first step is to create the Excel data file.

Chapter 1: Introduction to PLS-SEM

1. The Research Dilemma In any marketing research project, an ideal data set should have a large sample size and be normally distributed. Unfortunately, the reality is that many applied research projects have limited participants because of the nature of the project. Insufficient resources and tight project timelines further prevent researchers from obtaining a

Chapter 2: Understanding the PLS-SEM Components

1. Inner (Structural) and Outer (Measurement) Models Partial Least Squares Structural Equation Modeling (PLS-SEM) is a second-generation multivariate data analysis method that is often used in marketing research because it can test theoretically supported linear and additive causal models (Chin, 1996; Haenlein & Kaplan, 2004; Statsoft, 2013). With PLS-SEM, marketers can visually examine the

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Chapter 3: Using SmartPLS Software for Path Model Estimation

1. Introduction to the SmartPLS Software Application There are currently different versions of SmartPLS in circulation. The previous version, 2.0M3, is a freeware that has all of the core functionalities for PLS-SEM. Although it is only available on the Windows platform, this freely available version 2 is still used by thousands of researchers around

Chapter 4: Evaluating PLS-SEM Results in SmartPLS

1. The Colorful PLS-SEM Estimations Diagram SmartPLS presents path modeling estimations not only in the Modeling Window but also in a text-based report31 which is accessible via the “Report” menu. In the PLS-SEM diagram, there are two types of numbers: Numbers in the circle: These show how much the variance of the latent variable

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Chapter 5: Evaluating Model with Formative Measurement

1. Different Things to Check and Report As described earlier in this book, a model does not necessarily have reflective measurements. When working with model that utilizes a formative measurement scale, we do not analyze indicator reliability, internal consistency reliability, or discriminant validity because the formative indicators are not highly correlated together. Instead, we

Chapter 6: Determining Measurement Model Using Confirmatory Tetrad Analysis (CTA-PLS)

1. Formative or Reflective? Determining the Measurement Model Quantitatively Designing the measurement model in PLS-SEM is not always a straight-forward task. It has been a challenge for some researchers to determine if the relationship between the indicators and the latent variable is formative or reflective. This is particularly the case in Marketing when researchers

Chapter 7: Handling Non-Linear Relationship Using Quadratic Effect Modeling (QEM)

1. Non-linear Relationship Explained Experienced researchers would agree that variables do not always hold a linear relationship as in a straight line (Hay & Morris, 1991; Eisenbeiss, CorneliEen, Backhaus, & Hoyer, 2014). For example, in the field of marketing, advertising activities and sales revenue often hold a non-linear, quadratic relationship.48 Consumers may be eager

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Chapter 8: Analysing Segments Using Heterogeneity Modeling

1. Something is Hiding in the Dataset In exploratory research, marketing researchers may have little knowledge about the nature of the underlying data. They need to consider the potential problem of heterogeneous data structures in PLS-SEM modeling because the data do not necessarily come from a homogeneous population (see Figure 42). If heterogeneity is

Chapter 9: Estimating Complex Models Using Higher Order Construct Modeling (HCM)

1. Case Study: Customer Survey in a Photocopier Manufacturer (B2B) Since PLS-SEM is a relatively new approach to modeling, researchers who are not familiar with it may find the analytical and reporting aspects challenging, especially in the areas of higher-order constructs modeling, mediation analysis, and categorical moderation analysis. Chapter 9, 10 and 11 are

Chapter 10: Mediation Analysis with SmartPLS Software

1. Customer Satisfaction (SATIS) as a Mediator The relationships among constructs in PLS-SEM can be complex and not always straightforward. To gain a better understanding of the role of SATIS in our model, its potential mediating effect on the linkage between REPUT and LOYAL (see Figure 92), and those between PRICE and LOYAL (see

Chapter 11: Comparing Groups Using Categorical Moderation Analysis (PLS-MGA)

1. Multi-group Analysis – “Business Type” in the Photocopier Manufacturer Example Before starting this research project, Susan’s colleagues in the sales department keep telling her that non-profit business customers often behave very differently from for-profit ones in their decision-making processes. To confirm such insights, the last hypothesis is built as follows: H8: There is

Chapter 12: New Techniques in PLS-SEM

1. Estimating Factor Models Using Consistent PLS (PLSc) The traditional PLS algorithm has its shortcomings. Dijkstra & Schermelleh-Engel (2014) argue that it overestimates the loadings in absolute value and underestimates multiple and bivariate correlations between latent variables. It is also found that the R2 value of endogenous latent variables is often underestimated (Dijkstra, 2010).