Prediction, Goodness of Fit and Modeling Issues by using EViews

1. PREDICTION IN THE FOOD EXPENDITURE MODEL In this chapter we continue to work with the simple linear regression model and our model of weekly food expenditure. To begin, open the food expenditure workfile food.wfl. On the EViews menu choose File/Open and then open the file. So that the original file is not altered

The Multiple Linear Regression Model by using EViews

In the simple linear regression model the average value of a dependent variable is modeled as a linear function of a constant and a single explanatory variable. The multiple linear regression model expands the number of explanatory variables. As such it is a simple but important extension that makes linear regression quite powerful. The

Further Inference in the Multiple Regression Model by using EViews

1. F AND CHI-SQUARE TESTS In Chapter 5 we saw how to use EViews to test a null hypothesis about a single coefficient in a regression model. This test can be extended in two ways. We may want to test a single null hypothesis that involves two or more coefficients, or we might want

Nonlinear Relationships by using EViews

1. POLYNOMIALS In microeconomics you studied “cost” curves and “product” curves that describe a firm. Total cost and total product curves are mirror images of each other, taking the standard “cubic” shapes shown in POE Figure 7.1. Average and marginal cost curves, and their mirror images, average and marginal product curves, take quadratic shapes,

Heteroskedasticity by using EViews

1. EXAMINING RESIDUALS In this chapter we return to the example considered in Chapters 2 to 4 where weekly expenditure on food was related to income. Data in the file food.wfl were used to find the following least squares estimates. We are now concerned with whether the error variance for this equation is likely

Dynamic Models, Autocorrelation, and Forecasting by using EViews

1. LEAST-SQUARES RESIDUALS: SUGARCANE EXAMPLE The first example considered in Chapter 9 is an area response model for sugarcane in Bangladesh where area sown to sugarcane A is related to price P by the equation In contrast to earlier chapters where the index used for the observations was mainly i, here we use the

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