Dummy variables

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  • The quantitative independent variables used in regression equations, which usually take values over some continuous range. Frequently, one may wish to include the quality independent variables, often called dummy variables, in the regression model in order to (i) capture the presence or absence of a ‘quality’, such as male or female, poor or rich, urban or rural areas, college degree or do not college degree, different stages of development, different period of time; (ii) to capture the interaction between them; and, (iii) or to take on one or more distinct values. ...

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  • Định nghĩa: Biến giả (dummy variables) ở đây được hiểu là các biến giải thích định tính(qualitative). Trong phân tích hồi qui, biến phụ thuộc không chị phụ thuộc vào các biến định lượng mà còn các biến định tính như giới tính, chủng tộc, tôn giáo, dân tộc, chính trị.)

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  • Biến định lượng: các giá trị quan sát được thể hệ bằng con số Biến định tính: thể hiện một số tính chất nào đó Để đưa những thuộc tính của biến định tính vào mô hình hồi quy, cần lượng hóa chúng = sử dụng biến giả (dummy variables) Ví dụ 5.1: Xét mô hình Yi = 1 + 2Xi + 3Di + Ui với Y Tiền lương (triệu đồng/tháng) X Bậc thợ D=1 nếu công nhân làm trong khu vực tư nhân D=0 nếu công nhân làm trong khu vực nhà nước D được gọi là biến giả trong mô hình...

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  • This book is designed to give you the essential, nitty-gritty information typically covered in a first semester statistics course. It’s bottom-line information for you to use as a refresher, a resource, a quick reference, and/or a study guide. It helps you decipher and make important decisions about statistical polls, experiments, reports and headlines with confidence, being ever aware of the ways people can mislead you with statistics, and how to handle it.

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  • Nội dung cơ bản của bài giảng Hồi quy với biến giả trình bày về bản chất của biến giả (Dummy variable), xây dựng mô hình hồi quy với biến, ứng dụng của biến giả. Chương này nghiên cứu mô hình hồi quy với biến độc lập là biến định tính còn biến phụ thuộc là biến định lượng.

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  • Biến định lượng giá trị quan sát thể hiện bằng số. VD: thu nhập, giá cả, lãi suất, … 2/ Biến định tính  có hay không có 1 tính chất hoặc các mức độ một tiêu thức  biến giả. VD: giới tính, dân tộc, tôn giáo, khu vực bán hàng, … 3/ Lượng hoá biến định tính  biến giả (Dummy variables) VD C5.1: Năng suất của 2 công nghệ sản xuất (công nghệ A và B) Zi Yi 0 28 1 32 1 35 0 27 0 25 1 37 0 29 1 34 1 33 0...

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  • Results obtained from fixed effect and ordinary least squares are indifferent. However, results based on fixed effect model yield more insightful interpretation. The outcomes from fixed effect model help indicating that three determinants affecting mutual fund growth are types of AMCs, Administrative expense ratio, and size of AMCs. Types of AMCs or dummy variables are used to represent distribution channel and parent reputation.

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  • Given the considerable variation in deposit insurance arrangements and the relatively large number of banking crises, it is possible to use this panel to test whether the nature of the deposit insurance system has a significant impact on the probability of a banking crisis once other factors are controlled for. We carry out these tests using the multivariate logit econometric model developed in our previous work on the determinants of banking crises (Demirg†e-Kunt and Detragiache, 1998).

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  • Chương 4 Hồi quy với biến giả, chương học này trình bày những nội dung sau: Bản chất của biến giả (Dummy variable), mô hình có một biến độc lập là biến định tính, mô hình có hai biến độc lập là biến định tính, phân tích ảnh hưởng tương tác giữa các biến định tính, sử dụng biến giả để phân tích sự biến động mùa vụ, so sánh hai hàm hồi quy, hồi quy tuyến tính từng khúc.

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  • The distance measure consists of three dummy variables:7 The first indicates diary weeks starting on payday, and weeks starting 1 to 7 days after payday. The second indicates weeks starting 8 to 14 days after payday. Diary weeks beginning at distances 15 to 22 are omitted from the equation and serve as the reference category. The third dummy indicates weeks beginning 4 to 1 days before the next payday. These weeks overlap the (imputed) next payday by at least three days.

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  • This high level of spending extends well into the pay period: in weeks beginning 8 to 14 days after payday, spending is still 5 percent higher than in the omitted category. The final distance category captures the significant increase in spending due to overlap with the next payday.13 To further verify that our baseline results are not driven by our parameterization of the distance measure, we regress log expenditure on separate dummy variables for each starting distance from payday.

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  • We employ three different measures of performance: return on equity (ROE) which is defined as earnings before interest and tax (EBIT) divided by book value of shareholders‟ equity; return on sales (ROS), calculated as EBIT divided by total turnover; and Tobin‟s Q. 3 The latter indicator is calculated as the ratio of the firm‟s market capitalization to book value of equity. 4 We analyse the effect of IED using two definitions of variable IEit. First, we employ a dummy variable which takes the value of 1 if at least one non-executive director is also...

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  • The purpose of this and the following chapter is to briefly go through the most basic concepts in probability theory and statistics that are important for you to understand. If these concepts are new to you, you should make sure that you have an intuitive feeling of their meaning before you move on to the following chapters in this book.

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  • Maysami and Sims (2002, 2001a, 2001b) employed the Error-Correction Modelling technique to examine the relationship between macroeconomic variables and stock returns in Hong Kong and Singapore (Maysami and Sim, 2002b), Malaysia and Thailand (Maysami and Sim 2001a), and Japan and Korea (Maysami and Sim 2001b).

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  • We will use whether or not the household owns animals, bikes and/or motorbikes. Households living in the rural part of the municipality might be more prone to own these assets. Moreover, they might have worse access to health and sanitary infrastructure. If this was the case, we expect that the importance of household consumption will be underestimated when we use the ownership of animals, bikes and/or motorbikes as instruments.

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  • The objectives of this chapter are to switching models. In this chapter, you will learn how to: Use intercept and slope dummy variables to allow for seasonal behaviour in time series, motivate the use of regime switching models in financial econometrics, specify and explain the logic behind Markov switching models,...

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  • We first examine whether the likelihood of acquisition affects the firm’s decision to split, after controlling for price run-ups and other factors that may influence a firm’s stock-split decision. Acquiring firms and their industry-size-price matching firms are pooled for all regressions. 9 We run probit regressions of the split dummy variable (equals one if there is a stock split from month −6tomonth −1, and zero otherwise) on the M&A dummy variable (equals one if the firm is an acquirer, and zero otherwise) and other control variables.

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  • Obviously, in this case we cannot fully eliminate the identification and causality problem because firms’ profits/losses may affect their demand for credit as well. In order to circumvent this problem (at least partly) we use in addition an interaction variable between the profitability dummy and the lagged value of the access to credit variable. The rationale is to test whether long-term enterprise-bank relations are associated with the firm’s viability.

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