# Econometrics-Stata

Submitted By lanhuong1
Words 1314
Pages 6

4 ～ 2015

The data use for this paper was constructed in the following way. The data are drawn from the OPE Campus Safety and Security Statistics website database to which crime statistics (as of the 2010 data collection) are submitted annually, via a web-based data collection, by all postsecondary institutions that receive Title IV funding (i.e., those that participate in federal student aid programs). This data collection is required by the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Higher Education Opportunity Act. The outcome of this process was that the enrolments is not influenced significantly by the price or living conditions of the campus. When reading some articles I was personally very surprised about this since normally the living conditions should be influent more than the other factors in the enrolments. Based on this, I was very interested which other factors influence to students’ decisions. That is why I chose this dataset to analyze for the final term paper in the course of econometrics.

2. Multiple regressions

2.1 Data source

The numerous data used in this paper (reference see below), consists of observations on six variables. The variables are: • enroll • priv = private collage • police • crime

2.2 Model specification

At the first place it is necessary to run a regression with all variables:

[pic]

In the first step the variable priv is removed, because according to the t-test it has a low significance. In that case, this means that priv is not significant at the 10% level.

[pic]
In this decision...

### Similar Documents

...Econometrics (Economics 360) Syllabus: Spring 2015 Instructor: Ben Van Kammen Office: Krannert 531 Office Hours: Friday, 10 a.m.-noon Email: bvankamm@purdue.edu Meeting Location: KRAN G010 Meeting Days/Times: TR 1:30-2:45 p.m. (001) TR 3-4:15 p.m. (002) TR 4:30-5:45 p.m. (003) Course Description This is an upper division economics course required for students pursuing a BS in economics. It is one of the few courses that explicitly covers empirical methods, i.e., the analysis of observed economic behavior in the form of data. Empirics stand in contrast to theory, e.g., micro and macro, about how agents behave. Despite this under-representation, empirical analysis comprises a large part of economists’ workload and is one of the most practical skills that an economics student can learn. Course Objectives In this class students will: 1. perform statistical and practical inference based on the results of empirical analysis, 2. identify useful characteristics of estimators, e.g., unbiasedness, consistency, efficiency, 3. state predictions of theoretical economic models in terms of testable hypotheses, 4. model economic relationships using classical methods, such as Ordinary Least Squares, derive the properties of estimators related to these methods, and 5. perform estimation using methods discussed in class using software, 6. perform diagnostic tests that infer whether a model’s assumptions are invalid, 7. evaluate empirical models based on whether their resulting......

Words: 2067 - Pages: 9

Free Essay

#### Financial Development

...Research Department. Asteriou, D., & Monastiriotis, V. (2004). What do unions do at the large scale? Macro-economic evidence from a panel of OECD countries. Journal of Applied Economics, VII(I), pp. 27-46. Arellano, M. (2003): Panel Data Econometrics, Oxford University Press. Arellano, M., and O. Bover. (1995). Another Look at The Instrumental Variable Estimation of Error- Components Models. Journal of Econometrics, 68, 29-52. Arellano, M., & Bond, S. R. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies (new York), 58, 194, 277- 297. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 1, 115-143. Baltagi, B. (2008). Econometric analysis of panel data, John Wiley and Sons, Chichester. Baltagi, Gri, and Xiong (2000). To Pool or Not To Pool: Homogeneous Versus Heterogeneous Estimators Applied to Cigarette Demand. Review of Economics and Statistics 82: 117. Barro, R.J. (1991). Economic Growth in a Cross Section of Countries. Homepage of National Bureau of Economic Research (online). Beck, T., (2008). The Econometrics of Finance and Growth, Palgrave Handbook of Econometrics, Vol. 2. Beck, T., Levine, R. and World Bank. Financial Sector Strategy and Policy Group (2000). New firm formation and industry growth : does having a market- or bank-based system matter? Washington, D.C.:...

Words: 881 - Pages: 4

Free Essay

#### Stata

...Appendix II: STATA Preliminary STATA is a statistical software package that offers a large number of statistical and econometric estimation procedures. With STATA we can easily manage data and apply standard statistical and econometric methods such as regression analysis and limited dependent variable analysis to crosssectional or longitudinal data. STATA is widely used by analysts working with household survey data. 1. Getting Started 1.1. Starting STATA Start a STATA session by double-clicking on the STATA icon in your desktop. The STATA computing environment comprises four main windows. The size and shape of these windows may be moved about on the screen. Their general look and description are shown below: It is useful to have the Stata Results window be the largest so you can see a lot of information about your commands and output on the screen. In addition to these windows STATA environment has a menu and a toolbar at the top (to perform STATA operations) and a directory status bar at the bottom (that shows the current directory). You can use menu and toolbar to issue different Poverty Manual, All, JH Revision of August 8, 2005 Page 170 of 218 STATA commands (like opening and saving data files), although most of the time it is more convenient to use the Stata Command window to perform those tasks. If you are creating a log file (see below for more details), the contents can also be displayed on the screen; this is sometimes useful if one needs to back up......

Words: 3880 - Pages: 16

Free Essay

#### House Price Data in Iowa

...Submission Date: 14th April, 2016 Introduction Throughout this report I endeavour to present a clear, concise documentation of the factors that influence house prices in Ames, Iowa. I will initiate this report with my estimate of the possible regression based on my economic theory, create a dummy variable in respect to the absence/presence of a garage, followed by a comprehensive description of continuous and discrete variables. Preceding this I aim to report an extensive description of my estimated regression, test said regression for multicollinearity and heteroscedasticity, predict possible solutions to these problems and re run the regression taking into consideration the possible solutions. Main Body Part (a) From my study of econometrics and my knowledge of house prices, the following equation is my estimate of the factors that influence the price of houses PR= f (SI, YD, GA, lnAGE) + + + - (see appendix 1.1 for variable details) My reasoning for the inclusion of the above variables and their predicted signs are as follows: SI: Generally speaking, the larger the home the more you pay as house buyers like to buy houses with as much space as possible. I believe there would be a positive relationship between the price and the size of a house in square feet. YD: I believe that yard size is positively related to the cost......

Words: 3224 - Pages: 13

Free Essay

#### Oil Price

...Boston College Economics The Stata Journal (yyyy) Working Paper Number ii, pp. 1–38 vv, No. 667 Enhanced routines for instrumental variables/GMM estimation and testing Christopher F. Baum Mark E. Schaﬀer Boston College Heriot–Watt University Steven Stillman Motu Economic and Public Policy Research Abstract. We extend our 2003 paper on instrumental variables (IV) and GMM estimation and testing and describe enhanced routines that address HAC standard errors, weak instruments, LIML and k-class estimation, tests for endogeneity and RESET and autocorrelation tests for IV estimates. Keywords: st0001, instrumental variables, weak instruments, generalized method of moments, endogeneity, heteroskedasticity, serial correlation, HAC standard errors, LIML, CUE, overidentifying restrictions, Frisch–Waugh–Lovell theorem, RESET, Cumby-Huizinga test 1 Introduction In an earlier paper, Baum et al. (2003), we discussed instrumental variables (IV) estimators in the context of Generalized Method of Moments (GMM) estimation and presented Stata routines for estimation and testing comprising the ivreg2 suite. Since that time, those routines have been considerably enhanced and additional routines have been added to the suite. This paper presents the analytical underpinnings of both basic IV/GMM estimation and these enhancements and describes the enhanced routines. Some of these features are now also available in Stata 10’s ivregress, while others are not. The additions include: • Estimation and......

Words: 16813 - Pages: 68

Free Essay

#### Mata

...Introduction GMM for OLS GMM for IV Poisson Extras References GMM estimation in Mata Using Stata’s new optimizer to program estimators Austin Nichols July 24, 2008 Austin Nichols GMM estimation in Mata Introduction GMM for OLS GMM for IV Poisson Extras References optimize() is exciting stuﬀ The new (as of Stata 10) optimize function in Mata is exciting. You can use it e.g. to ﬁnd maxima of a function, solve a diﬃcult nonlinear system of equations, or write a new estimator. Likely suspects: Generalized Methods of Moments (GMM) or Minimum Distance estimators (MDE). More on GMM: Hansen (1982) More on MDE: Chamberlain (1982, 1984) More on both: Wooldridge (2002) chapter 14 Today: a couple of quick examples of GMM estimators; see ivpois on SSC for a more detailed example. Austin Nichols GMM estimation in Mata Introduction GMM for OLS GMM for IV Poisson Extras References Linear regression GMM Eﬃcient GMM Simple example The OLS model Consider the most common regression framework: y = Xβ + ε where we assume E (X ε) = 0 so our estimator β is unbiased. The usual approach is to deﬁne βOLS = (X X )−1 (X y ) that minimizes the sum of squared residuals (y − y )2 = and has an easy solution. (y − X β)2 Austin Nichols GMM estimation in Mata Introduction GMM for OLS GMM for IV Poisson Extras References Linear regression GMM Eﬃcient GMM Simple example The GMM model Could also deﬁne βGMM that gets E (X ε) as close to zero as possible in the sample (zero, in fact, with a......

Words: 3768 - Pages: 16

#### Making Decisions Based on Demand and Forecasting

...AND COMMENTS TO Joy de Beyer ( jdebeyer@worldbank.org) and Ayda Yurekli (ayurekli@worldbank.org) World Bank, MSN G7-702 1818 H Street NW Washington DC, 20433 USA Fax : (202) 522-3234 Contents I. Introduction 1 Purpose of this Tool 1 Who Should Use this Tool 2 How to Use this Tool 2 II. Define the Objectives of the Analysis 4 The Reason for Analysis of Demand 4 The Economic Case for Demand Intervention 4 Analysis of Demand for the Policy Maker 5 Design an Analysis of Demand Study 6 Components of a Study 6 The Nature of Econometric Analysis 7 Resources Required 7 Summary 8 References and Additional Information 8 III. Conduct Background Research 9 IV. Build the Data Set 11 Choose the Variables 11 Data Availability 11 Data Types 12 Prepare the Data 13 Data Cleaning and Preliminary Examination 14 Preparing the Data Variables 14 References and Additional Information 19 V. Choose the Demand Model 20 Determine the Identification Problem 20 Test for Price Endogeneity 21 ...

Words: 36281 - Pages: 146

Free Essay

#### Most Harmless Econometrics

...Mostly Harmless Econometrics: An Empiricist’ Companion s Joshua D. Angrist Massachusetts Institute of Technology Jörn-Ste¤en Pischke The London School of Economics March 2008 ii Contents Preface Acknowledgments Organization of this Book xi xiii xv I Introduction 1 3 9 10 12 16 1 Questions about Questions 2 The Experimental Ideal 2.1 2.2 2.3 The Selection Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Random Assignment Solves the Selection Problem . . . . . . . . . . . . . . . . . . . . . . . . Regression Analysis of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II The Core 19 21 22 23 26 30 36 38 38 44 47 51 51 3 Making Regression Make Sense 3.1 Regression Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 3.1.2 3.1.3 3.1.4 3.2 Economic Relationships and the Conditional Expectation Function . . . . . . . . . . . Linear Regression and the CEF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asymptotic OLS Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saturated Models, Main E¤ects, and Other Regression Talk . . . . . . . . . . . . . . . Regression and Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 3.2.2 3.2.3 The Conditional Independence Assumption . . . . . . . . . . . . . . . . . . . . . . . . The Omitted Variables Bias Formula . ....

Words: 114745 - Pages: 459

Free Essay

#### Crowdfunding

...SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING Marco Caliendo IZA, Bonn Sabine Kopeinig University of Cologne Abstract. Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like ‘what to do if there is choice-based sampling?’ or ‘when to measure effects?’ can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes. Keywords. Propensity score matching; Treatment effects; Evaluation; Sensitivity analysis; Implementation 1.......

Words: 20722 - Pages: 83

#### Econometrics Book Description

Words: 73046 - Pages: 293

#### Econometrics

Words: 194599 - Pages: 779

#### Tips to Writting a Term Paper

Words: 12292 - Pages: 50

#### Thyt

Words: 12292 - Pages: 50

#### Statistics Paper

...Case of Salary discrimination based on Gender In this case the assertion has been made that the males of the population receive a higher salary than do females as a direct result of their gender. In order to test this assertion, we must first set up a hypothesis, and then test that hypothesis by analyzing the data provided. The first step in hypothesis testing is formalizing the hypothesis into a null hypothesis and an alternative hypothesis. The null hypothesis is a restatement of the assertion being made, while the alternative hypothesis is the negation of the null hypothesis. The null and alternative hypotheses can be stated respectively as follows: H0 : The mean salary of females < the mean salary of males H1 : The mean salary of females > or = the mean salary of males Before proceeding with determining a difference, and cause and effect linear relationship between these two variables, it is important to note that any individual outliers must be eliminated from the data set before a multiple linear regression is performed. The existence of such outliers in a model could skew the resulting linear relationship. The performance of successive stem and leaf box plots revealed the existence of 6 outliers. These outliers were then removed from the sample Splitting the data set according to gender and comparing the means of the two groupings reveals a difference in the average salary of the sampled males and sampled females. This does not, however, tell...

Words: 414 - Pages: 2