Free Essay

In: Science

Submitted By catech8

Words 1769

Pages 8

Words 1769

Pages 8

GSU Department of Economics, Spring 2016

Practice Midterm Questions

(No Solution will be Provided)

1. Suppose the data generating process (the true relationship) is y = Xβ + ε, where E[ε|X] = 0, E[εε |X] = σ 2 I n ; and X includes an intercept term. You do not observe the data set Z = [y X]. Instead you observe

150 15 50

Z Z = 15 25 0

50 0 100

2

Compute the least squares estimators β, s2 , R2 and RAdj (the adjusted R2 ). Is there anything to be gained by observing the full data set?

2. Suppose you have the simple regression model with no intercept: yi = xi β+ i for i = 1, 2. Suppose further that the true value of β is 1, the values of xi observed in the sample are x1 = 2 and x2 = 3, and the distribution of i is

Pr( i = −2) = Pr( i = 2) = 1/2 with 1 independent of 2 .

(a) Find the least squares estimator of β.

(b) What is it mean and variance? Is it BLUE?

(c) Consider the alternative estimator β ∗ = y /¯, where y is the sample mean

¯ x

¯

of yi and x is the sample mean of xi . What is the mean and variance of

¯

β ∗ ? Is it unbiased?

(d) Which estimator is more eﬃcient, the least squares estimator or β ∗ ?

3.

Suppose x1 , x2 . . . xn is an independent but not identically distributed random sample from a population with E[xi ] = µ and Var[xi ] = σ 2 /i for i =

1, 2, . . . , n. Consider the following class of estimators for the population mean µ: n µ=

ˆ

ci xi

where

c1 , . . . , c n

are constants

i=1

Each sequence {c1 , c2 , . . . , cn } deﬁnes an estimator for µ.

(a) Give a necessary and suﬃcient condition on the ci for µ to be an unbiased

ˆ

estimator of µ.

(b) Find the best unbiased estimator µ∗ in the class of estimators µ.

ˆ

ˆ

1

(c) Compare the relative eﬃciency of µ∗ and the sample mean, x =

ˆ

¯ n Explain.

1

n i=1 xi .

Some possibly useful results:

i

i = n(n + 1)/2

i

i2 = n(n + 1)(2n + 1)/6.

4. Suppose we have the linear regression model y = Xβ + ε

(a) Show that if E[ε] = 0 then the least squares estimator of β is biased.

(b) Suppose E[ε] = 0 and E[εε ] = σ 2 I n . Let X = [i X2 ] where i is an nvector of ones and X2 is n×(k − 1) and measured in deviations from means.

Show that the least squares estimator of the intercept (the parameter on i) is uncorrelated with the least squares estimator of the slopes (the parameters on X2 ).

5. Suppose you observe a random sample of n observations (y, X) from a population that satisﬁes y = Xβ + ε with E[ε|X] = 0 and E[εε |X] = σ 2 I n . Let β denotes the least squares estimate of β based on the sample (y, X). Now you observe the vector of independent variables, x∗ for one more observation that is not part of your estimation sample but is sampled from the same population. You do not observe the dependent variable y∗ for the new observation. Let y∗ = x∗ β

ˆ

denote the OLS prediction of y∗ .

(a) Is y∗ an unbiased estimator of y∗ ? Explain/prove your claim.

ˆ

(b) What is the variance of the OLS prediction error, y∗ − y∗ ?

ˆ

(c) What is the best linear (in y) unbiased estimator of y∗ ? Explain/prove your claim.

(d) Now suppose that ε|X ∼ N (0, σ 2 I n ). Derive a test statistic (and its sampling distribution) to test the null hypothesis that y∗ = y0 .

6. Suppose the data generating process is yi = xi β + εi where the errors are spherical and have mean zero. The data fall into one of two groups of equal size.

In the ﬁrst group (group 1) of n/2 observations, x i = [1 1]. In the second group (group 2), x i = [1 − 1]. Despite your knowledge of least squares, you

¯

devise a new estimator β by noting that y 1 = β1 + β2 + ε1

¯

¯

(1)

y 2 = β1 − β2 + ε2

¯

¯

(2)

¯ where y 1 is the sample mean of yi in group 1, y 2 is the sample mean of yi in

¯

group 2, ε1 is the sample mean of εi in group 1, and ε2 is the sample mean of εi

¯

¯

¯

in group 2. Since E[¯1 ] = E[¯2 ] = 0 you deﬁne your estimator β as the solution ε ε to the linear equations (1) and (2) with ε1 and ε2 set to zero.

¯

¯

¯

(a) Give a formula for β

¯ unbiased? If yes, prove it. If not, derive and sign the bias.

(b) Is β

¯

(c) What is the sampling variance of β?

(d) How do its ﬁnite sample properties compare to the least squares estimator?

Explain carefully.

2

7. Suppose we have the linear regression model y = Xβ + ε

(a) Show that if E[ε|X] = 0 then the least squares estimator of β is biased.

(b) Suppose we write the model as y = X1 β1 + X2 β2 + ε and E[ε|X] = X1 γ for some γ = 0. Is the least squares estimator of β 2 biased? Prove your claim. 8.

Consider the regression model y = X1 β1 + X2 β2 + ε. For mysterious reasons, you are mainly interested in β 2 . Let M1 = I − X1 (X1 X1 )−1 X1 and

P1 = I − M1 . For even more mysterious reasons, you estimate the following regressions: (a) y = X1 β1 + X2 β2 + ε.

(b) P1 y = X2 β2 + ε

(c) P1 y = P1 X2 β2 + ε

(d) M1 y = X2 β2 + ε

(e) y = M1 X2 β2 + ε

(f) M1 y = X1 β1 + M1 X2 β2 + ε

(g) M1 y = M1 X1 β1 + M1 X2 β2 + ε which gives you a variety of estimates of β 2 . How many diﬀerent estimates are there? How are they related?

9. Consider the translog production function ln Qi = β1 + β2 ln Li + β3 ln Ki + β4

(ln Ki )2

(ln Li )2

+ β5

+ β6 ln Li ln Ki + εi

2

2

(a) Show that the condition for constant returns to scale is

∂ ln Qi

∂ ln Qi

+

=1

∂ ln Li

∂ ln Ki

(b) What restrictions on the coeﬁicients correspond to constant returns to scale?

(c) How would you estimate the restricted model?

(d) How would you test the hypothesis of constants returns to scale?

10. Suppose the data generating process is given by y = X1 β1 + X2 β2 + ε where X1 is n × k1 , X2 is n × k2 , and the other quantities are vectors. Suppose you estimate this model (call it the “long” model) via OLS, and you also estimate the “short” model, which excludes X2 .

(a) Derive the sum of squared residuals in both models, and sign their diﬀerence.

(b) Derive the expected sum of squared residuals in both models, and sign their diﬀerence. 3

(c) Suppose β2 = 0. Does this change your answers to parts a and b? Explain.

11. As part of your dissertation research, your senior supervisor suggests you estimate the linear regression model y = Xβ + Gθ + ε where X is n × k, G is n × p, and β and θ are conformable parameter vectors.

The model has no intercept. G is a matrix that indicates whether an observation belongs to one of p mutually exclusive and collectively exhaustive groups. So each column of G is a vector of ones and zeros. The value in column g is one if the observation belongs to group g and zero otherwise.

(a) While writing code to estimate the regression, another graduate student warns you: “don’t forget about the dummy variable trap! If there are p groups, you can only include p − 1 columns of G in the regression!” Is she right? Explain.

(b) (A freebie) It turns out that p is a very large number. Despite your best eﬀorts, you can’t convince your computer to calculate the least squares estimates because it requires inverting a (k + p) × (k + p) matrix. Write down the system of equations your computer is attempting to solve, and identify the problem matrix.

(c) Another helpful graduate student (who has already taken ECON 9720) says

“ no problem! You can calculate the least squares estimate of β without inverting that matrix!? She’s right. Prove it, and show there is a very easy way to compute the least squares estimate of β.

(d) Feeling proud of your accomplishment, you show your estimates β to your advisor. He says “that’s great. But what would be really interesting is an estimate of θ”. He assures you there is a very easy way to compute the least squares estimate of θ (i.e., you could do it by hand if you had to).

What is it?

(e) Your advisor is impressed with your eﬀort to this point, but he has one more question for you: “what proportion of the variation in y is explained by group membership?” Answer the question. (Decompose R2 into a proportion of variation explained by X and a proportion explained by group membership). 12. Consider the linear regression model yi = βxi + εi , where xi is a scalar, E[εi |xi ] = 0, E[ε2 |xi ] = σ 2 . However, the data on xi i have outliers and the researcher would like to avoid that those observations have impact on the estimation of β, so they perform the OLS only on data whose magnitude are less than some chosen constant c. The estimator is given by

˜

β=

n i=1 xi yi 1[|xi | < c]

,

n

2

i=1 xi 1[|xi | < c]

4

where 1[·] is an indicator function whose value is 1 if the statement in bracket is true and 0 otherwise.

˜

(a) Is β a consistent estimator of β? prove your claim.

˜

(b) What is the asymptotic distribution of β?

˜

(c) Compare β with β, the usual OLS estimator obtained from the whole sample.

(d) Suppose that the researcher wants to exclude outliers on the dependent variable yi instead. The estimator is

˜

β=

n i=1 xi yi 1[|yi | < c]

.

n

2

i=1 xi 1[|yi | < c]

Show that this estimator is inconsistent in general.

13.

(a) Lecture notes

(b) Homework 1

(c) Homework 2

(d) Homework 3

(e) Homework 4

Partitionned inverse formula:

A11

A21

A12

A22

−1

= with A−1 (I − A12 F2 A21 A−1 ) −A−1 A12 F2

11

11

11

−F2 A21 A−1

F2

11

F2 = (A22 − A21 A−1 A12 )−1

11

5…...

Premium Essay

...Econometrics = Science & art of using economic theory & statistical techniques to analyze economic data 1. Causal Effect & the Logic of Randomized Experiments Causal Relationships Decision depends on understanding relationships among variables Empirical research seek to reveal causal relationships: cause/treatment effect Treatment or costs = variables which are subject to intervention (change) Direction & magnitude of effects? Causal Question 1. Hormone Therapy does HRT risk of coronary events? Causal Question 2.Class size does redcing class size improce outcomes of elementary school? A. pupils get more attention, less class disruptions = better grades Smaller classes = expensive, only possible if they produce better outcomes Potential outcomes & treatment effects of binary treatments Outcome (yi) without treatment: Di = 0 Outcome (yi) with treatment: Di = 1 TE (treatment effect)= difference between potential outcomes: Counterfactuals: Fundamental problem of casual inference (Holland) Not able to observe both potential outcomes (y1i & y0i) (would need parallel world) Outcome that is not observable = counterfactual outcome Average treatment effects (ATE) Estimate average effect in target population (probability that y occurs when D has already happened) straightforward: simple comparison of means estimation of ATE 1. collect data from target population 2. identify individuals with/without treatment 3....

Words: 7829 - Pages: 32

Premium Essay

...Applied Econometrics BE5103 Tutorial 1 Q1 a) In the simple OLS regression estimation it is not possible that all actual independent Yi values lie above the estimated regression line. This is because OLS minimizes SUM ê2 , the residual , ê, is the difference between the actual Yi and the predicted Yi and has zero mean. In other words, OLS calculates the slope coefficient so that the difference between the predicted Yi and actual Yi is minimized. The OLS estimates of the βs: Are unbiased – the βs are centred around the true population values of βs Have minimum variance – the distribution of the β estimates around the true βs are as tight as possible Are consistent – as the sample size(n) approaches infinity, the estimated βs converge on the true βs. Are normally distributed. b) i) GDP = β0 + β1FDI + ê The expected sign of β1 would be +ve, as FDI increase by 1 unit, there will by an increase of GDP by β1 unit. This show positive relation between foreign direct investment and GDP. ii) GEXP = β0 + β1PGOPSE + ê The expected sign of β1 would be +ve, as Percentage growth of public school increase by 1%, there will be a β2 unit of increase in government expenditure (measured in unit of Government expenditure) iii) The regression would not make sense as there is no economic theory to prove the relationship between average hair length and tax rate. The β1 would have no meaning. Iv) UEMP = β0 - β1PR + ê The expected sign of β1 would be –ve, as participation rate......

Words: 1531 - Pages: 7

Premium Essay

...C h a p t e r One The Nature of Econometrics and Economic Data C hapter 1 discusses the scope of econometrics and raises general issues that result from the application of econometric methods. Section 1.3 examines the kinds of data sets that are used in business, economics, and other social sciences. Section 1.4 provides an intuitive discussion of the difficulties associated with the inference of causality in the social sciences. 1.1 WHAT IS ECONOMETRICS? Imagine that you are hired by your state government to evaluate the effectiveness of a publicly funded job training program. Suppose this program teaches workers various ways to use computers in the manufacturing process. The twenty-week program offers courses during nonworking hours. Any hourly manufacturing worker may participate, and enrollment in all or part of the program is voluntary. You are to determine what, if any, effect the training program has on each worker’s subsequent hourly wage. Now suppose you work for an investment bank. You are to study the returns on different investment strategies involving short-term U.S. treasury bills to decide whether they comply with implied economic theories. The task of answering such questions may seem daunting at first. At this point, you may only have a vague idea of the kind of data you would need to collect. By the end of this introductory econometrics course, you should know how to use econometric methods to formally evaluate a job training program or to test a...

Words: 363343 - Pages: 1454

Premium Essay

...MSc Money, Banking and Finance 2011/2012 Econ 403 – Applied Econometrics 2 hrs 30 minutes ------------------------------------------------- Answer ALL questions from Section A (60 marks), ONE question from Section B (20 marks), and ONE question from Section C (20 marks). Please use a separate answer book for each section. Section A – ANSWER ALL QUESTIONS Question 1 (24 marks) Assess whether each of the statements below is TRUE or FALSE. Marks will be awarded only for the explanation provided. a) When a relevant variable is omitted from a multiple linear regression the OLS estimates are always biased. (4 marks) b) Absence of correlation does not mean independence. (4 marks) c) The ACF never converges if the series is non-stationary. (4 marks) d) OLS estimates of a non-stationary model are super consistent. (4 marks) e) The can be used to determine the goodness of fit in a GARCH framework. (4 marks) f) The null hypothesis for the Durbin-Watson and co-integration Durbin-Watson are similar. (4 marks) Question 2 (36 marks) a) Explain the role of intercept and slope dummy variables in linear regression models by using an example. Use diagrams to illustrate where appropriate. (12 marks) b) Explain the difference between parametric and non-parametric measures of volatility. Give an example for each. (12......

Words: 1177 - Pages: 5

Premium Essay

...2871e+10 10936 1176923.06 -------------+-----------------------------Total | 1.3233e+10 10939 1209670.09 Number of obs F( 3, 10936) Prob > F R-squared Adj R-squared Root MSE = = = = = = 10940 102.46 0.0000 0.0273 0.0271 1084.9 -----------------------------------------------------------------------------earn | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------higrade | 105.7343 6.605693 16.01 0.000 92.78593 118.6826 age | 32.24626 11.72718 2.75 0.006 9.258862 55.23365 agesq | -.3568549 .1868963 -1.91 0.056 -.7232054 .0094956 _cons | -1221.05 186.2466 -6.56 0.000 -1586.127 -855.973 ------------------------------------------------------------------------------ . * Question 2 . * -----------. gen lessHS= (higrade < 12) if higrade~=. (320 missing values generated) . . gen moreHS= (higrade > 12) if higrade~=. (320 missing values generated) We note that the sample size of N = 10,904 is the same as in Question 1. I have chosen to define an indicator for less than high school education and an indicator for more than high school education. Thus, the base group is those with exactly 12 years of schooling. The value of having a high school diploma versus not having one is given by a $361.78 gain in earnings. The value of having more than a diploma compared to just 12 years of education is a $94.05 gain in earnings, but this number is not statistically different from zero (at the 0.1 significance level).......

Words: 1192 - Pages: 5

Free Essay

...Оценивание отдачи от образования. Данные из учебника Manno Verbeek “A guide to Modern Econometrics” http://www.econ.kuleuven.ac.be/GME/ Файл schooling содержит данные Национального панельного опроса 1976 года молодых мужчин (NLSYM, проживающих в США. Переменные в файле и их описание: ￼smsa66 1 if lived in smsa in 1966 1 Семинары по эконометрике, 2013 г. smsa76 1 if lived in smsa in 1976 nearc2 grew up near 2-yr college nearc4 grew up near 4-yr college nearc4a grew up near 4-year public college nearc4b grew up near 4-year private ed76 education in 1976 ed66 education in 1966 age76 age in 1976 college daded dads education (imputed avg nodaded 1 if dads education imputed momed mothers education nomomed 1 if moms education imputed momdad14 1 if lived with mom and dad sinmom14 1 if single mom at age 14 step14 1 if step parent at age 14 south66 1 if lived in south in 1966 south76 1 if lived in south in 1976 lwage76 log wage in 1976 (outliers trimmed) famed mom-dad education class (1-9) black 1 if black wage76 wage in 1976 (raw, cents per hour) enroll76 1 if enrolled in 1976 kww the kww score iqscore a normed IQ score mar76 marital status in libcrd14 1 if library card exp76 exp762 experience in 1976 exp76 squared 1976 (1 if married) in home at age 14 if missing) at age 14 1.1. Оцените простую линейную модель регрессии: reg lwage76 ed76 exp76 exp762 black smsa76 south76 est store ols 1.2. Проверка мультиколлинеарности: vif 1.3. Проверка......

Words: 505 - Pages: 3

Premium Essay

...Principles of Econometrics Tips for a Term Paper Topic Your work MUST BE ORIGINAL, but the issue/model/methodology need not! Money/Macro/International Economics Common Approaches 1. Apply a model or law (e.g., Phillips curve, Okun’s law, etc.) to more recent data. 2. Extend what is known for the U.S. to other countries (emerging, developing or Eastern European). Examples: 1. Outsourcing: Do firms that outsource tend to do better? Or why they outsource? 2. Trade deficit: What causes the huge US trade deficit? 3. Twin deficits: Is there a link between the trade deficit and the government budget deficit? 4. Foreign exchange: What has caused the recent drop of the US dollar? 5. Oil shocks: Have oil shocks led to recessions in the US or elsewhere? 6. Growth: Why some countries are rich while others poor? 7. Election: What determines an election outcome? 8. Big Mac Index Finance/Management/Accounting Common Approaches 1. What affects stock performance of different firms or over time? 2. Firm performance? Some Issues 1. Any link between the economy and the stock market? 2. How does monetary policy affect the financial markets? 3. Any link between stocks and bonds? Microeconomic/Socioeconomic/Marketing Issues General Approach: Apply any theory, model or concept to firms, people or markets. Some Issues 1. What affects the demand (or price) for a product? 2. Does money buy happiness? 3. Any link between market price (or profit) and......

Words: 921 - Pages: 4

Premium Essay

...Basic Econometrics, Fourth Edition Front Matter Preface © The McGraw−Hill Companies, 2004 PREFACE BACKGROUND AND PURPOSE As in the previous three editions, the primary objective of the fourth edition of Basic Econometrics is to provide an elementary but comprehensive introduction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level. In this edition I have attempted to incorporate some of the developments in the theory and practice of econometrics that have taken place since the publication of the third edition in 1995. With the availability of sophisticated and user-friendly statistical packages, such as Eviews, Limdep, Microﬁt, Minitab, PcGive, SAS, Shazam, and Stata, it is now possible to discuss several econometric techniques that could not be included in the previous editions of the book. I have taken full advantage of these statistical packages in illustrating several examples and exercises in this edition. I was pleasantly surprised to ﬁnd that my book is used not only by economics and business students but also by students and researchers in several other disciplines, such as politics, international relations, agriculture, and health sciences. Students in these disciplines will ﬁnd the expanded discussion of several topics very useful. THE FOURTH EDITION The major changes in this edition are as follows: 1. In the introductory chapter, after discussing the steps involved in traditional econometric......

Words: 394375 - Pages: 1578

Premium Essay

...e YOUR ECONOMETRICS PAPER BASIC TIPS There are a couple of websites that you can browse to give you some ideas for topics and data. Think about what you want to do with this paper. Econometrics is a great tool to market when looking for jobs. A well-written econometrics paper and your presentation can be a nice addition to your resume. You are not expected to do original research here. REPLICATION of prior results is perfectly acceptable. Read Studenmund's Chapter 11. One of the most frustrating things in doing an econometrics paper is finding the data. Do not spend a lot of time on a topic before determining whether there is data available that will allow you to answer your question. It is a good idea to write down your ideal data set that would allow you to address your topic. If you find that the available data is not even close to what you had originally desired, you might want to change your topic. Also, remember that knowing the location of your data – website, reference book, etc – is not the same as having your data available to use. It may take a LONG time to get the data in a format that EVIEWS can read. Do not leave this till the last minute. For most data, I enter the data into Excel first. I save the Excel sheet in the oldest version, namely MS Excel Worksheet 2.1 . The reason is that format can be read by most programs whereas newer formats may or may not be read. Eviews easily reads an Excel sheet 2.1 version. You should......

Words: 2376 - Pages: 10

Premium Essay

...A Guide to Modern Econometrics 2nd edition Marno Verbeek Erasmus University Rotterdam A Guide to Modern Econometrics A Guide to Modern Econometrics 2nd edition Marno Verbeek Erasmus University Rotterdam Copyright 2004 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 Email (for orders and customer service enquiries): cs-books@wiley.co.uk Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to permreq@wiley.co.uk, or faxed to (+44) 1243 770620. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the Publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required,...

Words: 194599 - Pages: 779

Premium Essay

...ACKNOWLEDGEMANT First of all, we are grateful to The Almighty God for establishing strength to us to complete this report that given by our lecturer. We are really grateful because we manage to complete our assignment in subject Econometrics within the time given by our beloved lecturer. We are very thankful to our Econometrics lecturer, Sir Abdul Razak Bin Jambari, for his valuable guidance, encouragement, and co-operation until the report have been done smoothly and successfully. He really helps us to giving ideas to do a research on this subject and also give some new skills and information that we did not have before. It is so valuable for us to know more about this subject and hopefully we can use it in future. This assignment cannot be complete without the effort and co-orperation from our group member. It is a great opportunity for us to do some research and write a report about relationship of KLCI between M2, price of gold, interest rate and CPI. At the time of preparing this report, we had gone through some research by using several methods for collecting the data and explained it through Eviews 8. Although this subject is new in UiTM Segamat and especially in Degree of Investment, by expertise from our lecturer, we able to do this assignment smoothly. Last but not the least, we would like to express our gratitude to our friends whose help us in guiding us collecting the data and complete this assignment. We hope this assignment can give useful knowledge to......

Words: 1140 - Pages: 5

Premium Essay

...ECONOMETRICS INTRODUCTION We will examine the data set of wealth, age, family size and income. In this data, our dependent variable is wealth and independent variables are age, family size and income. We will estimate and interpret how independent variables affect the dependent variable. We will use that equation: WEALTH = c1+ c2INCOME + c3AGE + c4FSIZE + u DATA AND METHODOLOGY The data of wealth is unstructured or undated and it include 9275 observations and we will use all observations thereby we will not generate a sample. Ordinary least square method will be used to estimate the equation that is above. ESTIMATION AND RESULTS After the model is estimated the new equation is: WEALTH = c1+ c2INCOME + c3AGE + c4FSIZE + u WEALTH = -52.6737937357 + 0.973735136211(INCOME) + 1.01304960998(AGE) - 2.80563662208(FSIZE) Dependent Variable: WEALTH | | | Method: Least Squares | | | Date: 01/29/16 Time: 06:42 | | | Sample: 1 9275 | | | | Included observations: 9275 | | | | | | | | | | | | | Variable | Coefficient | Std. Error | t-Statistic | Prob. | | | | | | | | | | | C | -52.67379 | 2.829309 | -18.61719 | 0.0000 | INCOME | 0.973735 | 0.025377 | 38.37042 | 0.0000 | AGE | 1.013050 | 0.059022 | 17.16389 | 0.0000 | FSIZE | -2.805637 | 0.398602 | -7.038697 | 0.0000 | | | | | | | | | | | R-squared | 0.173474 | Mean dependent var |......

Words: 563 - Pages: 3

Premium Essay

...This page intentionally left blank Introductory Econometrics for Finance SECOND EDITION This best-selling textbook addresses the need for an introduction to econometrics speciﬁcally written for ﬁnance students. It includes examples and case studies which ﬁnance students will recognise and relate to. This new edition builds on the successful data- and problem-driven approach of the ﬁrst edition, giving students the skills to estimate and interpret models while developing an intuitive grasp of underlying theoretical concepts. Key features: ● Thoroughly revised and updated, including two new chapters on ● ● ● ● ● ● panel data and limited dependent variable models Problem-solving approach assumes no prior knowledge of econometrics emphasising intuition rather than formulae, giving students the skills and conﬁdence to estimate and interpret models Detailed examples and case studies from ﬁnance show students how techniques are applied in real research Sample instructions and output from the popular computer package EViews enable students to implement models themselves and understand how to interpret results Gives advice on planning and executing a project in empirical ﬁnance, preparing students for using econometrics in practice Covers important modern topics such as time-series forecasting, volatility modelling, switching models and simulation methods Thoroughly class-tested in leading ﬁnance schools Chris Brooks is Professor of......

Words: 195008 - Pages: 781

Premium Essay

...Impact of Education on Growth 27.05.2015 Applied Econometrics İpek Tuğrul 15415 İpek Tuğrul 15415 27.05.2015 Term paper: Impact of Education on Growth As a basic theory which is always taught in economics, the effect of education on the economic growth of a country is always positive. In order to further verify this theory, the authors of many books provide examples which complement it. If we go deep into this theory and search for other evidence, we come across different researches done by economists and statisticians who have analyzed the theory to a great extent. In order to differentiate their studies they have done their empirical research on the subject by using different econometric models. Even though the underlying result i.e. derived from their research is the same, yet these models have different comprehensive implications, something which will also be discussed in our review. As a part of our review we have taken four different research paper written by different researchers, which have the same fundamental scope. However, the origins of these researches are subject to geographical changes and this has been done in order to prove our basic rationality of the theory. The critical reviews consist of the methods used by the authors and the way they have tried to analyze the empirical evidence by using econometric models. The first article which we will be reviewing in our...

Words: 3086 - Pages: 13

Free Essay

...JOURNAL OF APPLIED ECONOMETRICS J. Appl. Econ. 23: 925– 948 (2008) Published online 7 November 2008 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/jae.1036 ECONOMETRICS OF AUCTIONS BY LEAST SQUARES LEONARDO REZENDE* PUC-Rio, Rio de Janeiro, Brazil; and University of Illinois at Urbana–Champaign, Illinois, USA SUMMARY I investigate using the method of ordinary least squares (OLS) on auction data. I ﬁnd that for parameterizations of the valuation distribution that are common in empirical practice, an adaptation of OLS provides unbiased estimators of structural parameters. Under symmetric independent private values, adapted OLS is a specialization of the method of moments strategy of Laffont, Ossard and Vuong (1995). In contrast to their estimator, here simulation is not required, leading to a computationally simpler procedure. The paper also discusses using estimation results for inference on the shape of the valuation distribution, and applicability outside the symmetric independent private values framework. Copyright 2008 John Wiley & Sons, Ltd. Received 15 September 2006; Revised 1 July 2008 1. INTRODUCTION The ﬁeld of econometrics of auctions has been successful in providing methods for the investigation of auction data that are well grounded in economic theory and allow for inference on the structure of an auction environment. Today, a researcher has a number of alternative structural methods, especially within the independent private-values......

Words: 12659 - Pages: 51