Omitted Variable Bias
Ommitted variables may increase the apparent importance of another variable, damaging the ability to prove causality. Effect of OVB on
1. Find the variable outside of the model
2. Find Corr(ZY)
3. Find Corr (ZX)
4. Multiply the signs
5. If positive, there is an upwards bias ()
Adjusted R2

OLS Wonder Equation

A good model for proving causality has a low , a good model for predicting Y has a low R2

Multiple Variable Tests

Reparametrisation

1.
2. For showing
3. Let
4. Thus,
5.
6. Now, let
7. Thus,
8. Now, run a new regression and do the usual hypothesis tests (: a one sided test). If you can reject H0, then

F-stat tests
Here, the H0 is a joint hypothesis with n restrictions (the number of coefficients equated to 0). 1. Create a restricted regression whereby we assume that H0 is correct 2. We see how the “fit” of the regression changes with the removed variables a.

b.
c. q is the number of restrictions, n the number of observations and k the number of variables 3. We compare this with an Fcrit value (using number of...

...ECONOMETRIC ANALYSIS.
INDEX:
- Introduction..................................................................................3
-Background....................................................................................8
-Empirical Analysis.........................................................................9
-Conclusion.....................................................................................31
-Bibliography..................................................................................31
*
INTRODUCTION:
For many years it has tried to explain and predict economic phenomena. In the present work we destructive her to perform an econometric study of the function of the number of travelers who occupy tourist accommodation in Andalusia. The data required for such analysis have been collected from the database of the Institute of statistics and cartography of Andalusia, for easy access through the official website (http://www.juntadeandalucia.es/institutodeestadisticaycartografia/index.html).
The National Statistical Institute(NSI) sends every month to the Institute of statistics of Andalusia provisional results which offers survey during the previous month in the eight Andalusian provinces. The survey is exhaustive in all provinces, except in some categories where sampling procedures are performed.
The estimates are published disaggregated by categories up to the level that allows the maintenance of...

...Introduction to Econometrics coursework
For the assignment I will examine whether or not a linear regression model is suitable for estimating the relationship between Human development index (HDI) and its components. Linear Regression is a statistical technique that correlates the change in a variable to other variable/s, the representation of the relationship is called the linear regression model.
Variables are measurements of occurrences of a recurring event taken at regular intervals or measurements of different instances of similar events that can take on different possible values. A dependent variable is a variable whose value depends on the value of other variables in a model. Hence, an independent variable is a variable whose value is not dependent on other variables in a model.
The dependent variable here is HDI and this will be regressed against the independent variables which include Life expectancy at birth, Mean years of schooling, expected years of schooling and Gross National Income per capita Hence we can model this into Yi = b0 + b1 xi + b2 xi + b3 xi + b4 xi + where Y is HDI, β0 is a constant, β1 β2 β3 β4 are the coefficients and denotes for random/error term.
R2 is how much your response variable (y) is explained by your explanatory variable (x). The value of R2 ranges between 0 and 1, and the value will determine how much of the independent variable impacts on the dependent variable. The R2 value will show how reliable the...

...Crime Rates: An Econometric Analysis using population, unemployment and growth
Table of Contents
I. Introduction
A.) Background of the Study
B.) Problem Statement
C.) Objectives
D.) Significance of the Study
E.) Scope and Limitations
II. Review of Related Literature
III. Operational Framework
A.) Variable List
B.) Model Specification
C.) A-priori Expectations
IV. Methodology
A.) Data
B.) Preliminary Tests
V. Results and Discussions
VI. Conclusion and Recommendations
VII. Bibliography
VIII. Appendices
INTRODUCTION
A. Background of the Study
Ever since the first civilizations, ever since the dawn of government and morals, crime has accompanied mankind in his everyday life. Whether it is in the streets or at home, in the office or in workplace, crime is always present. Since then, governments have exhausted billions of dollars in order to eradicate crime. Unfortunately, crime cannot be totally eradicated, unless that world or country is a utopia.
Here in the Philippines, crime is one of the most problematic dilemmas. However, in the midst of volatile economies, wars, corruption, poverty, and other more urgent problems, crime loses its significance, letting nature take its course unfazed by some if not no government intervention. It is then relegated to the bottom of the long list of problems that we experience here in the Philippines. Moreover, because it has become a part of our...

...Estimate the equation in (1) but now compute the heteroskedasticity-robust
standard errors. (Use the command: reg profits assets mcontrol, robust)
Construct a 95% confidence interval for β2 and compare it with the nonrobust
confidence interval from part (a).
(c) Compute the Breusch-Pagan test for heteroskedasticity. Form both the F and
LM statistics and report the p values. (You can use the command hettest to
perform the LM version of the Breusch-Pagan test in Stata.)
(d) Compute the special case of the White test for heteroskedasticity. Form both
the F and LM statistics and report the p values. How does this compare to
your results from (c)? (You can use the command imtest, white to perform
the LM version of the original White test in Stata. Note that this test statistic
will be different from the one you compute for the special case of the White
test.)
(e) Divide all of the variables (including the intercept term) in equation (1) by the
square root of assets. Re-estimate the parameters of (1) using these data. What
are these estimates known as? These new estimates are BLUE if what is true
about the disturbances in (1)?
(f) Conduct White’s test for the disturbances in (e) being homoskedastic. What do
you conclude at the 5% level?
(g) Construct a 95% confidence interval for β2 using the estimates from the regression in (e). Are you sympathetic to the claim that managerial control has no
large effect on corporate profits?
3
2. Let arr86 be a binary variable...

...ECO
1 chapter
An overview of regression analysis
Econometrics – literally ,,economic measurement” is the quantitative measurement and analysis of actual economic and business phenomena.
Econometrics has three major uses:
1) Describing economic reality
2) Testing hypothesis about economic theory
3) Forecasting future economic activity
The simplest use of econometrics is description.
For most goods, the relationship between consumption and disposable income is expected to be positive, because an increase in disposable income will associated with increase in the consumption of the goods.
Consumer demand for a particular commodity often can be thought of as relationship between the quantity demanded (Q) and the commodity’s price (P), the price of a substitute good (Ps), and disposable income (Yd).
The second and perhaps most common use of econometrics is hypothesis testing – the evaluation of alternative theories with quantitative evidence.
Normal good – one for which the quantity demanded increases when disposable income increases.
The third and most difficult use of econometrics is to forecast or predict what is likely to happen next quarter, next year, or further into the future, based on what has happened in the past.
Nonexperimental quantitative research:
1) Specifying the models or relationships to be studied
2) Collecting the data needed to...

...051 × 9.75 + 8.827 = 14.75 (h) Jim’s expected years of education is 2 × 0.0315 = 0.0630 less than Bob’s. Thus, Jim’s expected years of education is 14.75 − 0.063 = 14.69.
3.
Variable growth rgdp60 tradeshare yearsschool rev_coups assasinations oil Mean 1.86 3131 0.542 3.95 0.170 0.281 0 Standard Deviation 1.82 2523 0.229 2.55 0.225 0.494 0 Units Percentage Points $1960 unit free years coups per year assasinations per year 0–1 indicator variable
(b) Estimated Regression (in table format):
Regressor tradeshare Coefficient 1.34 (0.88) 0.56** (0.13) −2.15* (0.87) 0.32 (0.38) −0.00046** (0.00012) 0.626 (0.869) 1.59 0.29 0.23
yearsschool rev_coups assasinations rgdp60 intercept SER R2 R2
116
Stock/Watson - Introduction to Econometrics - Second Edition
The coefficient on Rev_Coups is −2.15. An additional coup in a five year period, reduces the average year growth rate by (2.15/5) = 0.43% over this 25 year period. This means the GPD in 1995 is expected to be approximately .43×25 = 10.75% lower. This is a large effect. (c) The 95% confidence interval is 1.34 ± 1.96 × 0.88 or −0.42 to 3.10. The coefficient is not statistically significant at the 5% level. (d) The F-statistic is 8.18 which is larger than 1% critical value of 3.32.
Chapter 7
Hypothesis Tests and Confidence Intervals in Multiple Regression
Solutions to Empirical Exercises
1. Estimated Regressions Model Regressor Age Female Bachelor Intercept 3.32 (0.97) 8.66 0.023 0.022 7.88...

...Rural poverty remains a critical economic problem in many developing countries. This paper conducts an econometric analysis of data from the 2006 Vietnam Household Expenditure Survey to assess the impact of selected socio-economic factors on the income of Vietnamese households. The data that is used is cross sectional in that it is widely discrete data (such as per capita income) relating to one period or without respect to variance due to time.
Jehovaness Aikaeli, in his research report, “Determinants of Rural Income in Tanzania:An Empirical Approach”, carries out a study from the 2005 Tanzania Rural Investment Climate Survey to assess the impact of selected socio-economic and geographic factors on the income of rural households and communities. What he found out was that improvement in four variables: the level of education of the household head, size of household labor force, acreage of land use and ownership of a non-farm rural enterprise had a significant positive impact on the incomes of rural households (Aikaeli vi). I will use his paper as a guide to my research paper; however, I will not be using household labor force, acreage of land use and ownership of non-farm rural areas as I do not have the data for it.
Steve Onyeiwu, in his paper, “Determinants of Income Poverty in Rural Africa: Empirical Evidence from Kenya and Nigeria”, uses panel and cross- sectional regressions, with socio-economic and demographic survey data collected from...

...Answers to Selected
Exercises
For
Principles of Econometrics, Fourth Edition
R. CARTER HILL
Louisiana State University
WILLIAM E. GRIFFITHS
University of Melbourne
GUAY C. LIM
University of Melbourne
JOHN WILEY & SONS, INC
New York / Chichester / Weinheim / Brisbane / Singapore / Toronto
CONTENTS
Answers for Selected Exercises in:
Probability Primer
1
Chapter 2
The Simple Linear Regression Model
3
Chapter 3
Interval Estimation and Hypothesis Testing
12
Chapter 4
Prediction, Goodness of Fit and Modeling Issues
16
Chapter 5
The Multiple Regression Model
22
Chapter 6
Further Inference in the Multiple Regression Model
29
Chapter 7
Using Indicator Variables
36
Chapter 8
Heteroskedasticity
44
Chapter 9
Regression with Time Series Data: Stationary Variables
51
Chapter 10
Random Regressors and Moment Based Estimation
58
Chapter 11
Simultaneous Equations Models
60
Chapter 15
Panel Data Models
64
Chapter 16
Qualitative and Limited Dependent Variable Models
66
Appendix A
Mathematical Tools
69
Appendix B
Probability Concepts
72
Appendix C
Review of Statistical Inference
76
29 August, 2011
PROBABILITY PRIMER
Exercise Answers
EXERCISE P.1
(a)
X is a random variable because attendance is not known prior to the outdoor concert.
(b)
1100
(c)
3500
(d)
6,000,000
EXERCISE P.3...