Prof. Chitwan Lalji teaches economics at the Indian Institute of Management Kozhikode. The document discusses key concepts in econometrics including:
1) Econometrics uses statistical methods to analyze economic data and test economic theories using real-world data.
2) The main steps in econometrics analysis are developing an economic theory, specifying an econometric model, and conducting hypothesis tests.
3) Econometric models relate an outcome variable like demand or wages to explanatory variables based on economic theory and include an error term for unobserved factors. These models are used to test hypotheses about economic relationships.
The aim of this course is to equip the students with the necessary skills, including both the acquisition of habits of thought and knowledge of the techniques of modern econometrics.
The course is application oriented.
The course also aims to provide students with the ability to use appropriate software in an effective manner.
The aim of this course is to equip the students with the necessary skills, including both the acquisition of habits of thought and knowledge of the techniques of modern econometrics.
The course is application oriented.
The course also aims to provide students with the ability to use appropriate software in an effective manner.
This presentation on Introduction to Statistics helps Engineering students to review the fundamental topics of statistics. It is according tl syllabus of Institute of Engineering (IOE) but is similar to that of almost all the engineering colleges.
Lecture 2:
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Types of statistics
Types of research and types of statistics
Levels of measurement
Rules of using measurement
Hands on: Graphical Descriptive Techniques
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
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Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This presentation on Introduction to Statistics helps Engineering students to review the fundamental topics of statistics. It is according tl syllabus of Institute of Engineering (IOE) but is similar to that of almost all the engineering colleges.
Lecture 2:
What is statistics?
Types of statistics
Types of research and types of statistics
Levels of measurement
Rules of using measurement
Hands on: Graphical Descriptive Techniques
Format of asking
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
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This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
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• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
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A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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Day 1_ Introduction.pptx
1. Prof. Chitwan Lalji
Economics Area
Indian Institute of Management Kozhikode
*Text Books: Wooldridge, J. M. (2016). Introductory econometrics: A modern approach. Nelson Education.
Enders, Walter (2005). Applied Econometric Time Series, 4th ed., Wiley.
Econometric Applications for
Research*
2.
3. What is econometrics..?
• It is the use of statistical methods to analyze economic data
• It is science/art of testing economic theories
• It is the process of fitting mathematical economic models to real-world data
• It is a set of tools used for forecasting future values of economic variables, such as a
firm’s sales, the overall growth of the economy, or stock prices.
• Evaluating and implementing government and business policy
• Science and art of using historical data to make numerical, or quantitative, policy
recommendations in government and business
4. Steps in econometrics analysis?
1. Economic Theory
2. Econometric model
3. Hypothesis Testing
5. Economic Theory
Economic Theory
• Demand Theory – shows the relationship between price and quantity
of a good that consumers are willing to buy at a given price, holding
constant other factors that might affect the quantity demanded.
QD = QD(P)
• Others factors – The quantity that consumers are willing to buy also
depends on their income, price of related goods, etc.
QD = f(Price of good, Income, Price of related goods, etc.)
6. Econometric Model
Economic Theory
QD = f(Price of good, Income, Price of related goods, etc.)
Econometric Model
QD = α + β(Price of good) + γ(Income) + δ(Price of related goods) + ε
Unobserved factors, such
as tastes, habits,
expectations, etc.
Dependent variable,
explained variable,
response variable,…
Independent variable(/s),
explanatory variable(/s),
regressor(/s),…
7. Another example
Model of job training and worker productivity
– What is effect of additional training on worker productivity?
– Formal economic theory not really needed to derive equation:
– Other factors may also be relevant
Hourly wage
Years of formal
education Years of work-
force experience
Weeks spent
in job training
8. Another example
Econometric model of job training and worker productivity
• Most of econometrics deals with the specification of the error
• Econometric models may be used for hypothesis testing
– For example, the parameter represents effect of training on wage
– How large is this effect? Is it different from zero?
Hourly wage Years of formal
education
Years of work-
force experience
Weeks spent
in job training
Unobserved deter-
minants of the wage
e.g. innate ability,
quality of education,
family background …
11. Classification of Data
Qualitative data and Quantitative data
Primary data and secondary data
Cross-sectional, pooled-cross-sectional, time series data and panel data
19. Causation effect
Definition of causal effect of x on y:
"How does variable y change if variable x is changed
but all other relevant factors are held constant“
21. Simple Linear Regression
Definition of the simple linear regression model
Dependent variable,
explained variable,
response variable,…
Independent variable,
explanatory variable,
regressor,…
Error term,
disturbance,
unobservables,…
Intercept Slope parameter
"Explains variable y in terms of variable x"
22. Simple Linear Regression
• Conditional mean independence assumption
The explanatory variable must not
contain information about the mean
of the unobserved factors
24. Simple Linear Regression
Properties of OLS on any sample of data
• Fitted values and residuals
• Algebraic properties of OLS regression
Fitted or predicted values Deviations from regression line (= residuals)
Deviations from
regression line sum up
to zero
Correlation between
deviations and regressors
is zero
Sample averages of y
and x lie on regression
line
27. Simple Linear Regression
What does "as good as possible" mean?
• Regression residuals
• Minimize sum of squared regression residuals
• Ordinary Least Squares (OLS) estimates
28. Simple Linear Regression
• Goodness-of-Fit
• Variation
"How well does the explanatory variable explain the dependent variable?"
29. Simple Linear Regression
• Decomposition of total variation
• Goodness-of-fit measure (R-squared)
Total
variation
Explained
part
Unexplained
part
R-squared measures the fraction
of the total variation that is
explained by the regression
36. Assumptions of CLRM
• Linear in Parameter
• Random Sampling
• Sample variation on the explanatory variable (not all the same value)
• Zero conditional mean: E(u|x) = 0
Cov(x,u)=0
• Variance of the unobservable (u) conditional on x, is constant
Homoskedasticity or same variance assumption
The value of the explanatory variable must
contain no information about the
variability of the unobserved factors
37.
38. Log and semi log form
• Incorporating nonlinearities: Semi-logarithmic form
• Regression of log wages on years of eduction
• This changes the interpretation of the regression coefficient:
Natural logarithm of wage
Percentage change of
wage
… if years of education
are increased by one year
39. Log and semi log form
• Incorporating nonlinearities: Log-logarithmic form
• CEO salary and firm sales
• This changes the interpretation of the regression coefficient:
Natural logarithm of CEO salary
Percentage change of salary
… if sales increase by 1 %
Natural logarithm of his/her firm‘s sales
Logarithmic changes are
always percentage changes
40. Example on STATA
Use the following data
• CEOSAL1 data
Please note:
The datasets and do files will be made available in the virtual classroom/moodle.
41. Assumptions of CLRM
• Linear in Parameter
• Random Sampling
• Sample variation on the explanatory variable (not all the same value)
• Zero conditional mean: E(u|x) = 0
Cov(x,u)=0
• Variance of the unobservable (u) conditional on x, is constant
Homoskedasticity or same variance assumption
The value of the explanatory variable must
contain no information about the
variability of the unobserved factors
1. Sample of individuals, households, firms, cities, states, countries, or other units of interest at a given point of time/in a given period
2. Cross-sectional observations are more or less independent
3. Pure random sampling from a population
4. Represent the population!
5. Ordering of observations does not matter
6. Typical applications: applied microeconomics
1. Observations of a variable or several variables over time
2. For example, stock prices, money supply, consumer price index, gross domestic product, annual homicide rates, automobile sales, …
3. Time series observations are typically serially correlated
4. Ordering of observations conveys important information
5. Data frequency: daily, weekly, monthly, quarterly, annually, …
6. Typical features of time series: trends and seasonality
7. Typical applications: applied macroeconomics and finance
1. Two or more cross sections are combined in one data set
2. Cross sections are drawn independently of each other
3. Pooled cross sections often used to evaluate policy changes
Example:
Evaluate effect of change in property taxes on house prices
- Random sample of house prices for the year 1993
- A new random sample of house prices for the year 1995
- Compare before/after (1993: before reform, 1995: after reform)
1. The same cross-sectional units are followed over time
2. Panel data have a cross-sectional and a time series dimension
3. Panel data can be used to account for time-invariant unobservables
4. Panel data can be used to model lagged responses
Example:
City crime statistics; each city is observed in two years
Time-invariant unobserved city characteristics may be modeled
Effect of police on crime rates may exhibit time lag
Correlation is the degree of association or relationship between two variables.
Causation refers to 1 variable causing the other variable, how one affects impacts the other.
Correlation shows the relationship between the two variables, while regression allows us to see how one affects the other, keeping other things constant – citrus paribus.
Example: more ice cream consumption and high deaths due to heart diseases >>>> caused by temperature/heat and not more ice cream consumption. Regressions helps to find the relationship between two variables, keeping other things constant.