•0 gostou•14 visualizações

Denunciar

Compartilhar

Baixar para ler offline

hgh

- 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*
- 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 …
- 9. Can you think of any examples….???
- 10. Econometric analysis requires data DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA
- 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
- 12. Cross-sectional data Observation number Hourly wage Categorical variables (1=yes, 0=no)
- 13. Time series data Unemployment rate Average coverage rate Average minimum wage for given year Gross national product
- 14. Pooled cross sections Number of bathrooms Size of house in square feet Property tax Before reform After reform
- 15. Panel or longitudinal data Each city has two time series observations Number of police in 1986 Number of police in 1990
- 16. Correlation and Causation Reference: https://twitter.com/SteveStuWill/status/1309968897391517696/photo/1
- 17. Correlation and Causation Reference: https://psychologenie.com/meaning-of-correlation-does-not-imply-causation-explained
- 18. Correlation and Causation Reference: https://twitter.com/EUFIC/status/1324667630238814209/photo/1
- 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
- 23. Simple Linear Regression Fitted regression line
- 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
- 25. Simple Linear Regression Fitted regression line Fit as good as possible a regression line through the data points:
- 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
- 30. Assumptions
- 31. Assumptions
- 32. Assumptions
- 34. Assumptions
- 35. Assumptions
- 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
- 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
- 42. Practice Questions Chapter 1, Wooldridge: • Problem 1 (pp. 17) • Computer exercise C4-JTRAIN2 (pp. 18) Chapter 2, Wooldridge: • Computer exercise C1-401K (pp. 61) • Computer exercise C2-CEOSAL2 (pp. 61) • Computer exercise C4-WAGE2 (pp. 62) • Computer exercise C5-RDCHEM (pp. 62)
- 43. Reference • Chapter 1 and 2, Jeffery M. Wooldridge, (2016). Introductory econometrics: A modern approach. Fifth Edition, Cengage Learning.
- 44. Thank you

- 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.
- dy/dx = beta 1 (du/dx=0)