Econometrics and Economic Data PDF

Econometrics Types of economic data Econometric models use economic data Types of economic data Cross-sectional data Time series data Pooled cross sections Panel/longitudinal data Which econometric models we apply depends on the type of data used Cross-sectional dat...

Econometrics Types of economic data Econometric models use economic data Types of economic data Cross-sectional data Time series data Pooled cross sections Panel/longitudinal data Which econometric models we apply depends on the type of data used Cross-sectional data Data for people, households, businesses, countries, cities, etc. Data are at a given point of time/during a given period – no time dimension Typically denote the individual by i Widely used in microeconomics Example: cross-sectional data on wages Wage yi expersq Hourly Female x4i lwage (experience wage Educ x1i Exper x2i Tenure x3i Female=1 Married x5i (log wage) squared) 3.1 11 2 0 1 0 1.131402 4 3.24 12 22 2 1 1 1.175573 484 3 11 2 0 0 0 1.098612 4 6 8 44 28 0 1 1.791759 1936 5.3 12 7 2 0 1 1.667707 49 8.75 16 9 8 0 1 2.169054 81 11.25 18 15 7 0 0 2.420368 225 5 12 5 3 1 0 1.609438 25 Note i, yi, x1i, etc. Every observation/row is for person i. Time series data Data can be macroeconomic, financial, etc. Examples: stock and bond prices, GDP, growth rates Observations are over time The time dimension can be annual, monthly, daily, etc. Time series may have trend (e.g. rising values over time), seasonality (e.g. higher values in a given month), and cycles (e.g. every 3-5 years). Observations may be serially correlated (errors are correlated from one period to the next). Time series data on minimum wages Prunemp x3t Avgmin yt Avgcov x1t Prgnp x2t Umemployme Year t Avg min wage Avg coverage GDP nt rate 1950 0.198 0.201 878.7 15.4 1951 0.209 0.207 925 16 1952 0.225 0.226 1015.9 14.8 1953 0.311 0.231 1081.3 14.5 1954 0.313 0.224 1104.4 15.3 1955 0.369 0.236 1138.5 13.2 1956 0.447 0.245 1185.1 13.3 1957 0.488 0.244 1221.8 12.8 Note t, yt, x1t, etc. Every observation/row is at time t. Pooled cross sections Example: house prices in two periods but not the same houses are sold. Two or more cross sections Cross sections are drawn independently of each other Used to estimate effect of new policy, for example effect of new tax on house prices Pooled cross-sectional data on house prices Year t Price yt Rooms x1t Baths x2t lprice y81 1978 60000 7 1 11.0021 0 1978 40000 6 2 10.59663 0 1978 34000 6 1 10.43412 0 1978 63900 5 1 11.06507 0 1981 49000 6 1 10.79958 1 1981 52000 5 1 10.859 1 1981 68000 6 2 11.12726 1 1981 54000 6 1 10.89674 1 Note t, yt, x1t, etc. Every observation is at time t. Before and after period. Panel data or longitudinal data Example: employment data across individuals and over time Same cross-sectional units over time Have both cross-sectional i and time series t dimensions Panel data on wages Person id Year lwage Exper Educ Hours i t yit x1it x2it x3it 13 1980 1.19754 1 14 2672 13 1981 1.85306 2 14 2320 13 1982 1.344462 3 14 2940 17 1980 1.675962 4 13 2484 17 1981 1.518398 5 13 2804 17 1982 1.559191 6 13 2530 18 1980 1.515963 4 12 2332 18 1981 1.735379 5 12 2116 18 1982 1.631744 6 12 2500 Note i, t, yit, x1it, etc. Every observation is for unit i at time t. Causation versus correlation Causation: An additional year of education causes wages to increase by a given amount, all else equal. Correlation: An additional year of education is associated with higher wages. For most economic studies, a causation cannot be determined. It is only correlation. Example on causation vs correlation Causal effect of fertilizer on crop yield Will crop yield increase signficantly if fertilizer is applied? Implicit assumption: all other factors that influence crop yield such as quality of land are held fixed Experiment Choose several plots of land; randomly assign different amounts of fertilizer to the different plots and compare crop yield Valid experiment because fertilizer that is applied is not related to other factors influencing crop yields Observational study Plots that have different levels of fertilizer and different yields. If there is a positive correlation (plots with more fertilizer have more yields), does that mean that applying more fertilizer will result in a higher yield? Hint: this is a correlation, but not causation. Example of causation vs correlation Effect of education on wages If a person at random is given another year of education by how much will his/her wage increase? Implicit assumption: all other factors that influence wages such as experience and ability are held fixed Experiment Choose some people at random and assign them to get more education – not feasible. Problem without random assignment: amount of education is related to other factors that influence wages (such as intelligence) Observational study Data on people with wages and education. Interpretation: people with higher education have higher wages (correlation). Interpretation is not: if a person gets additional education, he/she will get higher wages (causation). Econometrics and economic data – review questions 1. Define econometrics, economic model, and econometric model. What are some goals of econometric analysis? 2. What are the main types of economic data in econometrics? Give examples for each type of economic data. 3. Which data/models have i or t dimension? How many dependent and independent variables do econometric models have? 4. Does a regression imply that there is a causal relationship or correlation between the dependent and independent variables? Give examples.