计量经济学导论chapter19课件

1、计量经济学导论chapter19课件计量经济学导论chapter19课件Finding interesting research questionsChoose the area of economics/social sciences you are interested inExamples for typical research questionsLabor Economics: Explaining wage differentialsPublic Economics: Effect of taxes on economic activityEducation Economics:

2、Effect of spending on school performanceMacroeconomics: Effect of investment on GNP growthLook for published papers on the chosen topic using tools such as EconLit, Google Scholar, the Journal of Economic Literature (JEL) etc.Carrying out an Empirical ProjectFinding interesting research qYour resear

3、ch project should add something newAdd a new variable whose influence has not been studied beforeExpand economic questions to include factors from other sciencesStudy an existing question for more recent data (may be boring)Use a new data set or study a question for a different countryTry out new/al

4、ternative methods to study an old questionFind a completely new question (hard but possible)It helps if your research question is policy relevant or of local interestCarrying out an Empirical ProjectYour research project should aLiterature reviewA literature review is important to place your paper i

5、nto contextUse online search services to systematically search for literatureWhen searching, think of related topics that may also be relevantA literature review can be part of introduction or a separate sectionData collectionMost questions can be addressed using alternative types of data (pure cros

6、s-sections, repeated cross-sections, time series, panels)Carrying out an Empirical ProjectLiterature reviewCarrying out Deciding on the appropriate data setMany questions can in principle be studied using a single cross-sectionBut for a reasonable ceteris paribus analysis one needs enough controlsPa

7、nel data provides more possibilities for convincing ceteris paribus analyses as one can control for time-invariant unobserved effectsExamples for panel data sets: PSID (individuals), Compustat (firms)Panel data for cities, counties, states etc. are often publicly availableData sets are often availab

8、le online, in journal archives, or from authorsCarrying out an Empirical ProjectDeciding on the appropriate daEntering and storing your dataData formats: 1) printed, 2) ASCII, 3) spreadsheet, 4) software specificImportant identifiers: 1) observational unit, 2) time periodTime series must be ordered

9、according to time periodPanel data are conveniently ordered as blocks of individual dataIt is always important to correctly identify and handle missing valuesNonnummerical data also have to be handled with great careSoftware specific formats often provide good ways of documentation Carrying out an E

10、mpirical ProjectEntering and storing your dataInspecting, cleaning, and summarizing your dataIt is extremely important to become familiar with your data setEven data sets that were used before may contain problems/errorsLook at individual entries/try to understand the structure of your dataUnderstan

11、d how missing values are coded; if they are coded as “999“ or “-1“, this can be extremely dangerous for your analysisIt is better to use nonnummerical values for missing valuesUnderstand the units of measurement of your variablesCarrying out an Empirical ProjectInspecting, cleaning, and summInspecti

12、ng, cleaning, and summarizing your dataKnow whether your data is real/nominal, seasonally adjusted/unadjustedCheck if means, std.dev., mins, and maxs of your data are plausibleClean your data of implausible values and obvious coding errorsWhen making data transformations (differencing, growth rates)

13、 make sure your data is correctly ordered and no wrong operations resultFor example, in a panel data set, be aware that the first observation of each cross-sectional unit has no predecessorCarrying out an Empirical ProjectInspecting, cleaning, and summEconometric AnalysisGiven your research question

14、 and the data available, you have to decide on the appropriate econometric methods to useSome general guidelinesOLS is still the most widely used method and often appropriateMake sure the key assumptions are satisfied in your modelAlways check for possible problems of omitted variables, self-selecti

15、on, measurement error, and simultaneityCarrying out an Empirical ProjectEconometric AnalysisCarrying oSome general guidelinesCarefully choose functional form specifications (logs, squares etc.)Beginners mistake: do not include variables that are listed as numerical values but have no quantitative me

16、aning (e.g., 3-digit occupations)Transform such variables to dummy variables representing categoriesHandle ordinal regressors in a similar way (e.g., job satisfaction)For ordinal dependent variables, there are ordered logit/probit modelsOne can also reduce ordered variables to binary variablesCarryi

17、ng out an Empirical ProjectSome general guidelinesCarryinSome general guidelinesThink of secondary complications such as heteroscedasticitySpecific problems in time series regressions: 1) levels vs. differences, 2) trends and seasonality, 3) unit roots and cointegrationCarry out misspecification tes

18、ts and think about possible biasesSensitivity analysis: look at variations of your specification/methodHopefully, results do not change in a substantial wayAre there problems with outliers/influential observations? Carrying out an Empirical ProjectSome general guidelinesCarryinSpecific aspects to th

19、ink of when using panel dataKey assumptionsRandom effects: regressors unrelated to individual specific effectsFixed effects: regressors related to individual specific effectsThe fixed effects assumption is often more convincingContemporaneous exogeneity: idiosyncratic errors are uncorrelated with th

20、e explanatory variables of the same time periodStrict exogeneity: idiosyncratic errors are uncorrelated with the explanatory variables of all time periods (often problematic)Carrying out an Empirical ProjectSpecific aspects to think of wSpecific aspects to think of when using panel dataMethods for p

21、anel dataPooled OLS: random effects assumption, serial correlation of error terms, needs only contemporaneous exogeneityRandom effects estimation: random effects assumption, more efficient than pooled OLS, needs strict exogeneityFixed effects estimation: fixed effects assumption, problem with time i

22、nvariant regressors, needs strict exogeneityFirst differencing: similar to fixed effects, good for longer time seriesCarrying out an Empirical ProjectSpecific aspects to think of wData mining/specification searchesThe process of looking for the best model is called specification searchOften, one sta

23、rts with a general model and drops insignificant variablesIf the specification search entails many steps, this is problematicOur assumptions actually require that the model is only estimated onceIf one sequentially estimates a number of models on the same data, the resulting test statistics and p-va

24、lues cannot be interpreted anymoreThis (difficult) problem is often ignored in practiceOne should keep the number of specification steps to a minimumCarrying out an Empirical ProjectData mining/specification searWriting an empirical paperA succesful empirical paper combines a careful, convincing dat

25、a analysis with good explanations and a clear expositionIntroductionState basic objectives and explain why the topic is importantLiterature review: What has been done? How do you add to this?Grab the readers attention by presenting simple statistics, paradoxical evidence, topical examples, or challe

26、nges to common wisdomOne may give a short summary of results in the introductionCarrying out an Empirical ProjectWriting an empirical paperCarrConceptual (or theoretical) frameworkDescription of general approach to answering your research questionYou may delevop/use a formal economic model for thisF

27、or example, setting up a utility maximization model of criminal activity clarifies the factors that matter for explaining criminal activity However, often common economic sense suffices to discuss the main mechanisms and control variables that have to be taken into accountAs one is in most cases int

28、erested in answering a causal question, a convincing discussion of what variables to control for is essentialCarrying out an Empirical ProjectConceptual (or theoretical) frEconometric models and estimation methodsSpecify the population model you have in mindExample: Effects of alcohol consumption on

29、 college GPAExample: Time series model of city-level car theftsExplain your functional form choicesCarrying out an Empirical ProjectEconometric models and estimatEconometric models and estimation methodsAfter specifying a population model, discuss estimation methodsDescribe how you measure the varia

30、bles in your population modelWhen using OLS: Discuss why exogeneity assumptions hold, and how you deal with heteroscedasticity, serial correlation and the likeWhen using IV/2SLS: Explain why your instrumental variables fulfill the assumptions: 1) exclusion, 2) exogeneity, 3) partial correlationWhen

31、using panel methods: Explain what the unobserved individual specific effects stand for, and how they are removed/accounted forCarrying out an Empirical ProjectEconometric models and estimatDataCarefully describe the data used in your empirical analysisName the sources of your data and how they can b

32、e obtainedTime series data and short data sets may be listed in the appendixIf your data is self-collected, include a copy of the questionnaireDiscuss the units of measurement of the variables of interestPresent summary statistics for the variables used in the analysis For trending variables, growth

33、 rates or graphs are more appropriateAlways state how many observations you use for different estimationsCarrying out an Empirical ProjectDataCarrying out ResultsPresent estimated equations, or, if there are too many, present tablesAlways include things like R-squared and the number of observationsA

34、re your estimated coefficients statistically significant?Are they economically significant? What is their magnitude?If coefficients do not have the expected signs, this may indicate there is a specification problem, for example, omitted variablesRelate differences between the results from different methods to the differences in the assumptions underlying these methods