Carrying out an Empirical Project Empirical Analysis & Style Hint JDS Special program: Pre-training 1 Carrying out an Empirical Project 1. 2. 3. 4. 5. Posing a Question Literature Review Data Collection

Econometric Analysis Writing an Empirical Paper 2 Steps in Empirical Analysis Causality & Ceteris Paribus JDS Special program: Pre-training 2 1 Posing a Question Start with a general area or set of questions. Make sure you are interested in the topic.

Use on-line services such as Google scholar to investigate past work on this topic. Narrow down your topic to a specific question or issue to be investigated. Work through the theoretical issue. You cannot be too ambitious for your master thesis. JDS Special program: Pre-training 3 2 Literature Review All papers, even if they are relatively short,

should contain a review of relevant literature. On-line services are useful for lit-review. You can read abstracts of papers to see how relevant they are to your own work. Think of related topics that might not show up in a search using a handful of key words. JDS Special program: Pre-training 4

3 Data Collection Deciding on which kind of data to collect depends on the nature of the analysis. Investigate what type of data sets have been used in the past literature. The most important is whether there are enough controls to do a reasonable ceteris paribus analysis. Consider collecting your own data. JDS Special program: Pre-training 5

Inspecting Data, etc. You must know the nature of the variables in the data set. Measurement units, rates, etc. Check the data for missing values, errors, outliers, etc. Drawing graph, finding descriptive stats,etc. Create variables appropriate for analysis.

For example, create dummy variables from categorical variables, create hourly wages, etc. JDS Special program: Pre-training 6 4 Econometric Analysis After deciding on a topic and collecting an appropriate data, decide on the appropriate econometric methods. If you want to use OLS, OLS assumptions must be satisfied for your model. The error term must be uncorrelated with x. Make functional form decisions.

Log, interactions, dummy, etc. JDS Special program: Pre-training 7 Estimating a Model Start with a model that is clearly based in theory. Test for significance of other variables that are theoretically less clear. Test for functional form misspecification.

Consider reasonable interactions, quadratics, logs, etc. JDS Special program: Pre-training 8 Cont. Estimating a Model Dont lose sight of theory and the ceteris paribus interpretation you need to be careful about including variables that greatly alter the interpretation.

For example, effect of bedrooms conditional on square footage. Be careful about putting functions of y on the right hand side affects interpretation. JDS Special program: Pre-training 9 Cont. Estimating a Model Once you have a well-specified model, need to worry about the standard errors.

Test for heteroskedasticity. Test for serial correlation if there is a time component. Correct if necessary. JDS Special program: Pre-training 10 Other Problems Often you have to worry about endogeneity of the key explanatory variable. Endogeneity could arise

from omitted variables that are not observed in the data. because the model is really part of a simultaneous equation. due to measurement error. JDS Special program: Pre-training 11 Cont. Other Problems

If you have panel data, you can consider a fixed effects model (or first differences). Problem with FE is that you need good variation over time. You can instead try to find a perfect instrument and perform 2SLS. Problem with IV is finding a good instrument JDS Special program: Pre-training 12

Interpreting Your Results Keep theory in mind when interpreting results. Be careful to keep ceteris paribus in mind. Keep in mind potential problems with your estimates be cautious drawing conclusions. You can get an idea of the direction of bias due to omitted variables, measurement error or simultaneity. JDS Special program: Pre-training 13 Further Issues Some problems are just too hard to easily

solve with available data. May be able to approach the problem in several ways, but something wrong with each one. Provide enough information for a reader to decide whether they find your results convincing or not. JDS Special program: Pre-training 14 Cont. Further Issues Dont worry if you dont prove your theory.

With unexpected results, you have to be careful in thinking through potential biases. But, if you have carefully specified your model and feel confident you have unbiased estimates, then thats just the way things are. JDS Special program: Pre-training 15 5 Writing an Empirical Paper 1. Introduction 2. Conceptual (or Theoretical) 3. 4. 5. 6.

Framework Econometric models & Estimation methods The data Results Conclusion JDS Special program: Pre-training 16 A1: 2 Steps in Empirical Analysis An empirical analysis uses data to test a theory or to estimate a relationship 1. Constructing economic model wage = f (educ, exper, training) (1.2)

2. Specifying econometric model wage = b0 + b1educ + b2exper + b3training + u (1.4) u is error term, and b s are parameters. JDS Special program: Pre-training 17 A2: Causality & Ceteris Paribus Economists goal is to infer that one variable has a causal effect on another variable, for testing economic theory or for evaluating policy.

Causal effect: A ceteris paribus change in one variable has an effect on another variable. Ceteris paribus: All other relevant factors are held fixed. JDS Special program: Pre-training 18 Example: Returns to Education A model of human capital investment implies getting more education should lead to higher earnings. In the simplest case, this implies an equation like wage = b0 + b1educ + b2exper + b3age + u .

JDS Special program: Pre-training 19 Example cont. The error term, u, includes other factors affecting earnings, like gender difference or job training. The estimate of b1 is the return to education. wage b1 educ s.t. exper age u 0 JDS Special program: Pre-training

20 Causality & Ceteris Paribus cont. Simply establishing a relationship between variables is rarely sufficient. If weve truly controlled for enough other variables, then the estimated ceteris paribus effect can often be considered to be causal. Econometric methods can simulate a ceteris paribus experiment. JDS Special program: Pre-training 21

Recently Viewed Presentations

  • HEALTH AND FITNESS - St Roch's Secondary School

    HEALTH AND FITNESS - St Roch's Secondary School

    HEALTH AND FITNESS HEALTH Health and fitness are important in every day living, and being healthy means more than not being ill or sick. There are many parts of a persons life which contributes to their good health Aspects of...
  • Protists - Mrs. Allen's WebSite, Maury High School, NPS

    Protists - Mrs. Allen's WebSite, Maury High School, NPS

    Great Diversity dinoflagellates & ciliates euglenoids brown algae & diatoms red algae green algae miscellaneous? Problems with Protist Classification Euglenozoa Animals Streptophyta (includes land plants) Choanoflagellida Fungi Chlorophyta Rhodophyta Stramenopila Alveolata Archaea Bacteria Something's not right here!
  • Welcome to Year 1 - The Oaks CE Learning Federation

    Welcome to Year 1 - The Oaks CE Learning Federation

    How do we plan our projects? Our curriculum is delivered through projects which start with a hook to motivate and engage the children. They then learn the skills, knowledge and understanding they need to work towards a real outcome/purpose, for...


    Analisa HAZID (Hazard Identification) Proses pengidentifikasian terhadap bahaya yang mungkin terjadi secara umum pada fasilitas operasi sebuah pabrik/ industri. Analisa HAZOP Identifikasi keselamatan, bahaya & masalah operasi yang berhubungan dengan proses yang secara langsung mengancam keselamatan pekerja produksi/penyebab masalah operasi.
  • Battle of Wounded Knee -

    Battle of Wounded Knee -

    Battle of Wounded Knee ... Muscle Shoals and the Tennessee Valley Authority. ... With the slogan" the only thing we have to fear itself," encouraged new hope for emerging from the Great Depression . At age 39, he contracted poliomyelitis,...
  • Morphology and Finite-state Transducers Part 2

    Morphology and Finite-state Transducers Part 2

    Morphology and Finite-state Transducers Part 2 ICS 482: Natural Language Processing Lecture 6 Husni Al-Muhtaseb * */ * */ Porter Example Computerization ization -> -ize computerize ize -> ε computer Other Rules ing -> ε (motoring -> motor) ational ->...
  • Baseband Demodulation - The Computer Engineers' Blog

    Baseband Demodulation - The Computer Engineers' Blog

    Procedure is complete * Final Step: * Signal Constellation Diagram Bandpass Signals Representation * Representation of Bandpass Signals Bandpass signals (signals with small bandwidth compared to carrier frequency) can be represented in any of three standard formats: 1.
  • MISSION STATEMENT Serve as a model program both

    MISSION STATEMENT Serve as a model program both

    MISSION STATEMENT Serve as a model program both academically and competitively for NCAA Division I Develop a national reputation of excellence and the ability to attract/retain quality staff and student-athletes Provide an athletics program that promotes and protects the comprehensive...