Quantitative Methods for Economic Analysis

Transcription

1 Quantitative Methods for Economic Analysis Seyed Ali Madani Zadeh and Hosein Joshaghani Sharif University of Technology February / 20

2 Economic Analysis In general, research in economics has the following steps: 1. Start from real world, 2. Data mining, to come up with a questions, puzzle, regularity, or even irregularity, etc. 3. Create some models to explain facts and observations 4. more empirical analysis to test models, 5. expanding economic models to better explain important facts, anomalies, more empirical analysis expanding economic models... 2 / 20

3 Economic Analysis In general, research in economics has the following steps: 1. Start from real world, 2. Data mining, to come up with a questions, puzzle, regularity, or even irregularity, etc. 3. Create some models to explain facts and observations 4. more empirical analysis to test models, 5. expanding economic models to better explain important facts, anomalies, more empirical analysis expanding economic models... 2 / 20

4 Economic Analysis In general, research in economics has the following steps: 1. Start from real world, 2. Data mining, to come up with a questions, puzzle, regularity, or even irregularity, etc. 3. Create some models to explain facts and observations 4. more empirical analysis to test models, 5. expanding economic models to better explain important facts, anomalies, more empirical analysis expanding economic models... 2 / 20

5 Economic Analysis In general, research in economics has the following steps: 1. Start from real world, 2. Data mining, to come up with a questions, puzzle, regularity, or even irregularity, etc. 3. Create some models to explain facts and observations 4. more empirical analysis to test models, 5. expanding economic models to better explain important facts, anomalies, more empirical analysis expanding economic models... 2 / 20

6 Economic Analysis In general, research in economics has the following steps: 1. Start from real world, 2. Data mining, to come up with a questions, puzzle, regularity, or even irregularity, etc. 3. Create some models to explain facts and observations 4. more empirical analysis to test models, 5. expanding economic models to better explain important facts, anomalies, more empirical analysis expanding economic models... 2 / 20

7 Economic Analysis In general, research in economics has the following steps: 1. Start from real world, 2. Data mining, to come up with a questions, puzzle, regularity, or even irregularity, etc. 3. Create some models to explain facts and observations 4. more empirical analysis to test models, 5. expanding economic models to better explain important facts, anomalies, more empirical analysis expanding economic models... 2 / 20

8 Economic Analysis In general, research in economics has the following steps: 1. Start from real world, 2. Data mining, to come up with a questions, puzzle, regularity, or even irregularity, etc. 3. Create some models to explain facts and observations 4. more empirical analysis to test models, 5. expanding economic models to better explain important facts, anomalies, more empirical analysis expanding economic models... 2 / 20

9 Empirical Analysis Two types of empirical analysis: 1. Reduced form estimation: finding correlation between outcome variables identifying cause and effects very few structure on the data 2. Structural Estimation Imposing much more structure to the data Estimating parameters of the model Counterfactual analysis Complete economic research incorporates both reduced form and structural estimation. 3 / 20

10 Empirical Analysis Two types of empirical analysis: 1. Reduced form estimation: finding correlation between outcome variables identifying cause and effects very few structure on the data 2. Structural Estimation Imposing much more structure to the data Estimating parameters of the model Counterfactual analysis Complete economic research incorporates both reduced form and structural estimation. 3 / 20

11 Reduced form estimation Ultimate goal in reduced form estimation is to identify causal effects. But, it is usually very hard! Randomized Control Trials (RCT) is one solution. RCT is not always feasible: Monterey policy, banking,... Natural experiment is another solution Endogeneity is the main culprit. 4 / 20

12 Reduced form estimation: Tools Main identification tools are: 1. Regression 2. Instrumental Variables (IV) 3. Diff in Diff 4. Regression Discontinuty Design (RDD) 5. Panel Data and Fixed effects Very nice resourse for this topic: Angrist and Pischke (2014). Mastering metrics Angrist and Pischke (2008). Mostly harmless econometrics 5 / 20

13 Reduced form estimation: Tools Main identification tools are: 1. Regression 2. Instrumental Variables (IV) 3. Diff in Diff 4. Regression Discontinuty Design (RDD) 5. Panel Data and Fixed effects Very nice resourse for this topic: Angrist and Pischke (2014). Mastering metrics Angrist and Pischke (2008). Mostly harmless econometrics 5 / 20

14 Reduced form estimation This is NOT the main topic of this course! 6 / 20

15 Structural Estimation Start from theory, impose a sort of structure that you think will match the desired and important facts, Solve the model for equilibrium, steady state, balanced growth path or etc. Try to adjust parameters of the model to match the data. Use the estimated parameters to study counterfactual. 7 / 20

16 Structural Estimation Start from theory, impose a sort of structure that you think will match the desired and important facts, Solve the model for equilibrium, steady state, balanced growth path or etc. Try to adjust parameters of the model to match the data. Use the estimated parameters to study counterfactual. 7 / 20

17 Structural Estimation Start from theory, impose a sort of structure that you think will match the desired and important facts, Solve the model for equilibrium, steady state, balanced growth path or etc. Try to adjust parameters of the model to match the data. Use the estimated parameters to study counterfactual. 7 / 20

18 Structural Estimation Start from theory, impose a sort of structure that you think will match the desired and important facts, Solve the model for equilibrium, steady state, balanced growth path or etc. Try to adjust parameters of the model to match the data. Use the estimated parameters to study counterfactual. 7 / 20

19 Structural versus Reduced Form Estimation A statistical model describes the relation between two or more random variables. Example: Y = X β + ɛ An economic model starts with assumptions about agents preferences, constraints, firms production functions, some notion of equilibrium, etc.; then it makes predictions about the relation between observable, often endogenous variables. Structural estimation is an attempt to estimate an economic model s parameters and assess model fit. Parameters to estimate often include Preference parameters: risk aversion coefficient Technology parameters: production function?s curvature Other time-invariant institutional features: agents? bargaining power, financing frictions 8 / 20

20 Structural versus Reduced Form Estimation A statistical model describes the relation between two or more random variables. Example: Y = X β + ɛ An economic model starts with assumptions about agents preferences, constraints, firms production functions, some notion of equilibrium, etc.; then it makes predictions about the relation between observable, often endogenous variables. Structural estimation is an attempt to estimate an economic model s parameters and assess model fit. Parameters to estimate often include Preference parameters: risk aversion coefficient Technology parameters: production function?s curvature Other time-invariant institutional features: agents? bargaining power, financing frictions 8 / 20

21 Structural versus Reduced Form Estimation: Questions Reduced Form: What is the (causal) effect of X on Y? Structural Estimation: Typically assumes X causes Y What are the parameters magnitudes? How well does theory fit the data? How would the world look if one of the parameters (counterfactually) changed? 9 / 20

22 Structural versus Reduced Form Estimation: Tools Reduced Form: Estimators: OLS, IV, DiD, RD Design Software: Stata Structural Estimation: Estimators: GMM, SMM, ML, SML Software: Python, Julia, MATLAB, C++, Fortran, etc. 10 / 20

23 Structural versus Reduced Form Estimation: Benefits Stractural Estimation has at least three benefits: 1. Estimates of interesting economic primitives 2. Deep tests of theory: Testing quantitative, not just directional predictions Seeing where models fail opens doors to future research Example: Mehra and Prescott (1985), equity premium puzzle 3. Can answer interesting counterfactual questions 11 / 20

24 Structural Estimation and Lucas Critique Reduced-form papers can also ask counterfactual questions, by changing a regressors from its actual value to a counterfactual value. But less convincing, since harder to believe all else equal. I.e., Lucas critique is more severe for reduced-form counterfactuals. Also, impossible to shock primitives in reduced-form. 12 / 20

25 Disadvantages of Structural Estimation More assumptions Harder to do. (actually much harder!) Harder to convince general audience WARNING If structural and reduced-form will both get the job done, then go reduced-form!! 13 / 20

26 Disadvantages of Structural Estimation More assumptions Harder to do. (actually much harder!) Harder to convince general audience WARNING If structural and reduced-form will both get the job done, then go reduced-form!! 13 / 20

27 Road-map of the course Part 0: Introduction to Python Part 1: Introduction to Structural Estimation Part 2: Economic Models with Heterogeneous Agents 14 / 20

28 Part 0: Introduction to Python Why Python? Object oriented programming, It is fast It is multi purpose Growing very fast: economists, Google, Facebook, Microsoft... Need for speed? easily interact with Fortran, C++ and it is free! 15 / 20

29 life is short, use Python / 20

30 Part 1: Structural Estimation Logit/Probit why do we need simulation? Closed form estimation: Logit The Need for Simulation: Probit Simulation 1. Monte Carlo Simulation 2. Monte Carlo Integration 3. Markov Chain Monte Carlo method (MCMC) 4. Numerical Maximization Mixed Logit Maximum Likelihood (ML) General Method of Moments (GMM) Simulated Method of Moments (SMM) 17 / 20

31 Part 2 - Economic Models with Heterogeneous Agents This part is the main body of the course! 1. Single Agent Problem Dynamic Programming 2. Competitive Equilibrium Stationary Distribution 3. Estimation Simulated Method of Moments 18 / 20

32 Part 2 - Economic Models with Heterogeneous Agents This part is the main body of the course! 1. Single Agent Problem Dynamic Programming 2. Competitive Equilibrium Stationary Distribution 3. Estimation Simulated Method of Moments 18 / 20

33 Part 2 - Economic Models with Heterogeneous Agents This part is the main body of the course! 1. Single Agent Problem Dynamic Programming 2. Competitive Equilibrium Stationary Distribution 3. Estimation Simulated Method of Moments 18 / 20

34 Part 2 - Economic Models with Heterogeneous Agents This part is the main body of the course! 1. Single Agent Problem Dynamic Programming 2. Competitive Equilibrium Stationary Distribution 3. Estimation Simulated Method of Moments 18 / 20

35 Part 2 - Economic Models with Heterogeneous Agents This part is the main body of the course! 1. Single Agent Problem Dynamic Programming 2. Competitive Equilibrium Stationary Distribution 3. Estimation Simulated Method of Moments 18 / 20

36 Part 2 - Economic Models with Heterogeneous Agents This part is the main body of the course! 1. Single Agent Problem Dynamic Programming 2. Competitive Equilibrium Stationary Distribution 3. Estimation Simulated Method of Moments 18 / 20

37 Part 2 - Economic Models with Heterogeneous Agents This part is the main body of the course! 1. Single Agent Problem Dynamic Programming 2. Competitive Equilibrium Stationary Distribution 3. Estimation Simulated Method of Moments 18 / 20

38 Part 2 - Economic Models with Heterogeneous Agents This part is the main body of the course! 1. Single Agent Problem Dynamic Programming 2. Competitive Equilibrium Stationary Distribution 3. Estimation Simulated Method of Moments 18 / 20

39 Evaluation Homework 35% Presentation 5% Project 20% Midterm 20% Final 20% 19 / 20

40 Project teamwork (but no free ride!) two different types for projects 1. replicating one of the papers related to the course and try to use Iran s data. 2. pursue your own research, but use the methods we discussed in class for answering your own questions. 20 / 20