Bài giảng Phân tích số liệu mảng - Chương 5: Dynamic panel model

Bài giảng Phân tích số liệu mảng - Chương 5: Dynamic panel model. Chương này cung cấp cho sinh viên những nội dung gồm: giới thiệu về Dynamic panel model; ước tính hiệu ứng cố định và ngẫu nhiên; ước tính biến công cụ (phương pháp IV) (Anderson và Hsiao, 1982); 2SLS, phương pháp tiếp cận mô men tổng quát (GMM) (Arenalloand Bond, 1985);... Mời các bạn cùng tham khảo!
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Bài giảng Phân tích số liệu mảng - Chương 5: Dynamic panel model 6/6/2022 Chapter 5 Dynamic Panel Model Mr U_KHOA TOÁN KINH TẾ 1 Objectives 2 (1) Introduce about Dynamic Panel Model (2) Fixed and Random Effects Estimation (3) Instrumental Variable Estimation (IV approach) (Anderson and Hsiao, 1982) (4) 2SLS, Generalized Method of Moment (GMM) approach (Arenallo and Bond, 1985) Mr U_KHOA TOÁN KINH TẾ 6/6/2022 5.1 Introduction 3 Linear dynamic panel data models include lag dependent variables as covariates along with the unobserved effects, fixed or random, and exogenous regressor p p yit   0    j y t  j  x it   i  u it    j y t  j  x it  *  u it i (5.1) j1 j1 Notes: The presence of lagged dependent variable as a regressor incorporates the entire history of it, and any impact of xit on yit is conditioned on this history. We consider a dynamic panel model, in the sense that it contains (at least) one lagged variables. For simplicity, let us consider Mr U_KHOA TOÁN KINH TẾ yit = γ1yit-1+β’itxit +αi* + uit (5.2) 6/6/2022 yit = γ1yit-1+β’itxit +αi* + uit (5.2) 4 Eq. (5.2) requires that |γ | By setting t = 1, 2,… and so on, the autoregressive process can be 5 expressed in the following way: yi ( t 1)   0   yi 0   i  ui 0 yi 2   0   yi1   i  ui 2   0   i   1   0   1 yi 0   i  ui 0   ui 2   0   0 1   i   i 1   12 yi 0   1ui1  ui 2 ............. t 1 yit   0 1   1  ...   t 1 1    1   i 1  ...   t 1 1  t 1 yi 0    1j ui ,t  j j 0 Or t 1 t 1 t 1 yit   0     i     yi 0    1j ui ,t  j 1 j 1 j t 1 j 0 j 0 j 0 Therefore t 2 t 2 t 2 yit 1   0     i     1 j 1 j t 1 1 y    1j ui ,t 1 j i0 j 0 Mr U_KHOA TOÁN KINH TẾ j 0 j 0 6/6/2022 For l arg e t , 1 1 6 E  yit /  i    0  i 1 1 1 1  2 V  yit /  i   1   12 5.2 Fixed and Random Effects Estimation yit = γ0 + γ1yit-1+ αi + uit (5.3) Remark: One possible cause for biasedness is the presence of the unknown individual effects αi, which creates a correlation between the explanatory variables and the residuals y it    y i   1 yit 1  y i ,1  uit  u i  Notes: yit 1  y i ,1  will be correlated  u it  ui  Mr U_KHOA TOÁN KINH TẾ 6/6/2022     yit  y i   1  yit 1   y i ,1    uit   ui  7  depen on past value of uit  depen on past value of uit The within estimator or fix effects estimator is   y  y  N T it  yi it 1  y i ,1   1FE  i 1 t 1   y  N T 2 it 1  y i , 1 i 1 t 1   y ...