- Page 2 and 3: Econometric Analysis of Cross Secti
- Page 4 and 5: vi Contents II LINEAR MODELS 47 4 T
- Page 6 and 7: viii Contents 7.8 The Linear Panel
- Page 10 and 11: xii Contents 13.9 Panel Data Models
- Page 12 and 13: xiv Contents 16.8.3 Dynamic Unobser
- Page 14 and 15: xvi Contents 20 Duration Analysis 6
- Page 16 and 17: Preface This book is intended prima
- Page 18 and 19: Preface xix siderably with methods
- Page 20 and 21: Preface xxi On an organizational no
- Page 22 and 23: 1 Introduction 1.1 Causal Relations
- Page 24 and 25: Introduction 5 interpreting assumpt
- Page 26 and 27: Introduction 7 section dimension. T
- Page 28 and 29: Introduction 9 Example 1.2 (E¤ect
- Page 30 and 31: Introduction 11 does it depend on x
- Page 32 and 33: 14 Chapter 2 conditional expectatio
- Page 34 and 35: 16 Chapter 2 Example 2.1 (continued
- Page 36 and 37: 18 Chapter 2 q log½Eðy j xÞŠ 10
- Page 38 and 39: 20 Chapter 2 For many purposes we n
- Page 40 and 41: 22 Chapter 2 This equation shows th
- Page 42 and 43: 24 Chapter 2 because we assume that
- Page 44 and 45: 26 Chapter 2 The linear projection
- Page 46 and 47: 28 Chapter 2 c. Suppose that x has
- Page 48 and 49: 30 Chapter 2 property CE.4: If fðx
- Page 50 and 51: 32 Chapter 2 2.A.3 Properties of Li
- Page 52 and 53: 34 Chapter 2 Define the 1 K vector
- Page 54 and 55: 36 Chapter 3 3.2 Convergence in Pro
- Page 56 and 57: 38 Chapter 3 (1) Z 1 N exists w.p.a
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40 Chapter 3 theorem 3.2 (Lindeberg
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42 Chapter 3 consistency certainly
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44 Chapter 3 For testing the null h
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46 Chapter 3 pffiffiffiffi p b. ffi
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4 The Single-Equation Linear Model
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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The Single-Equation Linear Model an
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84 Chapter 5 coe‰cient on z 1 is
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86 Chapter 5 collinearity in z in t
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88 Chapter 5 two di¤erent, often c
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90 Chapter 5 5.1.2 Multiple Instrum
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92 Chapter 5 What is the analogue o
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94 Chapter 5 equation (5.7) by x 0
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96 Chapter 5 Example 5.3 (Parents
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98 Chapter 5 aware that the normal
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100 Chapter 5 typical case. Under H
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102 Chapter 5 where Corrð ; Þ den
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104 Chapter 5 large. This is anothe
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106 Chapter 5 This assumption conta
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108 Chapter 5 y 1 ¼ z 1 d 1 þ a 1
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110 Chapter 5 5.5. One occasionally
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112 Chapter 5 pffiffiffiffi a. Unde
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6 Additional Single-Equation Topics
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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Additional Single-Equation Topics 1
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144 Chapter 7 y 1 ¼ x 1 b 1 þ u 1
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146 Chapter 7 section unit. Here we
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148 Chapter 7 Note that the dimensi
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150 Chapter 7 X N i¼1 0 xi1 0 x i1
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152 Chapter 7 The asymptotic varian
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154 Chapter 7 assumption SGLS.1: E
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156 Chapter 7 where we have also us
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158 Chapter 7 where ^u^u i 1 y i X
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160 Chapter 7 But if N is small rel
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162 Chapter 7 We obtain an estimato
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164 Chapter 7 e‰cient as system O
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166 Chapter 7 Table 7.1 An Estimate
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168 Chapter 7 Because of regional v
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170 Chapter 7 7.8.1 Assumptions for
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172 Chapter 7 y it on x it ; t ¼ 1
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174 Chapter 7 The variable cumgpa i
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176 Chapter 7 unconditional). The r
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178 Chapter 7 The test statistic is
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180 Chapter 7 pffiffiffiffi 7.4. Us
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182 Chapter 7 b. Add a one-year lag
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184 Chapter 8 A wage o¤er function
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186 Chapter 8 that is, by using a s
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188 Chapter 8 which has dimension G
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190 Chapter 8 " X # " # N 0 min Zi
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192 Chapter 8 When we plug equation
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194 Chapter 8 where ^u i 1 y i X i
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196 Chapter 8 theorem 8.4 (Optimali
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198 Chapter 8 exogenous in all equa
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200 Chapter 8 Let ^b again be the G
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202 Chapter 8 to the statistical si
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204 Chapter 8 Under homoskedasticit
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206 Chapter 8 a. Suppose that Eðx
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208 Chapter 8 e¤ectively the null
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210 Chapter 9 absence of omitted va
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212 Chapter 9 across di¤erent equa
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214 Chapter 9 The condition for ide
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216 Chapter 9 where u 1 ðu 1 ; ...
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218 Chapter 9 Given the linear rest
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220 Chapter 9 R 1 ¼ 0 0 0 0 1 0 0
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222 Chapter 9 When estimating a sim
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224 Chapter 9 It would be more e‰
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226 Chapter 9 where each z j is unc
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228 Chapter 9 where the notation u
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230 Chapter 9 system. Technically,
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232 Chapter 9 Before considering eq
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234 Chapter 9 2. Apply the rank con
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236 Chapter 9 where we have dropped
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238 Chapter 9 Equation (9.28) is st
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240 Chapter 9 e. For a family, y 1
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242 Chapter 9 d. Argue that, under
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244 Chapter 9 9.11. Consider a two-
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10 Basic Linear Unobserved E¤ects
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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Basic Linear Unobserved E¤ects Pan
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300 Chapter 11 Given model (11.1),
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302 Chapter 11 Cauchy-Schwartz ineq
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304 Chapter 11 Even with large T, t
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306 Chapter 11 w is only for s a t;
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308 Chapter 11 but where we allow w
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310 Chapter 11 where y t denotes di
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312 Chapter 11 plim N!y ^b POLS ¼
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314 Chapter 11 Assumption (11.37) o
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316 Chapter 11 One approach to esti
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318 Chapter 11 y it ¼ z it a i þ
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320 Chapter 11 Under Assumption FE.
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322 Chapter 11 The estimator ^a in
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324 Chapter 11 place the unobserved
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326 Chapter 11 Eðu it j z i ; x i1
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328 Chapter 11 ð€x it ; z i1 ; x
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330 Chapter 11 One complication tha
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332 Chapter 11 Problems 11.1. Let y
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334 Chapter 11 b. If you apply the
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336 Chapter 11 Specifically, di¤er
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338 Chapter 11 Eðc i j w i ; z i ;
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340 Part III The analysis of maximu
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342 Chapter 12 y o1 ¼ 4 and y o2
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344 Chapter 12 where u is defined i
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346 Chapter 12 would typically hold
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348 Chapter 12 one solution; identi
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350 Chapter 12 The notation H € i
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352 Chapter 12 where ‘ y denotes
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354 Chapter 12 condition, we need t
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356 Chapter 12 bias are two-step M-
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358 Chapter 12 equation (12.4). Und
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360 Chapter 12 or ^V ¼ ^s 2 X N i
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362 Chapter 12 ^D 1 N 1 XN ^g i^g i
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364 Chapter 12 restrictions define
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366 Chapter 12 a P Q matrix E with
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368 Chapter 12 probably correlated
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370 Chapter 12 where H € i is the
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372 Chapter 12 Consider the Wald st
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374 Chapter 12 in many cases they a
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376 Chapter 12 The term multiplying
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378 Chapter 12 We can also see how
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380 Chapter 12 where the error u i
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382 Chapter 12 a. If you apply pool
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384 Chapter 12 pffiffiffiffi pffiff
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386 Chapter 13 density, we would ha
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388 Chapter 13 We can easily obtain
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390 Chapter 13 We can apply inequal
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392 Chapter 13 relaxed, but doing s
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394 Chapter 13 Example 13.2 (contin
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396 Chapter 13 Thus, Avârð^yÞ ca
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398 Chapter 13 evaluate this vector
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400 Chapter 13 or E y ½‘ y g i
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402 Chapter 13 slight notational ch
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404 Chapter 13 tained, often implic
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406 Chapter 13 This situation is en
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408 Chapter 13 discuss a su‰cient
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410 Chapter 13 be correlated within
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412 Chapter 13 Now the problem beco
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414 Chapter 13 focus on the former
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416 Chapter 13 a. What is the joint
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418 Chapter 13 c. Under the same as
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420 Chapter 13 property CD.3: For r
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422 Chapter 14 " # 0 " # Q N ðyÞ
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424 Chapter 14 As in the linear cas
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426 Chapter 14 additional moment co
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428 Chapter 14 3SLS estimator in li
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430 Chapter 14 " X # ! N 0 1 " # mi
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432 Chapter 14 where w ¼ðw 1 ; ..
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434 Chapter 14 along with equation
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436 Chapter 14 models with unobserv
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438 Chapter 14 for some t A T and
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440 Chapter 14 where x i A R K is a
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442 Chapter 14 portant example wher
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444 Chapter 14 The proof that ^X 1
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446 Chapter 14 regressors appear in
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448 Chapter 14 the CMD estimator ba
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IV NONLINEAR MODELS AND RELATED TOP
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454 Chapter 15 and that in basic st
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456 Chapter 15 inclusive (kidsge6);
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458 Chapter 15 There is no particul
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460 Chapter 15 15.4 Maximum Likelih
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462 Chapter 15 where x is 1 K and
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464 Chapter 15 Pðy ¼ 1 j xÞ ¼P
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466 Chapter 15 Often we want to est
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468 Chapter 15 Table 15.1 LPM, Logi
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470 Chapter 15 15.7 Specification I
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472 Chapter 15 when it is independe
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474 Chapter 15 distributed with Eð
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476 Chapter 15 conditional on z, re
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478 Chapter 15 Pðy 1 ¼ 1 j y 2 ¼
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480 Chapter 15 Relaxing distributio
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482 Chapter 15 tains some recent re
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484 Chapter 15 second equality is t
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486 Chapter 15 approximating the in
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488 Chapter 15 As in the linear cas
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490 Chapter 15 lagged dependent var
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492 Chapter 15 (1997), the MLE of b
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494 Chapter 15 c i as parameters to
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496 Chapter 15 model (15.66) are id
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498 Chapter 15 (15.77) shows that e
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500 Chapter 15 15.9.2 Probabilistic
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502 Chapter 15 tives. A well-known
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504 Chapter 15 conditions, but the
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506 Chapter 15 itself, is of limite
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508 Chapter 15 hood estimation is p
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510 Chapter 15 b. What happens if y
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512 Chapter 15 15.7. Use the data i
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514 Chapter 15 correlation within f
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516 Chapter 15 d. Now estimate a dy
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518 Chapter 16 Data censoring also
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520 Chapter 16 The wealth example c
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522 Chapter 16 where fðÞ is the s
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524 Chapter 16 probability of obser
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526 Chapter 16 (As in recent chapte
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528 Chapter 16 Table 16.1 OLS and T
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530 Chapter 16 where q is an unobse
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532 Chapter 16 equation, and so the
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534 Chapter 16 the latent variable
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536 Chapter 16 Powell’s method al
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538 Chapter 16 f ðy j x; y > 0Þ
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540 Chapter 16 does not a¤ect the
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542 Chapter 16 Normalð0; sa 2Þ di
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544 Chapter 16 assumptions are made
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546 Chapter 16 a. Using only the da
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548 Chapter 16 and regular apples.
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17 Sample Selection, Attrition, and
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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Sample Selection, Attrition, and St
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604 Chapter 18 ‘‘treatment’
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606 Chapter 18 ATE is simple. Using
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608 Chapter 18 17.7.3 in the contex
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610 Chapter 18 Nevertheless, we wil
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612 Chapter 18 Therefore, by iterat
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614 Chapter 18 Obtaining a standard
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616 Chapter 18 E½wðy 1 y 0 ÞŠ
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618 Chapter 18 pffiffiffiffi w from
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620 Chapter 18 Example 18.2, we hav
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622 Chapter 18 Under parts a and b
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624 Chapter 18 model to be correct.
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626 Chapter 18 where a is the ATE a
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628 Chapter 18 Even if Assumption A
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630 Chapter 18 The term ^f i 1 fð^
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632 Chapter 18 along with the assum
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634 Chapter 18 statuses we would ob
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636 Chapter 18 where the second-to-
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638 Chapter 18 If the treatment is
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640 Chapter 18 functions of x and V
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642 Chapter 18 Heckman and Vytlacil
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644 Chapter 18 b. If w given ðx; z
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646 Chapter 19 assumption in count
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648 Chapter 19 where x is 1 K and
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650 Chapter 19 It is easily seen th
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652 Chapter 19 Table 19.1 OLS and P
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654 Chapter 19 the restricted estim
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656 Chapter 19 (1991b) that p remov
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658 Chapter 19 implies overdispersi
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660 Chapter 19 As in the case of un
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662 Chapter 19 Given explanatory va
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664 Chapter 19 and that e 1 is unco
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666 Chapter 19 If we assume that c
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668 Chapter 19 in the selected samp
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670 Chapter 19 ^B ¼ N 1 XN s i ð
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672 Chapter 19 exogeneity assumptio
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674 Chapter 19 the pooled Poisson e
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676 Chapter 19 s i ðbÞ 1 ‘ b l
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678 Chapter 19 e¤ects Poisson soft
- Page 688 and 689:
680 Chapter 19 b. If Varðy i j x i
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682 Chapter 19 19.9. Use the data i
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20 Duration Analysis 20.1 Introduct
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Duration Analysis 687 The survivor
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Duration Analysis 689 which is the
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Duration Analysis 691 Typically, k
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Duration Analysis 693 The definitio
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Duration Analysis 695 unemployed du
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Duration Analysis 697 Because the h
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Duration Analysis 699 log of the du
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Duration Analysis 701 Under the ass
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Duration Analysis 703 x i and has a
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Duration Analysis 705 GðÞ is the
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Duration Analysis 707 with covariat
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Duration Analysis 709 at most depen
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Duration Analysis 711 survivor func
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Duration Analysis 713 m ¼ 1; ...;
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Duration Analysis 715 unemployment
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Duration Analysis 717 20.5. In this
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Duration Analysis 719 respectively,
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722 References Ashenfelter, O., and
- Page 730 and 731:
724 References Cox, D. R. (1972),
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726 References Gurmu, S., and P. K.
- Page 734 and 735:
728 References Horowitz, J. L. (199
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730 References Maloney, M. T., and
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732 References Phillips, P. C. B.,
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734 References Vella, F., and M. Ve