Contents I

Foundations

10

1 Entering the ‘Matrix Laboratory’ 1.1 Introduction . . . . . . . . . . . . . . 1.2 Our “Hello World”: OLS in Matlab 1.3 The beauty of functions . . . . . . . 1.4 A simple utility function . . . . . . . 1.5 Review and exercises . . . . . . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

11 12 15 19 25 32

2 Optimisation I: The Agent Optimises 2.1 Profit maximisation and linear programming . . 2.2 Utility maximisation and non-linear optimisation 2.3 Simulating heterogeneity . . . . . . . . . . . . . . 2.4 Review and exercises . . . . . . . . . . . . . . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

33 34 40 48 52

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

3 Optimisation II: The Economist Optimises 53 3.1 Maximising likelihoods . . . . . . . . . . . . . . . . . . . . . . . . 54 3.2 Generalised Method of Moments . . . . . . . . . . . . . . . . . . 59 3.3 Review and exercises . . . . . . . . . . . . . . . . . . . . . . . . . 64

II

Discrete Choice

66

4 Discrete Multinomial Choice 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 The binary logit model . . . . . . . . . . . . . . . . . . . . . 4.2.1 The model . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Simulating the model . . . . . . . . . . . . . . . . . 4.2.3 Estimating the model . . . . . . . . . . . . . . . . . 4.2.4 Estimating by simulation . . . . . . . . . . . . . . . 4.3 The multinomial logit model . . . . . . . . . . . . . . . . . 4.3.1 The model . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Simulating, estimating and estimating by simulation 4.4 Multinomial Probit . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Independence assumptions in the Multinomial Logit

1

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

67 68 69 69 70 72 74 78 78 81 87 87

4.4.2 Simulating the model . . . . . . . . . . . . 4.4.3 Estimating by simulation . . . . . . . . . . 4.4.4 Estimating by simulation: A logit-smoothed 4.5 Review and exercises . . . . . . . . . . . . . . . . . 4.A Deriving the Multinomial Logit log-likelihood . . . 5 Discrete Games 5.1 Introduction: A simple Cournot game . . . . . . 5.1.1 Finding the best-response function . . . . 5.2 Estimating a discrete Bayesian game . . . . . . . 5.2.1 A simple discrete game . . . . . . . . . . 5.2.2 Implementing the best-response functions 5.2.3 Solving the game numerically . . . . . . . 5.2.4 Simulating the game . . . . . . . . . . . . 5.2.5 Estimating . . . . . . . . . . . . . . . . . 5.3 Review and exercises . . . . . . . . . . . . . . . .

III

. . . . . . . . .

. . . . . . . . . . . . . . . . AR simulator . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

Dynamics

89 90 92 97 98 100 100 102 109 109 113 115 116 119 124

125

6 Dynamic Choice on a Finite Horizon 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Dynamic decisions . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Household consumption . . . . . . . . . . . . . . . . . . . 6.2.2 A small firm . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Introducing the value function . . . . . . . . . . . . . . . . . . . . 6.3.1 Dynamic programming . . . . . . . . . . . . . . . . . . . . 6.3.2 Computational accuracy, curse of dimensionality and memoization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Shocks, uncertainty, and microeconometrics . . . . . . . . . . . . 6.5 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

126 126 127 127 134 138 139

7 Dynamic Choice on an Infinite Horizon 7.1 Value functions and infinite solutions . . . . . 7.1.1 A rough outline . . . . . . . . . . . . . 7.1.2 Computation . . . . . . . . . . . . . . 7.2 Policy iterations and faster solutions . . . . . 7.3 Solving for structural parameters using GMM 7.3.1 Applying GMM to our dynamic model 7.3.2 Final thoughts on estimation . . . . . 7.4 Review . . . . . . . . . . . . . . . . . . . . . . 7.A Analytically iterating the value function . . .

154 156 156 158 165 169 171 175 176 176

2

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

145 147 153

IV

Non-Parametric Methods

181

8 Kernel Regression 8.1 Parametric v. nonparametric approaches 8.2 Univariate kernel regression . . . . . . . 8.3 Structures and cell arrays . . . . . . . . 8.3.1 Cell arrays . . . . . . . . . . . . 8.3.2 Structure arrays . . . . . . . . . 8.4 Impact of kernel and bandwidth choice . 8.5 Cross validation . . . . . . . . . . . . . . 8.6 Review and exercises . . . . . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

182 183 187 191 192 194 197 204 210

9 Semiparametric Methods 9.1 Multivariate kernels . . . . . . . . 9.2 The curse of dimensionality . . . . 9.3 Approaches to dimension reduction 9.4 Partially linear model . . . . . . . 9.4.1 Robinson’s approach . . . . 9.4.2 Implementation . . . . . . . 9.5 Single index model . . . . . . . . . 9.5.1 Ichimura’s estimator . . . . 9.5.2 Implementation . . . . . . . 9.6 Review and exercises . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

211 212 215 216 217 218 219 221 223 224 229

V

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

Speed

10 Speeding Things Up. . . 10.1 Introduction . . . . . . . 10.2 Clever coding . . . . . . 10.2.1 Vectorising . . . 10.2.2 Sparse matrices . 10.2.3 Profiling code . . 10.2.4 Waiting. . . . . . 10.3 Parallel computing . . . 10.4 Parallel computing plus: 10.5 Other tricks . . . . . . .

230 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . the GPU . . . . . .

11 . . . and Slowing Things Down

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

231 231 232 232 234 237 239 241 245 249 251

3

Contents

90. 4.4.4 Estimating by simulation: A logit-smoothed AR simulator 92. 4.5 Review and exercises . . . . . . . . . . . . . . . . . . . . . . . . . 97. 4.A Deriving the Multinomial Logit log-likelihood . . . . . . . . . . . 98. 5 Discrete Games. 100. 5.1 Introduction: A simple Cournot game . . . . . . . . . . . . . . . 100. 5.1.1 Finding the best-response function .
Download PDF
97KB Sizes 0 Downloads 93 Views
Report

Recommend Documents

Contents - multiresolutions.com
Notes on the History of Data Analysis . . . . . . . . . . . . . 3. 1.2.1. Biometry . . . . . . . . . . . . . . . . . . . . . . . . . 4. 1.2.2. Era Piscatoria . . . . . . . . . . . . . . . . . . . . . . . 4. 1.2.3. Psychometrics . . . . . . . . . . .
Contents - GitHub
May 9, 2006 - 3. 2 Requirements from SAGA Use Cases. 3. 2.1 Use Cases in Detail . ... A useful though informal way of understanding SAGA's aim and scope is to think of the MPI .... the design and implementation of specific API calls and maybe even th
Contents - GitHub
Dec 12, 2015 - 5.2.2 TIR-FCS with a square-shaped lateral detection volume . . . . . . . 30. 6 Troubleshooting. 32 ... 3http://www.gnu.org/licenses/gpl.html. 3 ..... This entry displays this documentation using the systems default PDF viewer. Wiki.
Contents - GitHub
Jan 7, 2015 - Foundation, either version 2 of the License, or (at your option) any later version. An exemplary usage of ... cython.org/src/tutorial/appendix.html.
Contents
Contents. Java. Chapter 4: Object-Oriented Analysis and Design ..... while I've been holed up in my office working on this book. I know they'd like to have ... than slapping down a few lines of code in Java (or C++, Eiffel, or any other object-orient
contents
Jun 2, 2008 - meeting. Fred Sewall made a motion to approve the minutes as ... changes down the road, the waiver should not necessarily go with it. Parker.
Contents -
A study on the use of online public access catalogue at the library of M.G.M College. DEVENDRA .... Device, Talking Typing Teacher Software, Braille Scanning Software, SARA, etc. The libraries should .... development of digital library initiatives in
contents
6 Nov 2014 - which is now in the form of a difference equation, suitable for coding in an appropriate programming language. This particular form of the PID algorithm is known as the 'positional' PID ... is initially positive. The integral component w
Contents - Disability Rights California
Adult Day Health Services (ADHC). Annual Report 2010. 15. Administration. 100 Howe Ave.,. Suite 185-N. Sacramento, CA 95825. (916) 488-9955. Legal Offices. Sacramento. 100 Howe Ave.,. Suite 235-N. Sacramento, CA 95825. (916) 488-9950. Bay Area. 1330
Table of Contents - GitHub
random to receive a new welfare program called PROGRESA. The program gave money to poor families if their children went to school regularly and the family used preventive health care. More money was given if the children were in secondary school than
Table of Contents - Groups
It is intended for information purposes only, and may ... It is not a commitment to ... Levels of Security, Performance, and Availability. MySQL Enterprise. Audit ...
Contents - Beck-Shop
www.cambridge.org. © in this web service Cambridge University Press ... 3.2.2 Convex Distance Concentration and Rademacher Processes. 139. 3.2.3 A Lower ...
Contents
May 15, 2011 - describe recent E. cecorum-related lameness cases in Georgia, ... Page 2. Enterococcus cecorum is a normally innocuous inhabitant of the gastro-intestinal tract of various mam- ..... Broiler growers in the 19-State weekly program place
contents
(f) a ∧ b = 0 implies a ⊕ b = a ∨ b,. (g) a ∨ b = 1 implies a ⊙ b = a ∧ b. Proof. ... By Proposition 2.2.4 (a), it follows that 0 is the first element and 1 is the last element of A. In order to prove that l.u.b.{a, .... (d), (e) and (f)
contents
∗The Open University, ∗∗Lexical Computing Ltd. Abstract. In this paper ... be a “pernicious source of structural ambiguity in English” (Resnik 1999), they have ... the British National Corpus (bnc) (http://www.natcorp.ox.ac.uk). .... We use
Table of Contents
The Archaeological Evidence for the Jafnids and the Nas ̣rids. 172. Denis Genequand. 5. Arabs in the Conflict between Rome and Persia, AD 491–630. 214.
Contents - Ng Woon Lam
In the language of visual art, Color and Design are the most funda- mental qualities ... and today, its development and pace of practical use in the field of science ...
Table of Contents
Feb 24, 2012 - Commission for Africa (ECA) [South African. Mission]. E-mail: [email protected]. Mail: PO Box 1091, Addis Ababa, ETHIOPIA.
Contents - Ng Woon Lam
My practical experience and theoretical understanding with color issues helped me .... determine the speed of transition between the three pure colors. Fig.
Contents
... to create an SD Card. On a Mac the “Apple Pi Baker App” makes this easy, download available here: ... Download Cannybots Software login as the 'pi' user ...
Contents
1,2The Edward S. Rogers Sr. Department of Electrical and Computer Engineering .... First in Section 1.2, basic multilinear algebra is reviewed and the commonly used ..... the training set and repeating 20 times is to reduce the dependency of.
Contents
4 Mechanisms of Masking by Schroeder-Phase Complexes .................... 37. Magdalena Wojtczak and Andrew J. ... of the Tectorial Membrane in Cochlear Sensory Processing................ 69. Guy P. Richardson, Victoria ... 79. Ricardo Gómez-Nieto,