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en-usGiuseppe RagusaCopyright (c) Giuseppe Ragusa.Thu, 16 Feb 2017 00:00:00 +1100Econometrics of DSGE models
http://gragusa.org/teaching/eief-dsge/
Thu, 16 Feb 2017 00:00:00 +1100Giuseppe Ragusahttp://gragusa.org/teaching/eief-dsge/<p>This is a course on the econometric techniques used in the estimation of dynamic
macroeconomic models (DSGE models). The aim of the course is mostly theoretical, but applications are also presented using <a href="http://julialang.org">Julia</a>.</p>
<p></p>
<h3 id="topics">Topics</h3>
<ol>
<li>Motivation: DSGE models and their applications</li>
<li><p>Approximating and solving DSGE models</p>
<p>a. State space representation
b. Constructing (log-)linear approximation</p></li>
<li><p>Time series properties of the model and data</p></li>
<li><p>Classical estimation of DSGE models</p>
<p>a. Generalized Method of Moments (GMM)</p>
<p>b. Simulated Method of Moments (SMM) and Indirect Inference (IF)</p>
<p>c. Impulse response functions matching</p></li>
<li><p>Bayesian estimation of DSGE models</p>
<p>a. (log-)linear models</p>
<p>b. Non linear models</p></li>
<li><p>The twilight zone of DSGE estimation</p>
<p>a. Identification</p>
<p>b. Feasible non linear estimation</p>
<p>c. VAR and DSGE</p>
<p>d. Limited information estimation</p></li>
</ol>
<h3 id="readings">Readings</h3>
<p>This is a list of readings. This is by no means comprehensive — it
simply reflects the source that are closely related with the topics
covered in lectures.</p>
<h4 id="books">Books:</h4>
<ul>
<li>Canova, F. (2007), Methods for Applied Macroeconomic Research,
Princeton: Princeton University Press.</li>
<li>Dejong, D.N. and C. Dave (2007), Structural Macroeconomics,
Princeton: Princeton University Press</li>
</ul>
<h4 id="papers">Papers:</h4>
<ul>
<li>An and Schoerfheide (2007) Bayesian Analysis of DSGE Models,
Econometric Reviews, 26(2-4), 2007, 113-172.
<a href="http://www.tandfonline.com/doi/abs/10.1080/07474930701220071">Download</a></li>
<li>Fernández-Villaverde, Jesús. “The econometrics of DSGE models.”
SERIEs 1.1-2 (2010): 3-49.
<a href="http://link.springer.com/article/10.1007/s13209-009-0014-7">Download</a></li>
<li>Schorfheide, Frank. “Loss function-based evaluation of DSGE models.”
Journal of Applied Econometrics 15.6 (2000): 645-670.
<a href="http://onlinelibrary.wiley.com/doi/10.1002/jae.582/full">Download</a></li>
<li>Arulampalam, M. Sanjeev, et al. “A tutorial on particle filters for
online nonlinear/non-Gaussian Bayesian tracking.” Signal Processing,
IEEE Transactions on 50.2 (2002): 174-188.
<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=978374">Download</a></li>
<li>Doucet, Arnaud, and Adam M. Johansen. “A tutorial on particle
filtering and smoothing: Fifteen years later.” Handbook of Nonlinear
Filtering 12.656-704 (2009): 3.
<a href="http://automatica.dei.unipd.it/tl_files/utenti/lucaschenato/Classes/PSC10_11/Tutorial_PF_doucet_johansen.pdf">Download</a></li>
<li>Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. “Particle
markov chain monte carlo methods.” Journal of the Royal Statistical
Society: Series B (Statistical Methodology) 72.3 (2010): 269-342.
<a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2009.00736.x/full">Download</a></li>
<li>Gallant, A. Ronald, Raffaella Giacomini, and Giuseppe Ragusa. Bayesian
estimation of state space models using moment conditions. Technical
report, 2015. <a href="http://www.aronaldg.org/papers/bliml.pdf">Download</a></li>
<li>Giacomini, Raffaella. “The relationship between DSGE and VAR models.”
Advances in Econometrics 31 (2013).
<a href="http://www.emeraldinsight.com/doi/abs/10.1108/S0731-9053(2013)0000031001">Download</a></li>
<li>Komunjer, Ivana, and Serena Ng. “Dynamic identification of
stochastic general equilibrium models.” Econometrica 79.6
(2011):1995-2032.
<a href="http://www.columbia.edu/~sn2294/pub/ecta11.pdf">Download</a></li>
<li>Del Negro, Marco, and Frank Schorfheide. “Bayesian
macroeconometrics.” The Oxford handbook of Bayesian econometrics 293
(2011): 389.
<a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.414.4871&rep=rep1&type=pdf">Download</a></li>
</ul>
<h3 id="julia">Julia</h3>
<p><a href="http://julialang.org">Julia</a> is a high-level, high-performance dynamic
programming language for technical computing, with syntax that is
familiar to users of other technical computing environments. In
particular, its syntax is similar to Matlab. The similarity of the
syntax means that a lot of Matlab code will run almost unmodified.</p>
<p><code>Julia</code> has many advantages over other languages and for this reason is
being extensively used in industries and in research.</p>
<p>Recently, the <a href="https://www.newyorkfed.org/">Federal Reserve of New York</a> has
open sourced its macroeconomic model (used for producing forecast about key
variables and to conduct policy experiment). The code is written in Julia. You
can read about
it
<a href="http://libertystreeteconomics.newyorkfed.org/2015/12/the-frbny-dsge-model-meets-julia.html">here</a>.
The code is <a href="https://github.com/FRBNY-DSGE/DSGE.jl">here</a>.</p>
<p><a href="http://quant-econ.net/jl/learning_julia.html">Programming in Julia</a> in an
excellent tutorial is written by Thomas J. Sargent and John Stachurski. Along
with being a very good introduction to the language, this is also a complete macroeconomic textbook with concept illustrated in Julia.
.</p>
<h2 id="slides">slides</h2>
<ul>
<li><a href="files/teaching/lecture_1.pdf">Lecture 1</a></li>
<li><a href="files/teaching/lecture_2.pdf">Lecture 2</a></li>
<li><a href="files/teaching/lecture_4.pdf">Lecture 4</a></li>
<li><a href="files/teaching/lecture_5.pdf">Lecture 5</a> <a href="files/teaching/MH.ipynb">Jupyter notebook</a> <a href="files/teaching/PS-1.pdf">Problem set #1</a></li>
<li><a href="files/teaching/lecture_6.pdf">Lecture 6</a></li>
<li><a href="files/teaching/lecture_7.pdf">Lecture 7</a></li>
<li><a href="files/teaching/lecture_9.pdf">Lecture 9</a> <a href="files/teaching/particle_filter.ipynb">Jupyter notebook</a></li>
</ul>Applied Econometrics and Statistics
http://gragusa.org/teaching/ase/
Mon, 13 Feb 2017 00:00:00 +1100Giuseppe Ragusahttp://gragusa.org/teaching/ase/<p>The aim of this course is to provide an introduction to the practice of
econometrics. While both theoretical and practical aspects will be covered,
emphasis will be on intuitive understanding. Concepts will be illustrated with
real world applications on real world data.</p>
<p>
<span class="label error outline"><a href="../files/teaching/syllabus-ase-2017.pdf">Syllabus</a></span>
<span class="label outline"><a href="http://gragusa.org/ase">Class website</a></span></p>Econometric Theory
http://gragusa.org/teaching/grad-et/
Mon, 13 Feb 2017 00:00:00 +1100Giuseppe Ragusahttp://gragusa.org/teaching/grad-et/<p>The course main objective is to provide students with a solid grasp of the
probabilistic and statistical underpinnings of the most common econometric
techniques. The topics studied will be of practical use to any student who plans
on confronting data in their thesis or wishes to read and precisely understand
the econometrics typically used in empirical research published in academic
journals. The recommended software package is <a href="http://www.julialang.org">Julia</a>.</p>
<p></p>
<p>Econometric theory has two parts. The first part focuses on the specification
and estimation of the linear regression models and its extensions. The second
part focuses on the theory and the applications of time series methods in
econometrics.</p>
<p><span class="label outline"><a href="http://gragusa.org/et">Class website</a></span></p>Problem sets
http://gragusa.org/ase/problemsets/
Sat, 11 Feb 2017 00:00:00 UTCGiuseppe Ragusahttp://gragusa.org/ase/problemsets/
<p>There will be a weekly problem set. The assignments will involve both theoretical and empirical work. Group study and free discussion are encouraged. But you should submit your own answers. You will probably find the class very hard to follow if you fail to spend sufficient time on all of the problem sets. The problem sets are part of the final grade as explained in the next section below.</p>
<p>If you have any question on the problem sets, please ask me or TA’s during our office hours. Our office hours are for you. I prefer to talk to you in person. I feel that Email is not a very efficient way to ask econometric questions.</p>
<p>Problem set answers are to be turned in on time. You can hand in the homework AFTER the class. Please do not come and hand it in to me whilst I am lecturing. Do not email assignments. Late solution will not be accepted!</p>
<h4 id="problem-sets">Problem sets</h4>
<ul>
<li><p><a href="http://docenti.luiss.it/protected-uploads/580/2017/02/20170215192957-P_1.pdf">Problem Set 1</a></p></li>
<li><p>Problem Set 2 (Due Tuesday, March, 7 2017)</p>
<ul>
<li><a href="http://docenti.luiss.it/protected-uploads/580/2017/03/20170302161751-PS02_Questions.pdf">Questions</a></li>
<li><a href="http://docenti.luiss.it/protected-uploads/580/2017/03/20170302161740-PS02_Answers.pdf">Answer sheet</a></li>
</ul>
<p><em>Note:</em> This problem set is multiple choice. You have to print the <a href="http://docenti.luiss.it/protected-uploads/580/2017/03/20170302161740-PS02_Answers.pdf">answer sheet</a>, fill your id, and fill your answers based on the <a href="http://docenti.luiss.it/protected-uploads/580/2017/03/20170302161751-PS02_Questions.pdf">questions</a>.</p></li>
<li><p>Problem Set 3 (Due Thursday, March 16 2017)
This problem set is personalized. Each student must download her own version of the problem set. To download the problem set use the following URL:
<code>http://gragusa.org/ps3/xxxxxx.pdf</code> where <code>xxxxxx</code> is the LUISS ID of the student.</p></li>
<li><p>Problem Set 4 (Due Tuesday, April 11 2017)
This problem set is personalized. Each student must download her own version of the problem set. To download the problem set use the following URL:
<code>http://gragusa.org/ps4/xxxxxx.pdf</code> where <code>xxxxxx</code> is the LUISS ID of the student.</p></li>
<li><p>Problem Set 5 (Due Tuesday, May 2 2017)
This problem set is personalized. Each student must download her own version of the problem set. To download the problem set use the following URL:
<code>http://gragusa.org/ps5/xxxxxx.pdf</code> where <code>xxxxxx</code> is the LUISS ID of the student.</p></li>
<li><p><a href="../files/teaching/PS_06.pdf">Problem Set 6</a></p></li>
</ul>
tasessions et
http://gragusa.org/et/tasessions/
Sat, 11 Feb 2017 00:00:00 UTCGiuseppe Ragusahttp://gragusa.org/et/tasessions/<p>The TAs will lead a weekly practice session which will be held in the computer labs (301 and 306).</p>
<p>The sessions are an important part of the course and regular and
attendance is strongly advised. During these sessions, the TA will review the
concepts introduced in class and solve applied problems using Julia. Sessions are
also useful for asking questions about the problem sets.</p>
<p>The following table gives the distribution of students across the two sessions:</p>
<table>
<thead>
<tr>
<th align="left">TA</th>
<th align="left">Time</th>
<th align="left">Students</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left">Siria Angino</td>
<td align="left">10:15-11:45</td>
<td align="left">J-Z</td>
</tr>
<tr>
<td align="left">Chiara Perricone</td>
<td align="left">8:45-10:25</td>
<td align="left">A-I</td>
</tr>
</tbody>
</table>
Problem sets et
http://gragusa.org/et/problemsets/
Fri, 10 Feb 2017 00:00:00 UTCGiuseppe Ragusahttp://gragusa.org/et/problemsets/
<p>There will be a weekly problem set. The assignments will involve both theoretical and empirical work. Group study and free discussion are encouraged. But you should submit your own answers. You will probably find the class very hard to follow if you fail to spend sufficient time on all of the problem sets. The problem sets are part of the final grade as explained in the next section below.</p>
<p>If you have any question on the problem sets, please ask me or TA’s during our office hours. Our office hours are for you. I prefer to talk to you in person. I feel that email is not a very efficient way to ask econometric questions.</p>
<p>Problem set answers are to be turned in on time. You can hand in the homework AFTER the class. Late solution will not be accepted!</p>
<h4 id="problem-set">Problem set</h4>
<ul>
<li><p><a href="http://docenti.luiss.it/perricone/files/2017/02/PS_01.pdf">Problem Set 1</a></p></li>
<li><p><a href="http://docenti.luiss.it/perricone/files/2017/02/PS_02.pdf">Problem Set 2</a></p></li>
<li><p><a href="http://docenti.luiss.it/perricone/files/2017/02/PS_03.pdf">Problem Set 3</a></p></li>
<li><p><a href="http://docenti.luiss.it/perricone/files/2017/02/PS_04.pdf">Problem Set 4</a></p></li>
<li><p><a href="http://docenti.luiss.it/perricone/files/2017/02/PS_05.pdf">Problem Set 5</a></p></li>
</ul>
alertet
http://gragusa.org/et/alert/
Fri, 10 Feb 2017 00:00:00 UTCGiuseppe Ragusahttp://gragusa.org/et/alert/
<h4 id="announcements">Announcements</h4>
<ul>
<li><a href="../et_final">Final marks for June 15 exam</a></li>
</ul>
topics et
http://gragusa.org/et/topics/
Fri, 10 Feb 2017 00:00:00 UTCGiuseppe Ragusahttp://gragusa.org/et/topics/<p><strong>The following topics will be covered:</strong></p>
<ul>
<li><p>Asymptotic theory: law of large numbers and central limit theorems
for iid and time series data</p></li>
<li><p>Parametric and semi-parametric estimation techniques</p></li>
<li><p>Properties of estimators</p></li>
<li><p>Conditional expectation and related concepts with application to
econometrics</p></li>
<li><p>Single equation linear model and OLS estimation</p></li>
<li><p>Instrumental variables estimation of single equation linear model</p></li>
<li><p>Limited dependent variable models</p></li>
<li><p>Panel Data models</p></li>
<li><p>Univariate ARMA processes with stochastic and non-stochastic trends</p></li>
<li><p>Vector Autoregression (VAR)</p></li>
<li><p>Forecasting with ARMA and VAR models</p></li>
<li><p>Special Topics: Generalized Method of Moments</p></li>
</ul>
About ASE
http://gragusa.org/ase/body/
Sat, 17 Dec 2016 00:00:00 UTCGiuseppe Ragusahttp://gragusa.org/ase/body/<p>The aim of <strong>Applied Statistics and Econometrics</strong> is to provide an introduction to the practice of econometrics. While both theoretical and practical aspects are covered, emphasis will be on intuitive understanding and concepts will be illustrated with real-world applications.</p>
<p></p>
<p>Throughout, we will focus on both understanding and doing. The understanding will come from lectures, class discussions, and problem solving. The doing will come from extensive statistical software use. This course requires a quarter-long commitment. Econometrics is best learned by doing, and I will require you to do a fair amount of hands-on work.</p>
<p>For further details about the structure of the course, please download the <a href="../files/teaching/syllabus-ase-2017.pdf">syllabus</a>. The syllabus has information about this course. In particular, it has information on how, where and when to contact me and the teaching assistants; an outline of the course content, the schedule of exam dates; <strong>the grading policy</strong>, and other important organizational details of the course. You can find some of this information on this web page, but not all. So, please, refer to the <a href="../files/teaching/syllabus-ase-2017.pdf">syllabus</a> for any questions you may have about the course.</p>
<p><span class="label error outline"><a href="../files/teaching/syllabus-ase-2017.pdf">Syllabus</a></span></p>
<h4 id="lecture-content">Lecture content</h4>
<table>
<thead>
<tr>
<th align="left">n.</th>
<th align="left">date</th>
<th align="left">topic</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left">01</td>
<td align="left">M 13-02-2016</td>
<td align="left">Introduction to econometrics</td>
</tr>
<tr>
<td align="left">02</td>
<td align="left">T 14-02-2016</td>
<td align="left">Review of statistics</td>
</tr>
<tr>
<td align="left">03</td>
<td align="left">M 20-02-2016</td>
<td align="left">Review of statistics I</td>
</tr>
<tr>
<td align="left">04</td>
<td align="left">T 21-02-2016</td>
<td align="left">Review of statistics II</td>
</tr>
<tr>
<td align="left">05</td>
<td align="left">M 27-02-2016</td>
<td align="left">Bivariate regression I</td>
</tr>
<tr>
<td align="left">06</td>
<td align="left">T 28-02-2016</td>
<td align="left">Bivariate regression II</td>
</tr>
<tr>
<td align="left">07</td>
<td align="left">M 06-03-2016</td>
<td align="left">Bivariate regression III</td>
</tr>
<tr>
<td align="left">08</td>
<td align="left">T 07-03-2016</td>
<td align="left">Endogeneity and causality</td>
</tr>
<tr>
<td align="left">09</td>
<td align="left">M 13-03-2016</td>
<td align="left">Multiple regression I</td>
</tr>
<tr>
<td align="left">10</td>
<td align="left">T 14-03-2016</td>
<td align="left">Multiple regression II</td>
</tr>
<tr>
<td align="left">11</td>
<td align="left">M 20-03-2016</td>
<td align="left">Multiple regression II</td>
</tr>
<tr>
<td align="left">12</td>
<td align="left">T 21-03-2016</td>
<td align="left">Nonlinear regression models I</td>
</tr>
<tr>
<td align="left">13</td>
<td align="left">M 27-03-2016</td>
<td align="left">Nonlinear regression models II</td>
</tr>
<tr>
<td align="left">14</td>
<td align="left">T 28-03-2016</td>
<td align="left">Midterm</td>
</tr>
<tr>
<td align="left">15</td>
<td align="left">M 03-04-2016</td>
<td align="left">Assessing regression studies</td>
</tr>
<tr>
<td align="left">16</td>
<td align="left">T 04-04-2016</td>
<td align="left">~<del>Binary dependent variable I</del>~</td>
</tr>
<tr>
<td align="left">17</td>
<td align="left">M 10-04-2016</td>
<td align="left">Binary dependent variable I</td>
</tr>
<tr>
<td align="left">18</td>
<td align="left">T 11-04-2016</td>
<td align="left">Panel Data I</td>
</tr>
<tr>
<td align="left">19</td>
<td align="left">T 24-04-2016</td>
<td align="left">Panel Data II</td>
</tr>
<tr>
<td align="left">20</td>
<td align="left">T 02-05-2016</td>
<td align="left">Instrumental variables regression I</td>
</tr>
<tr>
<td align="left">21</td>
<td align="left">M 08-05-2016</td>
<td align="left">Instrumental variables regression II</td>
</tr>
<tr>
<td align="left">22</td>
<td align="left">T 09-05-2016</td>
<td align="left">Applications</td>
</tr>
</tbody>
</table>About ET
http://gragusa.org/et/body/
Sat, 17 Dec 2016 00:00:00 UTCGiuseppe Ragusahttp://gragusa.org/et/body/<p>The course main objective is to provide students with a solid grasp of the
probabilistic and statistical underpinnings of the most common econometric
techniques. The topics studied will be of practical use to any student who plans
on confronting data in their thesis or wishes to read and precisely understand
the econometrics typically used in empirical research published in academic
journals.
<strong>Econometric theory</strong> has two parts:</p>
<p>The <strong>first part</strong> focuses on the specification and estimation of the
linear regression models and its extensions.</p>
<p>The <strong>second part</strong> focuses on the theory and the applications of time
series methods in econometrics.</p>
<p>Throughout, we will focus on both understanding and doing. The understanding will come from lectures, class discussions, and problem solving. The doing will come from extensive statistical software use. This course requires a quarter-long commitment. Econometrics is best learned by doing, and I will require you to do a fair amount of hands-on work. Hands-on work requires the use of computer software. In this class we use <a href="http://julialang.org">Julia</a>.</p>
<h4 id="textbooks">Textbooks</h4>
<p>Lectures of the first part of the course will be mostly based on the
following two graduate textbooks:</p>
<ul>
<li>Wooldridge, Jeffrey M. Econometric analysis of cross section and panel
data. MIT press, 2010.</li>
</ul>
<p>Lectures of the second part will be based on the following textbooks:</p>
<ul>
<li>J.D. Hamilton. Time Series Analysis. Princeton University Press, 1994</li>
</ul>
<p>Another reference is:</p>
<ul>
<li>Hal White (2011), Asymptotic Theory for Econometricians, Academic Press</li>
</ul>
<h4 id="exams-and-grades">Exams and grades</h4>
<p>There will be two in class exam. A midterm and a comprehensive final.</p>
<p>The midterm will be held on March 28, 2017.</p>
<p>The grade will consist of a weighted average of these two exams
and the problem sets. The midterm will count 30%, the problem set 30%, and the final the remaining 40%. The final grade will be the largest between the weighted grade as described above and the grade on the final. More specifically, denoting <em>F</em>, <em>M</em>, and <em>PS</em> your grade on the final, midterm, and problem set, respectively, your grade will be</p>
<p><strong>Final Grade = max(.4*F + .3*M + .3*PS, .8*F + .2*PS, F)</strong></p>
<p><em>Note:</em> The midterm and the problem sets will be considered only for the first two final exam dates.</p>