SELF-STUDY MAP FOR APPLIED MACRO PHDs1
This document outlines a view of the sort of thing an applied PhD economist should aspire to master/read.
It’s work in progress. It probably always will be, since the subject and its teaching is in a constant state of
flux. Almost certainly no single person would get through all of this. But it gives you an idea of what is out
there. There is a distinction drawn between skills and methods on the one hand, and classic papers and
topics on the other. This distinction is a little forced, since some of the classic papers introduce skills, and
we can define a topic to be a method. But the point of it is to emphasise the premium on learning how to
do stuff, before deciding what you want to research. Many PhD applicants particularly to the minor
universities wrongly emphasise the topic before skills and methods.
Skills and methods
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Solving the household and firm problems using dynamic optimisation and forming a DSGE model
from first order conditions, resource constraints and market clearing assumptions.
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Linearising the equilibrium conditions of a DSGE model.
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Alternatives to linearisation, including experimenting with rudimentary expositions of the most
straightforward of those alternatives (eg higher order approximations; projection methods (eg
parameterised expectations) for solving non linear DSGE models). Solving for asset prices and risk
premia in non-linear DSGE models.
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Setting up and solving a model in DYNARE.
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Formulating and solving optimal policy and control problems: the derivation and implementation
of the optimal linear regulator.
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Finance: essentials of finance theory, especially risk-neutral and physical probability measures;
stochastic discount factors; and risk premia. Applications: the term structure of interest rates (noarbitrage affine term structure models); pricing risky assets using multi-factor models; pricing
options using the Black-Scholes formula.
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The derivation of and the use of the Kalman Filter: in, estimating unobserved drivers, computing
the likelihood of a DSGE model.
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Estimating and identifying simple vector autoregressions (VARs), using Cholesky factorisation,
sign restrictions, short run and long run zero restrictions; panel VARs.
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Estimating VARs, TVP-VARs and DSGE models using, where appropriate, partial information (eg
impulse response function matching), likelihood or Bayesian methods – including conjugate priors,
and simulation methods (eg like Gibbs Sampling, MCMC methods).
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Factor analysis.
1
This document is based on a self-study plan I developed while at the Bank of England. It had a lot of
input from ex-colleagues there, including Nicholas Fawcett, Richard Harrison, Andrew Meldrum, Steve
Millard, Haroon Mumtaz and Matthias Paustian. Particularly as this document veers towards finance,
labour or international macro, the past contribution of others rises. Thanks to those individuals without
implicating them in any of the mistakes left in that document, or introduced since I have been modifying it
independently.
Analytical 6605671
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The spectrum, detrending, band pass and Hodrick-Prescott filtering.
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Estimating reduced form and structural equations using cross section and panel micro data.
-
Programming (particularly in MATLAB): this is not really a distinct topic, as progress on the other
curriculum items will require learning programming skills, but, for the sake of transparency and
emphasis: in particular: ( a) use of script and function files, ( b) for and while loops, ( c) debugging,
(d) Monte Carlo techniques for testing code, (e) parallel computing,( f) optimisation using MATLAB
and other hill climbing functions, (g) global and local variables, (h) object oriented programming, (i)
creating function calls written in other languages using MEX, (j) vectorisation for fast computing
and (k) use of symbolic math toolbox.
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Maths tools: once again, not a distinct topic, since above will require, but: linear algebra,
(including matrix inversion, orthoganalisation, decompositions like QR and Cholesky (useful in VAR
analysis) function approximation, non-linear equation root finding, perturbation methods (using
exact solutions to approximations to our equation as approximate solutions to the exact equation),
solving (particularly expectational) difference equations, dynamic optimisation.
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Numerical methods for econometrics and economics: pseudo random numbers and efficient Monte
Carlo integration; assessing Monte Carlo error (e.g. for computing marginal likelihood from Gibbs
output); calculating numerical derivatives and Hessians; appropriate scaling of parameters for
optimisation; gradient and non-gradient based optimisation algorithms; assessing the convergence
of Markov chains; bootstrapping standard errors.
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SOME STUDY RESOURCES FOR THE DIRECTORATE WIDE CURRICULUM
Solving the household and firm problems using dynamic optimisation and forming a DSGE model from first
order conditions, resource constraints and market clearing assumptions
Lagrangian methods are a standard way to derive the FOC’s to the household and firm’s problem. These
are described very briefly in Obstfeld and Rogoff (1995), (p715); at more length and depth in Chow
(1997). Broadly speaking, these are applicable when the choice variable is continuous and constraints are
always binding, or you can safely approximate assuming they are.
Methods like dynamic programming are needed if the choice variable is discontinuous (shall I take a job or
not?) or the constraint (can’t borrow more than z% of income) is occasionally binding. Adda and Cooper
(2003) provide an excellent line by line, algebraic exposition of dynamic programming using value function
iteration in the case where it’s actually not needed, developing the intuition that way. Instructions on how
to program ‘value function iterations’ routines to execute dynamic programming can be found in many
places. Other, more formal treatments can be found in Stokey and Lucas (1989) and elsewhere, the
emphasis being here on the regularity conditions required for numerical procedures to find dynamic
programs to work. These materials are not considered part of the core curriculum.
Linearising the equilibrium conditions of a model
Some background best acquired by reading about Taylor’s theorem, which explains how one can
approximate any function (eg a nonlinear first order condition of the representative household) using a
sequence of higher and higher derivatives evaluated at a particular point (usually chosen to be the
deterministic steady state of a model)
Nice introductory treatment for the New Keynesian model when we are log linearising can be found in
slides Martin Ellison prepared for a Bank of England course on practical DSGE modelling here:
http://www.economics.ox.ac.uk/members/martin.ellison/Boe/dsge_all.pdf
Classic reference is the linearised RBC model in Campbell (1994). Many other expositions now on the web
by major and minor academics in the US.
Alternatives to linearisation, including experimenting with rudimentary expositions of the most
straightforward of those alternatives (eg higher order approximations; projection methods (eg
parameterised expectations) for solving non linear DSGE models)
This is a big topic, but a great introduction can be found in Heer and Maussner (2005). More technical
introductions can be found in an early book on these methods, Marimon and Scott (1999). General,
formal treatments are available in Judd (1998) and in teaching notes produced by Fernandez-Villaverde.
[Link to be added]
Setting up and solving a model in DYNARE
There is a DYNARE instruction manual to be found on the DYNARE website. Also there is a paper by
Sargent and co-authors called ‘Practising Dynare’.
Formulating and solving optimal policy and control problems: the derivation and implementation of the
optimal linear regulator
This is on the curriculum for a few reasons. First, its a necessary input into doing optimal monetary policy
analysis, which has to be a core skill in a central bank. Second, it’s not just a topic, because it has
applications for tax policy too, obviously, and also its exposition is related to the problem of finding the
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best estimator in econometrics (a topic touched on briefly and intriguingly in Ljungqvidst and Sargent
(2004)), and covering this will reinforce techniques learned in finding the solution to the household and
firm problems. (All are cases of dynamic optimisation).
Ljungqvist and Sargent (2004) is the best reference for the derivation of the optimal linear regulator. You
will need to refresh your matrix differentiation before going through this.
Not covered there is the issue of how to find a quadratic approximation to agents’ (could be a policy
maker or a consumer) utility function. That is covered in Woodford (2003) (for the sticky price model) and
Heer and Maussner (2005), and also in Rotemburg and Woodford (add reference). A nice example is
worked through in a series of lectures by Matthias Paustian [link]. The classic references are McGill (1977)
and Hall (1978). Also see Levine et al (2008).
An important issue that arises here is the distinction between commitment and discretion, and how to
compute optimal policy in these different cases.
Finance
John Cochrane’s address to the AFA is essential background reading on the finance literature and how it
relates to macroeconomics. The basics of finance theory are set out in Cochrane, chapter 1; and multifactor pricing models in chapter 6. The Black-Scholes formula and option pricing are explained in Hull,
chapter 12. There is a vast literature on no-arbitrage term structure models but no up-to-date accessible
summary or textbook treatment; we could produce a short primer on the topic.
The derivation of and the use of the Kalman Filter: in, estimating unobserved drivers, computing the
likelihood of a DSGE model
The Kalman Filter is the solution to an optimal control problem. Its widespread application in
econometrics shows that econometrics is an optimal control problem too. Hamilton (1994) provides a
line- by-line derivation which serves as a how to code it also. A really nice univariate treatment is in
lecture notes by Nimark, which I stole from liberally in my Bristol MSc time series module.
Estimating and identifying VARs
Introduction to VARs
Bread and butter topics here are: estimating VARs using OLS and MLE (explained cursorily, but the
formulae provided in Hamilton (1994)), identifying VARs using Cholesky decompositions; using sign
restrictions; short run restrictions, long run restrictions. Two good sources on identification: first, a
comprehensive discussion in a survey by Killian (2012) available here:
Second, Canova’s (2007) textbook is worth consulting. Third, take a look at Blake and Mumtaz’s (2012)
monograph on Bayesian econometrics which includes a short section on sign restrictions.
An important advanced topic that comes up from time to time is when DSGE models have a VAR
representation and when they don’t. One example of when they don’t is if there are news shocks
(advance pieces of information revealed about a future shock to TFP today, for example).
Econometrics of non-stationary data
In many situations, it makes more sense to model nonstationary data as a system, rather than in a singleequation model. This applies especially to modelling long-run relationships, where cointegrating
relationships are best expressed in a vector error correction model. The classic reference on cointegrated
VARs is Johansen (1995), but this is quite a technical book. More gentle introductions are offered by
Juselius (2006) and Banerjee, Dolado, Galbraith and Hendry (1993). Garratt, Lee, Pesaran and Shin (2006)
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also explain the theory nicely (chs 4 and 6) and then apply it to policy-relevant fields. An excellent
illustration of how the literature is relevant to central bank work on monetary policy is provided by Norges
Bank staffers Bardsen, Eitrheim, Jansen and Nymoen (2005).
Estimating VARs, TVP-VARs and DSGE models models using Bayesian (eg Gibbs Sampling) MCMC methods,
minimum distance estimation (eg impulse response function matching)
Blake and Mumtaz [reference to add] is a great primer on Bayesian VAR estimation using MCMC methods.
Lancaster (2004) is also a great introduction to MCMC methods and why they work. Gary Koop’s
monograph [Koop and Korobilis (2010)] is another good source, and there is accompanying code on his
website. An old cookbook for doing Bayesian VARs with conjugate methods is Kaddiyala and Karlsson
(1997). Villaverde has some great notes on the particle filter here:
http://economics.sas.upenn.edu/~jesusfv/ejemplo.pdf
Principal component, factor and dynamic factor analysis
Three useful sources are: Stock and Watson (2005), Del Negro and Schorfheide (2010), Chapter 6, and
Koop and Korobilis (2010), chapter 5. For principal component analysis, this monograph is useful:
http://cs5517.userapi.com/u133638729/docs/9465d37dec42/Jolliffe_I_Principal_Component_Analysis_22
4360.pdf
For forecasting with principal components, Bernanke, Boivin and Eliasz (2005) wrote an influential and
beautifully-written paper.
Estimating reduced form and structural equations using cross section and panel micro data
Panel time series
As panel data have grown in the time dimension, new techniques have emerged to exploit their the crosssection and time-series properties. This is commonly referred to as ‘panel time series’. The distinction is
more than semantic, as some methods that are appropriate for short time-dimension panels (e.g.
Arellano-Bond methods) are unsuitable for data with longer time dimensions. An excellent introduction is
the monograph by Ron Smith and Anna-Maria Fuentes (2010).
Programming
Programming (particularly in MATLAB): this is not really a distinct topic, as progress on the other
curriculum items will require learning programming skills, but, for the sake of transparency and emphasis:
in particular:
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
use of script and function files;
for and while loops;
reading data in, exporting it out, plotting;
debugging;
monte carlo techniques for testing code;
parallel computing;
optimisation using MATLAB and other hill climbing functions;
global and local variables;
object oriented programming;
creating function calls written in other languages using MEX;
vectorisation for fast computing;
use of symbolic math toolbox.
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There are a great many intros to Matlab on the web as a quick google will reveal. Sigmon and Davis’
‘Matlab primer’ (2002) is a handy book to have in your pocket. Not least because it is pocket sized.
Although you will have to take it out of the pocket to read it.
Maths tools: once again, not a distinct topic, since above will require, but: linear algebra, including matrix
inversion, function approximation, non-linear equation root finding
There are very many high quality, readable maths for economist books. The trick is finding something that
is focused enough to get you started without feeling overwhelmed. The most useful introductory source
has proven to be: Heer and Maussner’s (2005) appendices..
Numerical methods for economics and econometrics
[to be expanded and rewritten]
For most topics there will be a large choice of textbook material (Monte Carlo integration, numerical
derivatives, gradient-based optimisation using e.g. Newton-Raphson, assessing convergence of Markov
chains). An exception is that while there are a wide range of non-gradient-based optimisation algorithms,
there is not much textbook treatment. Heer and Maussner (2005) cover this in their appendix. Judd
(1998) is a great overview and worth reading cover to cover, despite its age.
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References to papers or textbooks in the curriculum
Adda, J and Cooper, R (2003) Dynamic Economics, MIT Press.
Banerjee, Dolado, Galbraith and Hendry (1993), ‘Co-Integration, Error-Correction, and the econometric
analysis of Non-Stationary Data’, Oxford University Press.
Bardsen, Eitrheim, Jansen and Nymoen (2005), ‘The Econometrics of Macroeconomic Modelling’, Oxford
University Press.
Bernanke, B, Boivin, J and Eliasz, P (2005), ‘Measuring the effects of monetary policy: a factor-augmented
vector autoregressive (FAVAR) approach’, QJE, February 2005.
Blake, A and Mumtaz, H (2012), ‘Applied Bayesian Econometrics for Central Bankers’, monograph.
Campbell, J (1994), ‘Inspecting the mechanism: an analytical approach to the stochastic growth model’,
Journal of Monetary Economics, 33(3).
Canova, F (2007), ‘Methods for applied macroeconomic research’.
Chow, G (1997), ‘Dynamic Economics: optimisation by the Lagrangian method’, OUP.
Cochrane, J H (2005), ‘Asset pricing’ (revised edition).
Cochrane, J H (2010), ‘Presidential address: discount rates’, Journal of Finance 66, pp. 1047-1108.
Dejong, D and Chetan, D, ‘Structural macroeconometrics’, Princeton University Press, 2nd edition.
Del Negro, M and Schorfheide, F (2010), ‘Bayesian Macroeconometrics’, in Handbook of Bayesian
Econometrics. Available at:
http://economics.sas.upenn.edu/~schorf/papers/bayesian_macro.pdf
Garratt, Lee, Pesaran and Shin (2005), ‘Global and National Macroeconometric Modelling’, Oxford
University Press.
Hall, R (1978), ‘Stochastic implications of the life cycle permanent income hypothesis’, Journal of Political
Economy, 86(6), 971-987.
Hamilton, J (1994), ‘Time Series Analysis’, Princeton.
Heer, and Maussner (2005), ‘DSGE modelling’, (Springer).
Hull, J C (various editions), ‘Options, Futures and Other Derivatives’.
Judd, K (1998), ‘Numerical methods in economics’, MIT press.
Kaddiyala and Karlsson, S (1997), ‘Numerical methods for estimation and inference in Bayesian VAR
models’ Journal of Applied Econometrics, 12(2), pp 99-132.
Killian, L (2012), ‘Structural vector autoregressions’, forthcoming in Handbook of Research Methods and
Applications in Empirical Macroeconomics, available now at:
http://www-personal.umich.edu/~lkilian/elgarhdbk_kilian_r1.pdf
Koop, G and Korobilis (2010), ‘Bayesian multivariate time series methods for empirical macroeconomics’:
http://personal.strath.ac.uk/gary.koop/koop_korobilis_Foundations_and_Trends_2010.pdf
Lancaster (2004), ‘An introduction to Modern Bayesian Econometrics’,Wiley-Blackwell.
Levine, P, Pearlman, J and Pierse, R (2008), ‘Linear-quadratic approximation, external habit and targeting
rules’, Journal of Economic Dynamics and Control, 32, pp 3315-3349.
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Ljungvist, L and Sargent, T (2004), ‘Recursive macroeconomic theory’, [2nd edition], MIT Press.
Marimon, R and Scott, A (1999), [eds] ‘Computational methods for the study of dynamic economies’, OUP.
McGill (1977), ‘A local analysis of N-sector capital accumulation under uncertainty’, Journal of Economic
Theory, 15,2, pp211-219.
Obstfeld, M and Rogoff, K (1995), ‘Foundations of International Economics’.
Sigmon, K and Davis, T (2002), ‘Matlab Primer’, 6th edition, Chapman and Hall.
Stock, J and Watson, M (2005), ‘Implications of dynamic factor models for VAR analysis’, NBER working
paper 11467:
http://www.nber.org/papers/w11467.pdf?new_window=1
Stokey, N and Lucas, R (1989), ‘Recursive methods in economic dynamics’, Harvard.
Woodford, M (2003), ‘Interest and prices’, Princeton, NJ.
Wooldridge, J, E (2002), ‘Econometric Analysis of Cross Section and Panel Data’, MIT Press.
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SOME CLASSIC AND ILLUSTRATIVE PAPERS BY TOPIC
General macro-time-series
Kydland, F and Prescott, E (1982), ‘Time to build and aggregate fluctuations’.
http://ideas.repec.org/a/ecm/emetrp/v50y1982i6p1345-70.html
Lucas, R (1978), ‘Asset pricing in an exchange economy’, Econometrica.
http://ideas.repec.org/a/ecm/emetrp/v46y1978i6p1429-45.html
Krussel, P and Smith, A (1998), [het agents paper]
Lucas (1976), ‘Econometric policy evaluation: a critique’.
http://ideas.repec.org/a/eee/crcspp/v1y1976ip19-46.html
Taking macro models to data
Sims, C (1981) ‘Macroeconomics and reality’
http://ideas.repec.org/a/ecm/emetrp/v48y1980i1p1-48.html
Kydland and Prescott ‘The computational experiment: an econometric tool’.
http://ideas.repec.org/p/fip/fedmsr/178.html [plus Sims’ reply in the same journal edition]
CEE (2005) ‘Nominal rigidities and the dynamic effects of a shock to monetary policy’, JPE.
http://ideas.repec.org/a/ucp/jpolec/v113y2005i1p1-45.html
Smets, F and Wouters, R (2003), ‘An estimated dynamic stochastic general equilibrium model for the euro
area’, JEEA [and their 2007 AER paper, plus the An and Schorfheide summary of Bayesian estimation].
http://ideas.repec.org/a/tpr/jeurec/v1y2003i5p1123-1175.html
Monetary policy, money and credit (Tony Yates)
Calvo, G A (1983), ‘Staggered prices in a utility-maximising framework’, Journal of Monetary
Economics, 12, 383-398.
Rotemberg, J, J (1982), ‘Sticky prices in the United States’, Journal of Political Economy, 90, 11871211.
Rotemburg and Woodford (1997), ‘An optimization-based econometric framework for the evaluation of
alternative monetary policy rules’, NBER.
http://www.nber.org/chapters/c11041.pdf
Kydland and Prescott (1977), ‘Rules rather than discretion: the inconsistency of optimal plans’.
http://www.minneapolisfed.org/research/prescott/papers/rulesdiscretion.pdf
Bernanke, Gertler, Gilchrist (1999), ‘The financial accelerator in a quantitative business cycle framework’.
http://ideas.repec.org/p/nbr/nberwo/6455.html
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Kiyotaki, N and Moore, J (1997), ‘Credit cycles’, JPE.
http://ideas.repec.org/a/ucp/jpolec/v105y1997i2p211-48.html
Lucas, R (2003), ‘Macroeconomic priorities’, AER.
http://ideas.repec.org/a/aea/aecrev/v93y2003i1p1-14.html
Taylor (1993), ‘Discretion versus policy rules in practice’ Carnegie-Rochester Series on Public Policyi [and
more recent Taylor/Williams handbook chapter].
http://ideas.repec.org/a/eee/crcspp/v39y1993ip195-214.html
Kiyotaki, N and Wright, R (1989), ‘On money as a medium of exchange’ JPE [and more recent monographs
on new monetarist economics by Williamson and Wright, written for the new handbook of monetary
economics].
http://ideas.repec.org/a/ucp/jpolec/v97y1989i4p927-54.html
Sargent and Wallace, ‘Some unpleasant monetarist arithmetic’.
http://ideas.repec.org/a/eee/crcspp/v39y1993ip195-214.html
Leeper, E (1991), ‘Equilibria under active and passive monetary and fiscal policies’, JME.
http://ideas.repec.org/a/eee/moneco/v27y1991i1p129-147.html
Consumption2
Hall, R E (1978), ‘Stochastic implications of the life-cycle permanent income hypothesis: Theory and
evidence’, Journal of Political Economy, Vol. 86, pages 971-88.
Fuhrer, J C (2000), ‘Habit formation in consumption and its implications for monetary policy’, American
Economic Review, Vol. 90, pages 367-90.
Iacoviello, M (2005), ‘House prices, borrowing constraints, and monetary policy in the business cycle’,
American Economic Review, Vol. 95, pages 739-64.
King, R G, Plosser, C I and Rebelo, S T (1988), ‘Production growth and business cycles: The basic
neoclassical model’, Journal of Monetary Economics, Vol. 21, pages 195-232.
Investment
Lucas, R E, Jr. and Prescott, E C (1971), ‘Investment under uncertainty’, Econometrica, Vol. 39, pages 65981.
Modigliani, F and Miller, M H (1958), ‘The cost of capital, corporation finance, and the theory of
investment’, American Economic Review, Vol. 48, pages 261-97.
Fiscal
Aiyagari, S R, Christiano, L J and Eichenbaum, M (1992), ‘The output, employment, and interest rate
effects of government consumption’, Journal of Monetary Economics, Vol. 30, pages 73-86.
Barro, R J (1974), ‘Are government bonds net wealth?’, Journal of Political Economy, Vol. 82, pages 1,095117.
Blanchard, O J (1985), ‘Debt, deficits and finite horizons’, Journal of Political Economy, Vol. 93, pages 22347.
2
Consumption, investment, fiscal and labour sections lifted, with apologies from recommendations by
Stephen Millard, my ex-colleague at the BoE.
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Chari, V V, Christiano, L J and Kehoe, P J (1994), ‘Optimal fiscal policy in a business cycle model’, Journal of
Political Economy, Vol. 102, pages 617-52.
Christiano, L J, Eichenbaum, M and Rebelo, S (2009), ‘When is the government spending multiplier
large?’, Journal of Political Economy, Vol. 119, pages 78-121.
Labour3
Pissarides, C ‘Equilibrium Unemployment Theory’, 2 nd Edition MIT Press.
Christoffel, K, Costain, J, Kuester, K, Linzert, T, Millard, S. P, Pierrard, O and G de Walque (2009),
‘Inflation dynamics with labour market matching: Assessing alternative specifications’, Bank of
England Working Paper No. 375.
Phillips, A, W (1958), ‘The relationship between unemployment and the rate of change of money
wages in the United Kingdom 1861-1957’, Economica, 25, 283-299.
Shimer, R (2005), ‘The cyclical behaviour of equilibrium unemployment and vacancies’, American
Economic Review, 95, 25-49.
Merz, M (1995), ‘Search in the labour market and the real business cycle’, Journal of Monetary
Economics, 36, 269-300.
Millard, S, P, Scott, A and M Sensier (1997), ‘Business cycles and the labour market: Can the theory
fit the facts?’, Oxford Review of Economic Policy, 13, 70-92.
Mortensen, D and C Pissarides (1994), ‘Job creation and job destruction in the theory of
unemployment’, Review of Economic Studies, 61, 397-415.
Phelps, E (1968), ‘Money-wage dynamics and labour market equilibrium’, Journal of Political
Economy, 76, 678-711.
Rotemberg, J, J and M Woodford (1999), ‘The cyclical behaviour of prices and costs’, in Taylor, J. B.
and M. Woodford (eds.), Handbook of Macroeconomics, Vol. 1B, Elsevier.
Trigari, A (2008), ‘Equilibrium unemployment, job flows and inflation dynamics’, Journal of Money,
Credit and Banking, 41, 1-33.
The open and international economy4
Corsetti, G, L Dedola and S Leduc (2008), ‘International Risk Sharing and the Transmission of Productivity
Shocks’, Review of Economic Studies. (CDL 2008).
Corsetti G, L Dedola and S Leduc (2010), ‘Optimal monetary policy in open economies’, Corsetti Dedola
and Leduc, in the Handbook of Monetary Economics, vol. III, Edited by Ben Friedman and Michael
Woodford. (CDL 2010).
Corsetti G and L. Dedola (2005), ‘ Macroeconomics of International Price Discrimination’, Journal of
International Economics.
3
Labour recommendations lifted from Stephen Millard, with apologies.
The international RBC etc readings originated from course materials put together by my former colleague
at the BoE Jumana Saleheen and Gianluca Corsetti, who delivered a course on international macro at the
BoE a few years ago.
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Corsetti G, L Dedola and S Leduc (2009), ‘High exchange rate volatility and low pass-through’, Journal of
Monetary Economics.
Atkeson A and A Burstein (2008), ‘Pricing-to-Market, Trade Costs and International Relative Prices’,
American Economic Review, 1998-2031.
Dornbusch R (1987), ‘ Exchange Rates and Prices’, American Economic Review.
Canzoneri M and D Henderson, ‘Monetary Policy in Interdependent Economies’, MIT press, 1991.
Canzoneri M. and D. Henderson and B Diba (2005), ‘The need for international policy coordination: what's
old, what's yet to come’, Journal of International Economics 363-384.
Corsetti, G, and P Pesenti, (2005), ‘International dimensions of optimal monetary policy’, Journal of
Monetary Economics, Elsevier, vol. 52(2), pages 281-305, March.
Dornbusch R (1980), ’Exchange Rate Economics, where do we stand?’, BPEA.
Corsetti G K Kuester and G Mueller, (2012), ‘Floats Pegs and the transmission of fiscal policy’, Banco de
Chile.
Backus, D, K and P, J Kehoe (1992), ‘International Evidence on the Historical Evidence of Business Cycles’,
American Economic Review, 82 (4), 864-888.
Backus, D, K and P, J Kehoe (1992), ‘International Real Business Cycles’, Journal of Political Economy, 100
(4), 755-775.
Backus, D, K and P, J Kehoe and F, E Kydland (1993), ‘International Business Cycles: Theory vs. Evidence’,
Quarterly Review, Federal Reserve Bank of Minneapolis.
Baxter, M, (1995), ‘International Trade and Business Cycles’, In: Gene Grossman and Kenneth Rogoff
(eds.), Handbook of International Economics, vol.3, North Holland, 1995.
Baxter, M, and M, J Crucini (1993), ‘Explaining Saving-Investment Correlations’, American Economic
Review, 83, 416-36.
Backus, D, K and P, J Kehoe and F, E Kydland (1994), `Dynamics of the Trade Balance and the Terms of
Trade: the J-curve’, American Economic Review.
Andrea Raffo, ‘Net Exports, Consumption Volatility and International Business Cycle Models’, Journal of
International Economics, 75 (1), 14-29.
Stockman A, C and L Tesar Tockman (1995), ‘Tastes and Technology in a Two-Country Model of the
Business Cycle: Explaining International Comovements’, American Economic Review, 83, 473–486.
Heathcote, J and F Perri, (2002), ‘Financial Autarky and International Business Cycles’, Journal of Monetary
Economics.
Bacchetta, P and E Van Wincoop ‘A Theory of the Currency Denomination of International Trade’, Journal
of International Economics.
Engel Devereux and Storgaard Endogenous, ‘Exchange Rate Pass-Through when Nominal Prices are Set in
Advance’, Journal of International Economics.
Ariel Burstein, Martin Eichenbaum and Sergio Rebelo (2005), ‘Large Devaluations and the Real Exchange
Rate’, Journal of Political Economy.
K Russ and B de Blas, ‘Understanding Markups in the Open Economy’, mimeo 2012.
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Rethinking Central Banking, Brookings and Alfred P. Sloan Foundation e-Book, September 2011.
Committee on International Economic and Policy Reform, Barry Eichengreen, Mohamed El‐Erian, Arminio
Fraga, Takatoshi Ito, Jean Pisani‐Ferry, Eswar Prasad, Raghuram Rajan, Maria Ramos, Carmen Reinhart ,
Hélène Rey, Dani Rodrik, Kenneth Rogoff, Hyun Song Shin, Andres Velasco, Beatrice Weder di Mauro,
Yongding Yu.
Giancarlo Corsetti and Paolo Pesenti, ‘The Simple Geometry of Transmission and Stabilization in Closed
and Open Economies’.
NBER International Seminar on Macroeconomics 2007, pages 65-116 National Bureau of Economic
Research, Inc.
Finance5
Review papers
Cochrane, J H (2007), ‘Financial markets and the real economy’, chapter in Mehra, R, Handbook of the
Equity Premium Puzzle, Elsevier, pp. 237-325. [Review of efforts to reconcile finance and macro.]
Cochrane, J H (2011) ‘Discount rates’, Journal of Finance 66(4), pp. 1047-1108. [Review of efforts to
understand variation in returns.]
Piazzesi, M (2010) ‘Affine term structure models’, chapter in Handbook of Financial Econometrics, Elsevier,
pp. 691-766. [Review of the literature on finance-based term structure models.]
The predictability of excess returns
Fama, E F (1984), ‘Forward and spot exchange rates’, Journal of Financial Economics 13(4), pp. 509528. [The FX premium puzzle.]
Cambell, J Y and Shiller, R J (1991), ‘Yield spreads and interest rate movements: a bird’s eye view’, NBER
Working Papers 3153. [Violation of the expectations hypothesis.]
Factor-based asset pricing
Sharpe, W F (1964), ‘Capital asset prices: a theory of market equilibrium under conditions of risk’, Journal
of Finance 19(3), pp. 425-442. [Excess returns relative to the risk-free rate are earned for covariance with
the market portfolio.]
Ross, S (1976), ‘The arbitrage theory of capital asset pricing’, Journal of Economic Theory 13(3), pp. 341360. [Compensation for risk on different assets appears to be earned for exposure to a small number of
risk factors.]
Fama, E F and French, K R (1993), ‘Common risk factors in the returns on stocks and bonds’, Journal of
Financial Economics 33(1), pp. 3-56. [The three-factor model that incorporated small firm and leverage
effects.]
No-arbitrage pricing and applications
Kreps, D M (1981), ‘Arbitrage and equilibrium in economies with infinitely many commodities’, Journal of
Mathematical Economics 8(1), pp. 15-35. [The start of modern asset pricing and no-arbitrage theory.
More accessible introductions are available, e.g. Cochrane’s asset pricing textbook.]
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This list of finance papers lifted, with apologies, from recommendations by Andrew Meldrum.
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Analytical 6605671
Black, F and Scholes, M (1973), ‘The pricing of options and corporate liabilities’, Journal of Political
Economy 81(3), pp. 637-654. [Seminal paper on option pricing.]
Vasicek, O (1977), ‘An equilibrium characterisation of the term structure’, Journal of Financial Economics
5, pp. 177-188. [The start of no-arbitrage term structure modelling.]
Duffie, D and Kan, R (1996), ‘A yield factor model of interest rates’, Mathematical Finance 6(4), pp. 379406. [Multi-factor ATSM.]
Ang, A and Piazzesi, M (2003), ‘A no-arbitrage vector autoregression of term structure dynamics with
macroeconomic and latent variables’, Journal of Monetary Economics 50, pp. 745-787. [The birth of
empirical macro-finance term structure models – incorporating macro and financial variables in the same
model.]
Consumption-based asset pricing
Lucas, R E (1978), ‘Asset prices in an exchange economy’, Econometrica 46(6), pp. 1429-1445. [The start of
consumption-based inter-termporal asset pricing.]
Mehra, R and Prescott, E C (1985), ‘The equity premium: a puzzle’, Journal of Monetary Economics 15(2),
pp. 145-161. [It is hard to reconcile the smoothness of consumption with the average return premium on
equities over risk-free assets.]
Campbell, J Y and Cochrane, J H (1999), ‘By force of habit: a consumption-based explanation of aggregate
stock market behaviour’, Journal of Political Economy 107(2), pp. 205-251. [First consumption-based
model to have any hope of matching a few asset pricing facts.]
Corporate finance
Modigliani, F and Miller, M (1958), ‘The cost of capital, corporate finance and the theory of investment’,
American Economic Review 48(3), pp. 261-297. [The classic irrelevance result in corporate finance.]
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