_biblio.bib

@misc{Aguirre.Danielsson_2020_WhichProgrammingLanguage,
  type = {Blog},
  title = {Which Programming Language Is Best for Economic Research: {{Julia}}, {{Matlab}}, {{Python}} or {{R}}?},
  author = {Aguirre, Alvaro and Danielsson, Jon},
  year = {2020},
  journal = {VOX.EU CEPR Research-based policy analysis and commentary from leading economists},
  url = {https://voxeu.org/article/which-programming-language-best-economic-research},
  abstract = {The most widely used programming languages for economic research are Julia, Matlab, Python and R. This column uses three criteria to compare the languages: the power of available libraries, the speed and possibilities when handling large datasets, and the speed and ease-of-use for a computationally intensive task. While R is still a good choice, Julia is the language the authors now tend to pick for new projects and generally recommend.}
}

@article{An.Schorfheide_2007_BayesianAnalysisDSGE,
  title = {Bayesian {{Analysis}} of {{DSGE Models}}},
  author = {An, Sungbae and Schorfheide, Frank},
  year = {2007},
  journal = {Econometric Reviews},
  volume = {26},
  number = {2-4},
  pages = {113--172},
  doi = {10.1080/07474930701220071},
  abstract = {This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models and a DSGE model that was solved with a second-order perturbation method.}
}

@incollection{Anderson.McGrattan.Hansen.EtAl_1996_MechanicsFormingEstimating,
  title = {Mechanics of Forming and Estimating Dynamic Linear Economies},
  booktitle = {Handbook of {{Computational Economics}}},
  author = {Anderson, Evan W. and McGrattan, Ellen R. and Hansen, Lars Peter and Sargent, Thomas J.},
  year = {1996},
  volume = {1},
  pages = {171--252},
  publisher = {Elsevier},
  url = {https://doi.org/10.1016/S1574-0021(96)01006-4},
  abstract = {This paper describes the recent advances for rapidly and accurately solving matrix Riccati and Sylvester equations and applies them to devise efficient computational methods for solving and estimating dynamic linear economies. The chapter explores the most promising solution methods available and compares their speed and accuracy for some particular economic examples. Except for the simplest dynamic linear models, it is necessary to compute solutions numerically. In estimation contexts, computation speed is important because climbing a likelihood function can require that a model be solved many times. Methods that are faster than direct iterations on the Riccati equation and are more reliable than solutions based on eigenvalue--eigenvector decompositions of the state--costate evolution equation are discussed in the chapter. Two generalizations are presented in the chapter: The first generalization introduces forcing sequences or ``uncontrollable states'' into the deterministic regulator problem, while the second generalization introduces, among other things, discounting and uncertainty into the augmented regulator problem.},
  isbn = {978-0-444-89857-9}
}

@book{Anderson.Moore_1979_OptimalFiltering,
  title = {Optimal Filtering},
  author = {Anderson, Brian and Moore, John},
  year = {1979},
  series = {Dover Books on Engineering},
  edition = {Dover ed., unabridged republ},
  publisher = {Dover Publ},
  address = {Mineola, NY},
  abstract = {This graduate-level text augments and extends beyond undergraduate studies of signal processing, particularly in regard to communication systems and digital filtering theory. Vital for students in the fields of control and communications, its contents are also relevant to students in such diverse areas as statistics, economics, bioengineering, and operations research. Topics include filtering, linear systems, and estimation; the discrete-time Kalman filter; time-invariant filters; properties of Kalman filters; computational aspects; and smoothing of discrete-time signals. Additional subjects encompass applications in nonlinear filtering; innovations representations, spectral factorization, and Wiener and Levinson filtering; parameter identification and adaptive estimation; and colored noise and suboptimal reduced order filters. Each chapter concludes with references, and four appendixes contain useful supplementary material.},
  isbn = {978-0-486-43938-9}
}

@article{Aruoba.Fernandez-Villaverde_2015_ComparisonProgrammingLanguages,
  title = {A Comparison of Programming Languages in Macroeconomics},
  author = {Aruoba, S. Bora{\u g}an and {Fern{\'a}ndez-Villaverde}, Jes{\'u}s},
  year = {2015},
  journal = {Journal of Economic Dynamics and Control},
  volume = {58},
  pages = {265--273},
  doi = {10.1016/j.jedc.2015.05.009},
  abstract = {We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++14, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and briefly comment on the strengths and weaknesses of each language.},
  annotation = {note the Update available at https://www.sas.upenn.edu/{\textasciitilde}jesusfv/Update\_March\_23\_2018.pdf}
}

@article{Ascari.Fagiolo.Roventini_2015_FatTailDistributionsBusinessCycle,
  title = {Fat-{{Tail Distributions}} and {{Business-Cycle Models}}},
  author = {Ascari, Guido and Fagiolo, Giorgio and Roventini, Andrea},
  year = {2015},
  journal = {Macroeconomic Dynamics},
  volume = {19},
  number = {2},
  pages = {465--476},
  doi = {10.1017/S1365100513000473},
  abstract = {Recent empirical findings suggest that macroeconomic variables are seldom normally distributed. For example, the distributions of aggregate output growth-rate time series of many OECD countries are well approximated by symmetric exponential-power (EP) densities with Laplace fat tails. In this work, we assess whether real business cycle (RBC) and standard medium-scale New Keynesian (NK) models are able to replicate this statistical regularity. We simulate both models, drawing Gaussian- vs Laplace-distributed shocks, and we explore the statistical properties of simulated time series. Our results cast doubts on whether RBC and NK models are able to provide a satisfactory representation of the transmission mechanisms linking exogenous shocks to macroeconomic dynamics.}
}

@book{Bjornland.Thorsrud_2015_AppliedTimeSeries,
  title = {Applied Time Series for Macroeconomics},
  author = {Bj{\o}rnland, Hilde Christiane and Thorsrud, Leif Anders},
  year = {2015},
  edition = {2. utgave, 1. opplag},
  publisher = {Gyldendal Akademisk},
  address = {Oslo},
  abstract = {This book focuses on time series econometrics with applications in macroeconomics. The text shows how to formulate time series models, carry out forecasting and structural analyses, and work with stationary and nonstationary data alike. Univariate and multivariate models are covered, as are methods for breaking down time series data into trends and cycles. The book is filled with practical applications using macroeconomic time series, and MATLAB code accompanies all examples. Simple Monte Carlo simulations are explained and used to illustrate important concepts. The book should be easily accessible for graduate students with one or more courses in statistics and regression analysis, but who have never been introduced to time series analysis before. Applied researcher and analysts in business, governmental institutions and academia may benefit from the book as it provides examples and tools relevant for their tasks.},
  isbn = {978-82-05-48089-6}
}

@article{Blanchard.Quah_1989_DynamicEffectsAggregate,
  title = {The {{Dynamic Effects}} of {{Aggregate Demand}} and {{Supply Disturbances}}},
  author = {Blanchard, Olivier J. and Quah, Danny},
  year = {1989},
  month = sep,
  journal = {American Economic Review},
  volume = {79},
  number = {4},
  pages = {655--673},
  abstract = {We interpret fluctuations in GNP and unemployment as due to two types of disturbances: disturbances that have a permanent effect on output and disturbances that do not. We interpret the first as supply disturbances, the second as demand disturbances.Demand disturbances have a hump-shaped mirror-image effect on output and unemployment. The effect of supply disturbances on output increases steadily over time, peaking after two years and reaching a plateau after five years.}
}

@article{Bloom_2009_ImpactUncertaintyShocks,
  title = {The {{Impact}} of {{Uncertainty Shocks}}},
  author = {Bloom, Nicholas},
  year = {2009},
  journal = {Econometrica},
  volume = {77},
  number = {3},
  pages = {623--685},
  doi = {10.3982/ECTA6248},
  copyright = {http://doi.wiley.com/10.1002/tdm\_license\_1.1}
}

@book{Brandimarte_2006_NumericalMethodsFinance,
  title = {Numerical Methods in Finance and Economics: A {{MATLAB-based}} Introduction},
  author = {Brandimarte, Paolo},
  year = {2006},
  series = {Statistics in Practice},
  edition = {2nd ed},
  publisher = {Wiley Interscience},
  address = {Hoboken, N.J},
  abstract = {A state-of-the-art introduction to the powerful mathematical and statistical tools used in the field of finance},
  isbn = {978-0-471-74503-7},
  keywords = {Economics,Finance,Statistical methods}
}

@article{Caldara.Iacoviello_2022_MeasuringGeopoliticalRisk,
  title = {Measuring {{Geopolitical Risk}}},
  author = {Caldara, Dario and Iacoviello, Matteo},
  year = {2022},
  month = apr,
  journal = {American Economic Review},
  volume = {112},
  number = {4},
  pages = {1194--1225},
  doi = {10.1257/aer.20191823},
  abstract = {We present a news-based measure of adverse geopolitical events and associated risks. The geopolitical risk (GPR) index spikes around the two world wars, at the beginning of the Korean War, during the Cuban Missile Crisis, and after 9/11. Higher geopolitical risk foreshadows lower investment and employment and is associated with higher disaster probability and larger downside risks. The adverse consequences of the GPR index are driven by both the threat and the realization of adverse geopolitical events. We complement our aggregate measures with industry- and firm-level indicators of geopolitical risk. Investment drops more in industries that are exposed to aggregate geopolitical risk. Higher firm-level geopolitical risk is associated with lower firm-level investment. (JEL C43, E32, F51, F52, G31, H56, N40)}
}

@article{Calvo_1983_StaggeredPricesUtilitymaximizing,
  title = {Staggered Prices in a Utility-Maximizing Framework},
  author = {Calvo, Guillermo A.},
  year = {1983},
  month = sep,
  journal = {Journal of Monetary Economics},
  volume = {12},
  number = {3},
  pages = {383--398},
  doi = {10.1016/0304-3932(83)90060-0},
  abstract = {We develop a model of staggered prices along the lines of Phelps (1978) and Taylor (1979, 1980), but utilizing an analytically more tractable price-setting technology. `Demands' are derived from utility maximization assuming Sidrauski-Brock infinitely-lived families. We show that the nature of the equilibrium path can be found out on the basis of essentially graphical techniques. Furthermore, we demonstrate the usefulness of the model by analyzing the welfare implications of monetary and fiscal policy, and by showing that despite the price level being a predetermined variable, a policy of pegging the nominal interest rate will lead to the existence of a continuum of equilibria.}
}

@inbook{Cantore.Gabriel.Levine.EtAl_2013_ScienceArtDSGE,
  title = {The Science and Art of {{DSGE}} Modelling: {{I}} -- Construction and {{Bayesian}} Estimation},
  booktitle = {Handbook of {{Research Methods}} and {{Applications}} in {{Empirical Macroeconomics}}},
  author = {Cantore, Cristiano and Gabriel, Vasco J. and Levine, Paul and Pearlman, Joseph and Yang, Bo},
  year = {2013},
  pages = {411--440},
  publisher = {Edward Elgar Publishing},
  doi = {10.4337/9780857931023.00026},
  collaborator = {Hashimzade, Nigar and Thornton, Michael},
  isbn = {978-0-85793-102-3}
}

@book{Chacon_2014_ProGit,
  title = {Pro {{Git}}},
  author = {Chacon, Scott},
  year = {2014},
  series = {The Expert's Voice in Software Development},
  edition = {Second edition},
  publisher = {Apress},
  address = {New York, NY},
  abstract = {Pro Git (Second Edition) is your fully-updated guide to Git and its usage in the modern world. Git has come a long way since it was first developed by Linus Torvalds for Linux kernel development. It has taken the open source world by storm since its inception in 2005, and this book teaches you how to use it like a pro. Effective and well-implemented version control is a necessity for successful web projects, whether large or small. With this book you'll learn how to master the world of distributed version workflow, use the distributed features of Git to the full, and extend Git to meet your every need. Written by Git pros Scott Chacon and Ben Straub, Pro Git (Second Edition) builds on the hugely successful first edition, and is now fully updated for Git version 2.0, as well as including an indispensable chapter on GitHub. It's the best book for all your Git needs.},
  isbn = {978-1-4842-0077-3},
  keywords = {Distributed processing,Electronic data processing,Git (Computer file)}
}

@article{Chib.Greenberg_1994_BayesInferenceRegression,
  title = {Bayes Inference in Regression Models with {{ARMA}} (p, q) Errors},
  author = {Chib, Siddhartha and Greenberg, Edward},
  year = {1994},
  month = sep,
  journal = {Journal of Econometrics},
  volume = {64},
  number = {1-2},
  pages = {183--206},
  doi = {10.1016/0304-4076(94)90063-9},
  abstract = {We develop practical and exact methods of analyzing ARMA (p, q) regression error models in a Bayesian framework by using the Gibbs sampling and Metropolis-Hastings algorithms, and we prove that the kernel of the proposed Markov chain sampler converges to the true density. The procedures can be applied to pure ARMA time series models and to determine features of the likelihood function by choosing appropriate diffuse priors. Our results are unconditional on the initial observations. We also show how the algorithm can be further simplified for the important special cases of stationary AR(p) and invertible MA(q) models. Recursive transformations developed in this paper to diagonalized the covariance matrix of the errors should prove useful in frequentist estimation. Examples with simulated and actual economic data are presented.}
}

@article{Christiano.Eichenbaum.Trabandt_2018_DSGEModels,
  title = {On {{DSGE Models}}},
  author = {Christiano, Lawrence J. and Eichenbaum, Martin S. and Trabandt, Mathias},
  year = {2018},
  journal = {Journal of Economic Perspectives},
  volume = {32},
  number = {3},
  pages = {113--140},
  doi = {10.1257/jep.32.3.113},
  abstract = {The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. We review the state of mainstream DSGE models before the financial crisis and the Great Recession. We then describe how DSGE models are estimated and evaluated. We address the question of why DSGE modelers---like most other economists and policymakers---failed to predict the financial crisis and the Great Recession, and how DSGE modelers responded to the financial crisis and its aftermath. We discuss how current DSGE models are actually used by policymakers. We then provide a brief response to some criticisms of DSGE models, with special emphasis on criticism by Joseph Stiglitz, and offer some concluding remarks.}
}

@article{Crack.Ledoit_2010_CentralLimitTheorems,
  title = {Central {{Limit Theorems When Data Are Dependent}}: {{Addressing}} the {{Pedagogical Gaps}}},
  author = {Crack, Timothy Falcon and Ledoit, Oliver},
  year = {2010},
  journal = {Journal of Financial Education},
  volume = {36},
  number = {1/2},
  eprint = {41948634},
  eprinttype = {jstor},
  pages = {38--60},
  issn = {00933961, 2332421X},
  url = {http://www.jstor.org/stable/41948634},
  abstract = {Although dependence in financial data is pervasive, standard doctoral-level econometrics texts do not make clear that the common central limit theorems (CLTs) contained therein fail when applied to dependent data. More advanced books that are clear in their CLT assumptions do not contain any worked examples of CLTs that apply to dependent data. We address these pedagogical gaps by discussing dependence in financial data and dependence assumptions in CLTs and by giving a worked example of the application of a CLT for dependent data to the case of the derivation of the asymptotic distribution of the sample variance of a Gaussian AR(1). We also provide code and the results for a Monte-Carlo simulation used to check the results of the derivation.}
}

@article{Dixit.Stiglitz_1977_MonopolisticCompetitionOptimum,
  title = {Monopolistic {{Competition}} and {{Optimum Product Diversity}}},
  author = {Dixit, Avinash K. and Stiglitz, Joseph E.},
  year = {1977},
  journal = {The American Economic Review},
  volume = {67},
  number = {3},
  pages = {297--308},
  doi = {https://www.jstor.org/stable/2117513}
}

@article{Fagiolo.Napoletano.Roventini_2008_AreOutputGrowthrate,
  title = {Are Output Growth-Rate Distributions Fat-Tailed? {{Some}} Evidence from {{OECD}} Countries},
  author = {Fagiolo, Giorgio and Napoletano, Mauro and Roventini, Andrea},
  year = {2008},
  journal = {Journal of Applied Econometrics},
  volume = {23},
  number = {5},
  pages = {639--669},
  doi = {10.1002/jae.1003},
  abstract = {This work explores some distributional properties of aggregate output growth-rate time series. We show that, in the majority of OECD countries, output growth-rate distributions are well approximated by symmetric exponential power densities with tails much fatter than those of a Gaussian (but with finite moments of any order). Fat tails robustly emerge in output growth rates independently of: (i) the way we measure aggregate output; (ii) the family of densities employed in the estimation; (iii) the length of time lags used to compute growth rates. We also show that fat tails still characterize output growth-rate distributions even after one washes away outliers, autocorrelation and heteroscedasticity.}
}

@incollection{Fernandez-Villaverde.Rubio-Ramirez_2010_StructuralVectorAutoregressions,
  title = {Structural Vector Autoregressions},
  booktitle = {Macroeconometrics and {{Time Series Analysis}}},
  author = {{Fern{\'a}ndez-Villaverde}, Jes{\'u}s and {Rubio-Ram{\'i}rez}, Juan F.},
  editor = {Durlauf, Steven N. and Blume, Lawrence E.},
  year = {2010},
  pages = {303--307},
  publisher = {Palgrave Macmillan UK},
  address = {London},
  url = {https://doi.org/10.1057/9780230280830_33},
  abstract = {Structural vector autoregressions (SVARs) are a multivariate, linear representation of a vector of observables on its own lags and (possibly) other variables as a trend or a constant. SVARs make explicit identifying assumptions to isolate estimates of policy and/or private agents' behaviour and its effects on the economy while keeping the model free of the many additional restrictive assumptions needed to give every parameter a behavioural interpretation. Introduced by Sims (1980), SVARs have been used to document the effects of money on output (Sims and Zha, 2006a), the relative importance of supply and demand shocks on business cycles (Blanchard and Quah, 1989), the effects of fiscal policy (Blanchard and Perotti, 2002), or the relation between technology shocks and worked hours (Gal{\i}{\' }, 1999), among many other applications.},
  isbn = {978-0-230-23885-5 978-0-230-28083-0}
}

@incollection{Fernandez-Villaverde.Rubio-Ramirez.Schorfheide_2016_SolutionEstimationMethods,
  title = {Solution and {{Estimation Methods}} for {{DSGE Models}}},
  booktitle = {Handbook of {{Macroeconomics}}},
  author = {{Fern{\'a}ndez-Villaverde}, Jes{\'u}s and {Rubio-Ram{\'i}rez}, Juan F. and Schorfheide, Frank},
  editor = {Taylor, John B. and Uhlig, Harald},
  year = {2016},
  volume = {A},
  pages = {527--724},
  publisher = {Elsevier North-Holland},
  url = {https://doi.org/10.1016/bs.hesmac.2016.03.006},
  abstract = {This chapter provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field.},
  isbn = {978-0-444-59469-3}
}

@article{Gali_1992_HowWellDoes,
  title = {How {{Well Does The IS-LM Model Fit Postwar U}}. {{S}}. {{Data}}?},
  author = {Gali, J.},
  year = {1992},
  month = may,
  journal = {The Quarterly Journal of Economics},
  volume = {107},
  number = {2},
  pages = {709--738},
  doi = {10.2307/2118487},
  abstract = {Postwar U. S. time series for money, interest rates, prices, and GNP are characterized by a multivariate process driven by four exogenous disturbances. Those disturbances are identified so that they can be interpreted as the four main sources of fluctuations found in the IS-LM-Phillips curve model: money supply, money demand, IS, and aggregate supply shocks. The dynamic properties of the estimated model are analyzed and shown to match most of the stylized predictions of the model. The estimated decomposition is also used to measure the relative importance of each shock, to interpret some macroeconomic episodes, and to study sources of permanent shocks to nominal variables.}
}

@book{Gali_2015_MonetaryPolicyInflation,
  title = {Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New {{Keynesian}} Framework and Its Applications},
  author = {Gal{\'i}, Jordi},
  year = {2015},
  edition = {Second edition},
  publisher = {Princeton University Press},
  address = {Princeton ; Oxford},
  abstract = {This revised second edition of Monetary Policy, Inflation, and the Business Cycle provides a rigorous graduate-level introduction to the New Keynesian framework and its applications to monetary policy. The New Keynesian framework is the workhorse for the analysis of monetary policy and its implications for inflation, economic fluctuations, and welfare. A backbone of the new generation of medium-scale models under development at major central banks and international policy institutions, the framework provides the theoretical underpinnings for the price stability--oriented strategies adopted by most central banks in the industrialized world. Using a canonical version of the New Keynesian model as a reference, Jordi Gal{\'i} explores various issues pertaining to monetary policy's design, including optimal monetary policy and the desirability of simple policy rules. He analyzes several extensions of the baseline model, allowing for cost-push shocks, nominal wage rigidities, and open economy factors. In each case, the effects on monetary policy are addressed, with emphasis on the desirability of inflation-targeting policies. New material includes the zero lower bound on nominal interest rates and an analysis of unemployment's significance for monetary policy.},
  isbn = {978-0-691-16478-6},
  keywords = {BUSINESS & ECONOMICS / Economics / Theory,BUSINESS & ECONOMICS / Finance,BUSINESS & ECONOMICS / Money & Monetary Policy,Business cycles,Inflation (Finance),Keynesian economics,Monetary policy}
}

@book{Greenberg_2008_IntroductionBayesianEconometrics,
  title = {Introduction to {{Bayesian}} Econometrics},
  author = {Greenberg, Edward},
  year = {2008},
  publisher = {Cambridge University Press},
  address = {Cambridge ; New York},
  abstract = {This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language.},
  isbn = {978-0-521-85871-7},
  keywords = {Bayesian statistical decision theory,Econometrics}
}

@inbook{Guerron-Quintana.Nason_2013_BayesianEstimationDSGE,
  title = {Bayesian Estimation of {{DSGE}} Models},
  booktitle = {Handbook of {{Research Methods}} and {{Applications}} in {{Empirical Macroeconomics}}},
  author = {{Guerr{\'o}n-Quintana}, Pablo A. and Nason, James M.},
  year = {2013},
  pages = {486--512},
  publisher = {Edward Elgar Publishing},
  url = {https://doi.org/10.4337/9780857931023.00029},
  collaborator = {Hashimzade, Nigar and Thornton, Michael},
  isbn = {978-0-85793-102-3}
}

@book{Heijdra_2017_FoundationsModernMacroeconomics,
  title = {Foundations of Modern Macroeconomics},
  author = {Heijdra, Ben J.},
  year = {2017},
  edition = {Third edition},
  publisher = {Oxford university Press},
  address = {Oxford},
  abstract = {The study of macroeconomics can seem a daunting project. The field is complex and sometimes poorly defined and there are a variety of competing approaches. It is easy for the senior bachelor and starting master student to get lost in the forest of macroeconomics and the mathematics it uses extensively. Foundations of Modern Macroeconomics is a guide book for the interested and ambitious student. Non-partisan in its approach, it deals with all the major topics, summarising the important approaches and providing the reader with a coherent angle on all aspects of macroeconomic thought. Each chapter deals with a separate area of macroeconomics, and each contains a summary section of key points and a further reading list. Using nothing more than undergraduate mathematical skills, it takes the student from basic IS-LM style macro models to the state of the art literature on Dynamic Stochastic General Equilibrium, explaining the mathematical tricks used where they are first introduced. Fully updated and substantially revised, this third edition of Foundations of Modern Macroeconomics now includes brand new chapters covering highly topical subjects such as dynamic programming, competitive risk sharing equilibria and the New Keynesian DSGE approach. --},
  isbn = {978-0-19-878413-5},
  keywords = {Macroeconomics,Problems and exercises,Problems exercises etc}
}

@book{Herbst.Schorfheide_2016_BayesianEstimationDSGE,
  title = {Bayesian {{Estimation}} of {{DSGE Models}}},
  author = {Herbst, Edward and Schorfheide, Frank},
  year = {2016},
  series = {The {{Econometric}} and {{Tinbergen Institutes Lectures}}},
  publisher = {Princeton University Press},
  abstract = {Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations.},
  isbn = {978-0-691-16108-2}
}

@article{Ireland_2004_TechnologyShocksNew,
  title = {Technology {{Shocks}} in the {{New Keynesian Model}}},
  author = {Ireland, Peter N.},
  year = {2004},
  month = nov,
  journal = {Review of Economics and Statistics},
  volume = {86},
  number = {4},
  pages = {923--936},
  doi = {10.1162/0034653043125158},
  abstract = {In the New Keynesian model, preference, cost-push, and mon- etary shocks all compete with the real-business-cycle model's technology shock in driving aggregate fluctuations. A version of this model, estimated via maximum likelihood, points to these other shocks as being more important for explaining the behavior of output, inflation, and interest rates in the postwar U.S. data. These results weaken the links between the current generation of New Keynesian models and the real-business-cycle models from which they were originally derived. They also suggest that Federal Reserve officials have often faced difficult trade-offs in conduct- ing monetary policy.}
}

@incollection{Kilian_2013_StructuralVectorAutoregressions,
  title = {Structural Vector Autoregressions},
  booktitle = {Handbook of {{Research Methods}} and {{Applications}} in {{Empirical Macroeconomics}}},
  author = {Kilian, Lutz},
  editor = {Hashimzade, Steven N. and Thornton, Michael},
  year = {2013},
  pages = {515--554},
  publisher = {Edward Elgar Publishing},
  url = {https://doi.org/10.4337/9780857931023.00031}
}

@book{Kilian.Lutkepohl_2017_StructuralVectorAutoregressive,
  title = {Structural {{Vector Autoregressive Analysis}}},
  author = {Kilian, Lutz and L{\"u}tkepohl, Helmut},
  year = {2017},
  series = {Themes in {{Modern Econometrics}}},
  publisher = {Cambridge University Press},
  address = {Cambridge},
  url = {https://doi.org/10.1017/9781108164818},
  abstract = {Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This book not only reviews the many alternative structural VAR approaches discussed in the literature, but also highlights their pros and cons in practice. It provides guidance to empirical researchers as to the most appropriate modeling choices, methods of estimating, and evaluating structural VAR models. The book traces the evolution of the structural VAR methodology and contrasts it with other common methodologies, including dynamic stochastic general equilibrium (DSGE) models. It is intended as a bridge between the often quite technical econometric literature on structural VAR modeling and the needs of empirical researchers. The focus is not on providing the most rigorous theoretical arguments, but on enhancing the reader's understanding of the methods in question and their assumptions. Empirical examples are provided for illustration.},
  isbn = {978-1-107-19657-5}
}

@book{Koop_2003_BayesianEconometrics,
  title = {Bayesian Econometrics},
  author = {Koop, Gary},
  year = {2003},
  publisher = {J. Wiley},
  address = {Chichester ; Hoboken, N.J},
  abstract = {Researchers in many fields are increasingly finding the Bayesian approach to statistics to be an attractive one. This book introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require that readers have previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. Topics covered in the book include the regression model (and variants applicable for use with panel data), time series models, models for qualitative or censored data, nonparametric methods and Bayesian model averaging. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics.},
  isbn = {978-0-470-84567-7},
  keywords = {Bayesian statistical decision theory,Econometric models}
}

@book{Koop.Korobilis_2010_BayesianMultivariateTime,
  title = {Bayesian Multivariate Time Series Methods for Emprirical Macroeconomics},
  editor = {Koop, Gary and Korobilis, Dimitris},
  year = {2010},
  series = {Foundations and {{Trends}} in {{Econometrics}}},
  number = {3.2009,4},
  publisher = {now},
  address = {Boston},
  abstract = {Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus, over-parameterization problems may arise. Bayesian methods have become increasingly popular as a way of overcoming these problems. In this monograph, we discuss VARs, factor augmented VARs and time-varying parameter extensions and show how Bayesian inference proceeds. Apart from the simplest of VARs, Bayesian inference requires the use of Markov chain Monte Carlo methods developed for state space models and we describe these algorithms. The focus is on the empirical macroeconomist and we offer advice on how to use these models and methods in practice and include empirical illustrations. A website provides Matlab code for carrying out Bayesian inference in these models.},
  isbn = {978-1-60198-362-6}
}

@article{Lucas_1976_EconometricPolicyEvaluation,
  title = {Econometric Policy Evaluation: {{A}} Critique},
  author = {Lucas, Robert E.},
  year = {1976},
  month = jan,
  journal = {Carnegie-Rochester Conference Series on Public Policy},
  volume = {1},
  pages = {19--46},
  doi = {10.1016/S0167-2231(76)80003-6}
}

@incollection{Lutkepohl_2004_UnivariateTimeSeries,
  title = {Univariate {{Time Series Analysis}}},
  booktitle = {Applied {{Time Series Econometrics}}},
  author = {L{\"u}tkepohl, Helmut},
  editor = {L{\"u}tkepohl, Helmut and Kr{\"a}tzig, Markus},
  year = {2004},
  edition = {1},
  pages = {8--85},
  publisher = {Cambridge University Press},
  url = {https://doi.org/10.1017/CBO9780511606885.003},
  isbn = {978-0-521-83919-8 978-0-521-54787-1 978-0-511-60688-5}
}

@book{Lutkepohl_2005_NewIntroductionMultiple,
  title = {New Introduction to Multiple Time Series Analysis},
  author = {L{\"u}tkepohl, Helmut},
  year = {2005},
  publisher = {Springer},
  address = {Berlin},
  abstract = {This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated,vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.},
  isbn = {978-3-540-40172-8},
  keywords = {Time-series analysis}
}

@book{McCandless_2008_ABCsRBCsIntroduction,
  title = {The {{ABCs}} of {{RBCs}}: An Introduction to Dynamic Macroeconomic Models},
  author = {McCandless, George T.},
  year = {2008},
  publisher = {Harvard University Pres},
  address = {Cambridge, MA},
  abstract = {The ABCs of RBCs is the first book to provide a basic introduction to Real Business Cycle (RBC) and New-Keynesian models. These models argue that random shocks, new inventions, droughts, and wars, in the case of pure RBC models, and monetary and fiscal policy and international investor risk aversion, in more open interpretations can trigger booms and recessions and can account for much of observed output volatility. George McCandless works through a sequence of these Real Business Cycle and New-Keynesian dynamic stochastic general equilibrium models in fine detail, showing how to solve them, and how to add important extensions to the basic model, such as money, price and wage rigidities, financial markets, and an open economy. The impulse response functions of each new model show how the added feature changes the dynamics. The ABCs of RBCs is designed to teach the economic practitioner or student how to build simple RBC models. Matlab code for solving many of the models is provided, and careful readers should be able to construct, solve, and use their own models. In the tradition of the freshwater economic schools of Chicago and Minnesota, McCandless enhances the methods and sophistication of current macroeconomic modeling.},
  isbn = {978-0-674-02814-2},
  keywords = {Business cycles,Econometric models,Macroeconomics}
}

@book{Miranda.Fackler_2002_AppliedComputationalEconomics,
  title = {Applied Computational Economics and Finance},
  author = {Miranda, Mario Javier and Fackler, Paul L.},
  year = {2002},
  publisher = {MIT},
  address = {Cambridge, Mass. London},
  abstract = {This book presents a variety of computational methods used to solve dynamic problems in economics and finance. It emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses. The examples are drawn from a wide range of subspecialties of economics and finance, with particular emphasis on problems in agricultural and resource economics, macroeconomics, and finance. The book also provides an extensive Web-site library of computer utilities and demonstration programs. The book is divided into two parts. The first part develops basic numerical methods, including linear and nonlinear equation methods, complementarity methods, finite-dimensional optimization, numerical integration and differentiation, and function approximation. The second part presents methods for solving dynamic stochastic models in economics and finance, including dynamic programming, rational expectations, and arbitrage pricing models in discrete and continuous time. The book uses MATLAB to illustrate the algorithms and includes a utilities toolbox to help readers develop their own computational economics applications.},
  isbn = {978-0-262-63309-3}
}

@book{Neusser_2016_TimeSeriesEconometrics,
  title = {Time Series Econometrics},
  author = {Neusser, Klaus},
  year = {2016},
  series = {Springer Texts in Business and Economics},
  publisher = {Springer},
  address = {Cham},
  url = {https://doi.org/10.1007/978-3-319-32862-1},
  abstract = {This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text  devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field.  Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students.},
  isbn = {978-3-319-81387-5 978-3-319-32861-4}
}

@misc{Pfeifer_2017_MATLABHandout,
  title = {{{MATLAB Handout}}},
  author = {Pfeifer, Johannes},
  year = {2017},
  url = {https://sites.google.com/site/pfeiferecon/dynare},
  abstract = {This is a short, self-contained introduction into Matlab that touches upon most issues you need to know in order to get started and be able to solve a fair amount of economic problems. Advanced topics are indicated with an asterisk in the heading.}
}

@incollection{Ploberger_2010_LawsLargeNumbers,
  title = {Law(s) of Large Numbers},
  booktitle = {Macroeconometrics and {{Time Series Analysis}}},
  author = {Ploberger, Werner},
  editor = {Durlauf, Steven N. and Blume, Lawrence E.},
  year = {2010},
  pages = {158--162},
  publisher = {Palgrave Macmillan UK},
  address = {London},
  url = {https://doi.org/10.1057/9780230280830_33},
  isbn = {978-0-230-23885-5 978-0-230-28083-0}
}

@book{Romer_2019_AdvancedMacroeconomics,
  title = {Advanced Macroeconomics},
  author = {Romer, David},
  year = {2019},
  series = {The {{McGraw-Hill}} Series in Economics},
  edition = {Fifth Edition},
  publisher = {McGraw-Hill Education},
  address = {Dubuque},
  abstract = {The fifth edition of Romer's Advanced Macroeconomics continues its tradition as the standard text and the starting point for graduate macroeconomics courses and helps lay the groundwork for students to begin doing research in macroeconomics and monetary economics. Romer presents the major theories concerning the central questions of macroeconomics. The theoretical analysis is supplemented by examples of relevant empirical work, illustrating the ways that theories can be applied and tested. In areas ranging from economic growth and short-run fluctuations to the natural rate of unemployment and monetary policy, formal models are used to present and analyze key ideas and issues. The book has been extensively revised to incorporate important new topics and new research, eliminate inessential material, and further improve the presentation.},
  isbn = {978-1-260-18521-8},
  keywords = {Macroeconomics}
}

@article{Rubio-Ramirez.Waggoner.Zha_2010_StructuralVectorAutoregressions,
  title = {Structural {{Vector Autoregressions}}: {{Theory}} of {{Identification}} and {{Algorithms}} for {{Inference}}},
  author = {{Rubio-Ram{\'i}rez}, Juan F. and Waggoner, Daniel F. and Zha, Tao},
  year = {2010},
  month = apr,
  journal = {Review of Economic Studies},
  volume = {77},
  number = {2},
  pages = {665--696},
  doi = {10.1111/j.1467-937X.2009.00578.x},
  abstract = {Structural vector autoregressions (SVARs) are widely used for policy analysis and to provide stylized facts for dynamic stochastic general equilibrium (DSGE) models; yet no workable rank conditions to ascertain whether an SVAR is globally identified have been established. Moreover, when nonlinear identifying restrictions are used, no efficient algorithms exist for small-sample estimation and inference. This paper makes four contributions towards filling these important gaps in the literature. First, we establish general rank conditions for global identification of both identified and exactly identified models. These rank conditions are sufficient for general identification and are necessary and sufficient for exact identification. Second, we show that these conditions can be easily implemented and that they apply to a wide class of identifying restrictions, including linear and certain nonlinear restrictions. Third, we show that the rank condition for exactly identified models amounts to a straightforward counting exercise. Fourth, we develop efficient algorithms for small-sample estimation and inference, especially for SVARs with nonlinear restrictions.}
}

@incollection{Schorfheide_2010_BayesianMethodsMacroeconometrics,
  title = {Bayesian Methods in Macroeconometrics},
  booktitle = {Macroeconometrics and {{Time Series Analysis}}},
  author = {Schorfheide, Frank},
  editor = {Durlauf, Steven N. and Blume, Lawrence E.},
  year = {2010},
  pages = {28--34},
  publisher = {Palgrave Macmillan UK},
  address = {London},
  url = {https://doi.org/10.1057/9780230280830_3},
  isbn = {978-0-230-23885-5 978-0-230-28083-0}
}

@article{Sims_1980_MacroeconomicsReality,
  title = {Macroeconomics and {{Reality}}},
  author = {Sims, Christopher A.},
  year = {1980},
  month = jan,
  journal = {Econometrica},
  volume = {48},
  number = {1},
  pages = {1},
  doi = {10.2307/1912017},
  abstract = {Existing strategies for econometric analysis related to macroeconomics are subject to number of serious objections, some recently formulated, some old. These objections summarized in this paper, and it is argued that taken together they make it unlikely th macroeconomic models are in fact over identified, as the existing statistical theory usua assumes. The implications of this conclusion are explored, and an example of econometr work in a non-standard style, taking account of the objections to the standard style presented.}
}

@article{Smets.Wouters_2007_ShocksFrictionsUS,
  title = {Shocks and {{Frictions}} in {{US Business Cycles}}: {{A Bayesian DSGE Approach}}},
  author = {Smets, Frank and Wouters, Rafael},
  year = {2007},
  journal = {American Economic Review},
  volume = {97},
  number = {3},
  pages = {586--606},
  doi = {10.1257/aer.97.3.586},
  abstract = {Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macroeconomic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model, we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the "Great Moderation"?}
}

@book{Torres_2013_IntroductionDynamicMacroeconomic,
  title = {Introduction to Dynamic Macroeconomic General Equilibrium Models},
  author = {Torres, Jos{\'e} L.},
  year = {2013},
  publisher = {Vernon Press},
  address = {Malaga, Spain},
  abstract = {This book offers an introductory step-by-step course in Dynamic Stochastic General Equilibrium (DSGE) modelling. Modern macroeconomic analysis is increasingly concerned with the construction, calibration and/or estimation and simulation of DSGE models. The book is intended for graduate students as an introductory course to DSGE modelling and for those economists who would like a hands-on approach to learning the basics of modern dynamic macroeconomic modelling. The book starts with the simplest canonical neoclassical DSGE model and then gradually extends the basic framework incorporating a variety of additional features, such as consumption habit formation, investment adjustment cost, investment-specific technological change, taxes, public capital, household production, non-ricardian agents, monopolistic competition, etc. The book includes Dynare codes for the models developed that can be downloaded from the book's homepage.},
  isbn = {978-1-62273-007-0},
  keywords = {Equilibrium (Economics),Macroeconomics,Mathematical models}
}

@book{Uribe.Schmitt-Grohe_2017_OpenEconomyMacroeconomics,
  title = {Open Economy Macroeconomics},
  author = {Uribe, Martin and {Schmitt-Grohe}, Stephanie},
  year = {2017},
  publisher = {Princeton University Press},
  address = {Princeton, NJ},
  abstract = {Combining theoretical models and data in ways unimaginable just a few years ago, open economy macroeconomics has experienced enormous growth over the past several decades. This rigorous and self-contained textbook brings graduate students, scholars, and policymakers to the research frontier and provides the tools and context necessary for new research and policy proposals. Mart{\'i}n Uribe and Stephanie Schmitt-Groh{\'e} factor in the discipline's latest developments, including major theoretical advances in incorporating financial and nominal frictions into microfounded dynamic models of the open economy, the availability of macro- and microdata for emerging and developed countries, and a revolution in the tools available to simulate and estimate dynamic stochastic models. The authors begin with a canonical general equilibrium model of an open economy and then build levels of complexity through the coverage of important topics such as international business-cycle analysis, financial frictions as drivers and transmitters of business cycles and global crises, sovereign default, pecuniary externalities, involuntary unemployment, optimal macroprudential policy, and the role of nominal rigidities in shaping optimal exchange-rate policy. Based on courses taught at several universities, Open Economy Macroeconomics is an essential resource for students, researchers, and practitioners. Detailed exploration of international business-cycle analysis Coverage of financial frictions as drivers and transmitters of business cycles and global crises Extensive investigation of nominal rigidities and their role in shaping optimal exchange-rate policy Other topics include fixed exchange-rate regimes, involuntary unemployment, optimal macroprudential policy, and sovereign default and debt sustainability Chapters include exercises and replication codes.},
  isbn = {978-0-691-15877-8},
  keywords = {BUSINESS & ECONOMICS / Economics / Macroeconomics,BUSINESS & ECONOMICS / Economics / Theory,BUSINESS & ECONOMICS / Finance,BUSINESS & ECONOMICS / Reference,Economic policy,Macroeconomics,Political planning,POLITICAL SCIENCE / Public Policy / Economic Policy}
}

@book{Walsh_2017_MonetaryTheoryPolicy,
  title = {Monetary Theory and Policy},
  author = {Walsh, Carl E.},
  year = {2017},
  edition = {Fourth edition},
  publisher = {MIT Press},
  address = {London, England ; Cambridge, Massachusetts},
  abstract = {This textbook presents a comprehensive treatment of the most important topics in monetary economics, focusing on the primary models monetary economists have employed to address topics in theory and policy. Striking a balance of insight, accessibility, and rigor, the book covers the basic theoretical approaches, shows how to do simulation work with the models, and discusses the full range of frictions that economists have studied to understand the impacts of monetary policy. For the fourth edition, every chapter has been revised to improve the exposition and to reflect recent research. The new edition offers an entirely new chapter on the effective lower bound on nominal interest rates, forward guidance policies, and quantitative and credit easing policies. Material on the basic new Keynesian model has been reorganized into a single chapter to provide a comprehensive analysis of the model and its policy implications. In addition, the chapter on the open economy now reflects the dominance of the new Keynesian approach. Other new material includes discussions of price adjustment, labor market frictions and unemployment, and moral hazard frictions among financial intermediaries. References and end-of-chapter problems allow readers to extend their knowledge of the topics covered. Monetary Theory and Policy continues to be the most comprehensive and up-to-date treatment of monetary economics, not only the leading text in the field but also the standard reference for academics and central bank researchers.},
  isbn = {978-0-262-03581-1},
  keywords = {Monetary policy,Money}
}

@book{White_2001_AsymptoticTheoryEconometricians,
  title = {Asymptotic Theory for Econometricians},
  author = {White, Halbert},
  year = {2001},
  edition = {Rev. ed},
  publisher = {Academic Press},
  address = {San Diego},
  abstract = {The amount of financial data created every day by world stock markets, world governments, financial institutions, and other sources, is increasing at an enormous rate. Economists and financial analysts need tools to manage these large sets of data in a timely and accurate way. Classical linear models of economics have failed to deal with such large amounts of data, and asymptotic theory is the tool that economists have come to rely on for this type of data management. Large sample theory and the fundamental tools of asymptotic theory converge in this thoroughly revised edition of Asymptotic Theory for Econometricians. New material on functional central limit theory and its applications, material on cointegration, and many small points make this Revised Edition a comprehensive and unified treatment of large sample theory. The scope of the book remains the same as that of the First Edition, with sufficient material to fill a full year's course work. This edition also contains updated material on asymptotically efficient instrumental variables estimation, efficient estimation with estimated error covariance matrices, and efficient IV estimation. Exercise solutions have also been updated and expanded. Asymptotic Theory for Econometricians is intended both as a reference for practicing econometricians and financial analysts and as a textbook for graduate students taking courses in econometrics beyond the introductory level. It assumes that the reader is familiar with the basic concepts of probability and statistics as well as with calculus and linear algebra, and that the reader also has a good understanding of the classical linear model.},
  isbn = {978-0-12-746652-1},
  keywords = {Asymptotic theory,Econometrics}
}

@article{Wolf_2022_WhatCanWe,
  title = {What {{Can We Learn}} from {{Sign-Restricted VARs}}?},
  author = {Wolf, Christian K.},
  year = {2022},
  month = may,
  journal = {AEA Papers and Proceedings},
  volume = {112},
  pages = {471--475},
  doi = {10.1257/pandp.20221045},
  abstract = {I use a simple business cycle model to illustrate the workings and limitations of sign restrictions in structural vector autoregressions. Three lessons emerge. First, such sign-based identification is vulnerable to ``shock masquerading'': linear combinations of other shocks may be misidentified as the shock of interest. Second, since the popular Haar prior automatically overweights more volatile shocks, the implied posterior is decisively shaped by relative shock volatilities--a feature of shocks that has nothing to do with their dynamic causal effects. Third, sign restrictions on structural elasticities--rather than just the usual restrictions on impulse responses--can be highly informative.}
}

@book{Woodford_2003_InterestPricesFoundations,
  title = {Interest and Prices: Foundations of a Theory of Monetary Policy},
  author = {Woodford, Michael},
  year = {2003},
  publisher = {Princeton University Press},
  address = {Princeton, N.J. ; Woodstock, Oxfordshire [England]},
  abstract = {With the collapse of the Bretton Woods system, any pretense of a connection of the world's currencies to any real commodity has been abandoned. Yet since the 1980s, most central banks have abandoned money-growth targets as practical guidelines for monetary policy as well. How then can pure ``fiat'' currencies be managed so as to create confidence in the stability of national units of account? Interest and Prices seeks to provide theoretical foundations for a rule-based approach to monetary policy suitable for a world of instant communications and ever more efficient financial markets. In such a world, effective monetary policy requires that central banks construct a conscious and articulate account of what they are doing. Michael Woodford reexamines the foundations of monetary economics, and shows how interest-rate policy can be used to achieve an inflation target in the absence of either commodity backing or control of a monetary aggregate. The book further shows how the tools of modern macroeconomic theory can be used to design an optimal inflation-targeting regime\,---\,one that balances stabilization goals with the pursuit of price stability in a way that is grounded in an explicit welfare analysis, and that takes account of the ``New Classical'' critique of traditional policy evaluation exercises. It thus argues that rule-based policymaking need not mean adherence to a rigid framework unrelated to stabilization objectives for the sake of credibility, while at the same time showing the advantages of rule-based over purely discretionary policymaking.},
  isbn = {978-0-691-01049-6},
  keywords = {Interet (Economie),Monetary policy,Politique economique,Politique monetaire,Prix}
}

@article{Yun_1996_NominalPriceRigidity,
  title = {Nominal Price Rigidity, Money Supply Endogeneity, and Business Cycles},
  author = {Yun, Tack},
  year = {1996},
  month = apr,
  journal = {Journal of Monetary Economics},
  volume = {37},
  number = {2},
  pages = {345--370},
  doi = {10.1016/S0304-3932(96)90040-9},
  abstract = {This paper investigates the ability of nominal price rigidity to explain the co-movement of inflation with the cyclical component of output observed in the post-war U.S. data. A dynamic general equilibrium model is constructed with the introduction of monopolistic competition and nominal price rigidity in a standard real business cycle model, allowing for an endogenous money supply rule. It is then demonstrated that sticky price models can explain the observed associations between movements in inflation and output much better than flexible price models. This result depends little on whether money supply is assumed to be endogenous or not.}
}