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Journal articles on the topic 'Modelo DSGE'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Nunes, André Francisco Nunes de, and Marcelo Savino Portugal. "Choques no Spread de Crédito Bancário e suas Implicações para a Condução da Política Monetária no Brasil." Economia Aplicada 22, no. 2 (June 4, 2018): 81–114. http://dx.doi.org/10.11606/1980-5330/ea130574.

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O trabalho busca analisar a transmissão dos choques no spread de crédito bancário para as demais variáveis da economia e suas implicações para a condução da política monetária no Brasil. Para isso, foi estimado um modelo DSGE com fricções financeiras. A inclusão do spread de crédito não altera a resposta do modelo DSGE em relação aos choques exógenos tradicionais. Porém, quando existem choques exógenos sobre o spread de crédito, o impacto sobre o ciclo econômico foi significativo e a adoção de uma regra de política monetária ajustada pelo spread pode estabilizar a economia mais rapidamente.

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2

Bonaldi, Pietro, Juan D. Prada, Andrés González, Diego Rodríguez, and Luis E. Rojas. "Método numérico para la calibración de un modelo DSGE." Revista Desarrollo y Sociedad, no. 68 (August 2011): 119–56. http://dx.doi.org/10.13043/dys.68.4.

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3

Bezerra, Jocildo Fernandes, and Igor Ézio Maciel Silva. "Estudo sobre rigidez de preços no Brasil: uma abordagem setorial com informações agregadas." Economia Aplicada 24, no. 1 (March 1, 2020): 101–26. http://dx.doi.org/10.11606/1980-5330/ea151245.

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Utilizando os componentes não observados dos produtos real e nominal estima-se modelo DSGE semi-estrutural de rigidez de preços que varia entre os setores. Exercício Monte Carlo testa a similitude da estrutura de preços do Brasil versus Estados Unidos e um modelo de Markov analisa a estabilidade da política monetária no período de 1996:1 a 2015:4. Revelam-se distribuições de rigidez de preços e graus de complementaridade estratégica e comparam-se as persistências dos efeitos dos choques nominais no produto e na inflação entre os cenários de homogeneidade e heterogeneidade estrutural. Neste último caso há vantagem substancial para identificar as propriedades dinâmicas dessas variáveis.

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4

Bezerra, Jocildo Fernandes, Ricardo Chaves Lima, and Igor Ézio Maciel Silva. "A study on bank credit channel in Brazil: The approach of impulse response functions matching." Economia Aplicada 20, no. 2 (June 30, 2016): 245. http://dx.doi.org/10.11606/1413-8050/ea137674.

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O presente estudo aborda a questão do canal de crédito bancário no Brasil considerando a sua operacionalidade e a identiﬁcação dos efeitos da oferta e da demanda de empréstimos. Para tanto,especiﬁca-se um modelo estilizado que caracteriza o comportamento da oferta de crédito dos bancos,em um ambiente de incerteza quanto à política monetária futura. Em seguida, estimam-se funções impulso resposta empíricas, obtidas de um modelo VECM, e teóricas, originadas de um modelo DSGE. Usando-se a técnica de calibragem, obtêm-se os parâmetros que determinam a distância mínima (matching) entre os dois tipos de funções em presença de um choque de política monetária. A motivação para o uso dessa metodologia é a possibilidade de separarem os componentes de demanda e de oferta de empréstimos, uma questão recorrente em estudos desta natureza

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5

Botero García, Jesús, and José García Guzmán. "Desarrollo, reestructuración del gasto público y alianzas público-privadas." Revista de Economía Institucional 20, no. 38 (April 4, 2018): 185. http://dx.doi.org/10.18601/01245996.v20n38.08.

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Este artículo analiza los efectos de bienestar de mejoras en la inversión en educación e infraestructura con participación público-privada, en un marco fiscal de economía pequeña y abierta para la economía colombiana. Se construye un modelo DSGE donde las familias toman decisiones de capital humano y el gobierno financia megaproyectos colocando bonos de infraestructura. Los resultados sugieren mayores efectos en el ajuste de largo plazo y mejoras del bienestar social cuando el gasto público se focaliza en sectores con mayores niveles de empleo y se introducen mejoras tecnológicas y de innovación.

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6

Besarria, Cássio Nóbrega, Nelson Leitão Paes, and Marcelo Eduardo Alves da Silva. "Como o Banco Central tem reagido aos choques (bolhas) nos preços das habitações brasileiras? Uma análise por meio por meio do Modelo Dinâmico Estocástico de Equilíbrio Geral (DSGE)." Nova Economia 26, no. 2 (August 2016): 553–83. http://dx.doi.org/10.1590/0103-6351/2678.

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Resumo: Este artigo tem o propósito de analisar os efeitos do choque (bolha) nos preços dos imóveis sobre as variáveis macroeconômicas brasileiras (PIB, inflação e taxa de juro). Foram utilizados dois procedimentos metodológicos: inicialmente, serão obtidos os parâmetros estruturais do modelo Dinâmico Estocástico de Equilíbrio Geral (DSGE), por meio do Método Generalizado dos Momentos (GMM). Esses resultados dessa etapa serão utilizados na simulação dos efeitos dos choques nos preços das habitações na economia artificial. Posteriormente, será utilizado o modelo de Vetores Autorregressivos (VAR), no qual os choques serão identificados por meio de restrições de sinais, baseadas nas respostas obtidas pela calibração do modelo teórico. Os resultados mostraram queos efeitos da bolha no mercado habitacional brasileiro afetou positivamente os movimentos subsequentes no produto e na inflação; no entanto, o efeito desse choque se deu de forma transitória sobre essas variáveis, trazendo efeitos persistentes apenas sobre a taxa de juros.

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7

Divino, José Angelo, and Alexandre Kornelius. "Monetary Policy and Reserve Requirement in a DSGE Model with Financial Frictions." Economia Aplicada 19, no. 4 (December 9, 2015): 579. http://dx.doi.org/10.11606/1413-8050/ea126946.

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Este artigo modifica o modelo DSGE de Gertler & Karadi (2011), que inclui fricção sobre o balanço dos intermediários financeiros, para introduzir a exigência de recolhimentos compulsórios pela Autoridade Monetária e um choque de confiança dos depositantes no sistema financeiro. Os impactos dessas mudanças sobre os canais de transmissão da política monetária são analisados. Os resultados indicam que a presença de compulsório amplifica a transmissão da política monetária pelo canal do crédito, aumentando a alavancagem dos bancos quando há uma queda nos juros e diminuindo caso contrário. A diminuição do crédito quando os juros aumentam pode ser contrabalanceada por uma política macroprudencial de ajuste do nível de compulsório baseada em uma regra que depende de desvios do crédito no estado estacionário. O compulsório não deve, porém, substituir a taxa de juros como o instrumento de política monetária mais adequada para estabilizar a inflação.

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8

Gil León, José Mauricio, and Andrés Felipe Suárez Cante. "Implicaciones de los choques de prima de riesgo en una economía pequeña y abierta." Lecturas de Economía, no. 92 (January 24, 2020): 133–72. http://dx.doi.org/10.17533/udea.le.n0a336434.

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En este artículo se pretende identificar el canal de transmisión de los choques de prima de riesgo en las diferentes variables macroeconómicas. Por tanto, se formula un modelo DSGE para una economía pequeña y abierta, que considera el comportamiento de los hogares, las decisiones de inversión de parte de las empresas, la función de reacción del banco central y la dinámica de las diferentes variables externas, tales como la variación de los activos externos netos del país y el comercio exterior. El modelo se calibra con datos de la economía colombiana del periodo 2005-2017. En los resultados de la simulación se identifica la persistencia del shock de prima de riesgo en las variables endógenas que componen al modelo, y en especial se observan efectos sobresalientes del choque sobre el tipo de cambio, la tasa de interés y la inflación. Se concluye que el tamaño de la elasticidad de la prima de riesgo al endeudamiento externo domina la fuerza con la que inciden los shocks en la economía.

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9

Gil León, José Mauricio, and Andrés Felipe Suárez Cante. "Implicaciones de los choques de prima de riesgo en una economía pequeña y abierta." Lecturas de Economía, no. 92 (January 24, 2020): 133–72. http://dx.doi.org/10.17533/udea.le.n92a336434.

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En este artículo se pretende identificar el canal de transmisión de los choques de prima de riesgo en las diferentes variables macroeconómicas. Por tanto, se formula un modelo DSGE para una economía pequeña y abierta, que considera el comportamiento de los hogares, las decisiones de inversión de parte de las empresas, la función de reacción del banco central y la dinámica de las diferentes variables externas, tales como la variación de los activos externos netos del país y el comercio exterior. El modelo se calibra con datos de la economía colombiana del periodo 2005-2017. En los resultados de la simulación se identifica la persistencia del shock de prima de riesgo en las variables endógenas que componen al modelo, y en especial se observan efectos sobresalientes del choque sobre el tipo de cambio, la tasa de interés y la inflación. Se concluye que el tamaño de la elasticidad de la prima de riesgo al endeudamiento externo domina la fuerza con la que inciden los shocks en la economía.

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10

Silva, Igor Ézio Maciel, Nelson Leitão Paes, and Jocildo Fernandes Bezerra. "Evidências de Pass-Through Incompleto da Taxa de Juros, Crédito Direcionado e Canal de Custo da Política Monetária no Brasil." Estudos Econômicos (São Paulo) 48, no. 4 (December 2018): 559–95. http://dx.doi.org/10.1590/0101-41614841inj.

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Resumo O objetivo deste artigo é analisar a relevância do canal de custo da política monetária no Brasil, a possibilidade de pass-through incompleto da taxa de juros, e a relação entre o crédito direcionado e a política monetária. Para tanto, elaborou-se um modelo DSGE Novo-Keynesiano, no qual: o custo das firmas é diretamente influenciado pelo nível da taxa de juros cobrada pelos bancos; uma parcela dos bancos não é capaz de modificar as taxas de juros que cobram pelos empréstimos a cada período; e, uma parcela dos bancos opera linhas de crédito direcionado. O modelo foi estimado por meio do método da distância mínima (Matching). Os resultados indicam que (i) o canal de custo desempenha papel relevante na transmissão da política monetária (explicando o price-puzzle da política monetária); (ii) não há evidência de pass-through incompleto; e (iii) o crédito direcionado reduz a capacidade de a política monetária modificar as condições de crédito.

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11

Costa Junior, Celso José. "Impacto das Variações no Crédito Rural e no Investimento em Pesquisa Agrícola na Produtividade da Agricultura Brasileira Contemporânea." Revista de Economia e Sociologia Rural 56, no. 4 (October 2018): 551–64. http://dx.doi.org/10.1590/1234-56781806-94790560401.

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Resumo: A queda da produtividade das principais lavouras pode estar relacionada à redução dos investimentos em pesquisa. Ainda, o crédito rural subsidiado pode ser necessário para um aumento da produtividade, uma vez que esse é um fator essencial para a produção e para a modernização setorial. Percebendo a relevância desses dois pontos, este trabalho utiliza um modelo DSGE com economia fechada, sem governo e com dois setores. Os resultados apontam que o baixo investimento em pesquisa levou à baixa produtividade do setor agrícola até 2011, que se recuperou em 2012 e 2013. Por outro lado, o crédito rural subsidiado apresentou resultado nulo até 2009 e, em 2010 e 2011, o resultado foi positivo, mas tornou-se negativo em 2012 e 2013. Entre as duas políticas propostas, o subsídio ao crédito rural apresentou melhor resultado do que a pesquisa agrícola, pois o desempenho do produto foi superior no primeiro caso.

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12

Bekiros, Stelios D., and Alessia Paccagnini. "MACROPRUDENTIAL POLICY AND FORECASTING USING HYBRID DSGE MODELS WITH FINANCIAL FRICTIONS AND STATE SPACE MARKOV-SWITCHING TVP-VARS." Macroeconomic Dynamics 19, no. 7 (June 17, 2014): 1565–92. http://dx.doi.org/10.1017/s1365100513000953.

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We focus on the interaction of frictions both at the firm level and in the banking sector in order to examine the transmission mechanism of the shocks and to reflect on the response of the monetary policy to increases in interest rate spreads, using DSGE models with financial frictions. However, VAR models are linear and the solutions of DSGEs are often linear approximations; hence they do not consider time variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy, especially in crisis periods. A novel method for time-varying VAR models is introduced. As an extension to the standard homoskedastic TVP-VAR, we employ a Markov-switching heteroskedastic error structure. Overall, we conduct a comparative empirical analysis of the out-of-sample performance of simple and hybrid DSGE models against standard VARs, BVARs, FAVARs, and TVP-VARs, using data sets from the U.S. economy. We apply advanced Bayesian and quasi-optimal filtering techniques in estimating and forecasting the models.

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13

Christiano, Lawrence J., Martin S. Eichenbaum, and Mathias Trabandt. "On DSGE Models." Journal of Economic Perspectives 32, no. 3 (August 1, 2018): 113–40. http://dx.doi.org/10.1257/jep.32.3.113.

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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.

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14

Del Negro, Marco, and Frank Schorfheide. "Monetary Policy Analysis with Potentially Misspecified Models." American Economic Review 99, no. 4 (August 1, 2009): 1415–50. http://dx.doi.org/10.1257/aer.99.4.1415.

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Policy analysis with potentially misspecified dynamic stochastic general equilibrium (DSGE) models faces two challenges: estimation of parameters that are relevant for policy trade-offs, and treatment of the deviations from the cross-equation restrictions. Using post-1982 US data, we study the robustness of the policy prescriptions from a state-of-the-art DSGE model with respect to two approaches to model misspecification pursued in the recent literature: (i) adding shocks to the DSGE model and/or generalizing the processes followed by these shocks; and (ii) explicit modeling of deviations from cross-equation restrictions (DSGE-VAR). (JEL C51, E13, E43, E52, E58)

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15

Slanicay, Martin, Jan Capek, and Miroslav Hlousek. "Some notes on problematic issues in DSGE models." Ekonomski anali 61, no. 210 (2016): 79–99. http://dx.doi.org/10.2298/eka1610079s.

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We review some of the problematic issues in DSGE models, which are currently much discussed in the economics profession. All of these issues are concerned with the DSGE models? (in)ability to match aspects of macroeconomic variables? observed behaviour. The optimizing agents framework implies that Ricardian equivalence typically holds, which is clearly at odds with the empirical evidence. A distinguishing feature of DSGE models is the assumption that structural parameters are invariant to policy changes. We argue that not all of them can be considered independent from economic policy. It is typical for DSGE models that agents form rational expectations, which can be considered unrealistic. The typical procedure for estimating a DSGE model is to use revised data. As some empirical studies suggest, a model?s behaviour may be different if real-time data are considered. It is also usually assumed that the monetary authority uses the interest rate as a tool of monetary policy. Nowadays, nominal interest rates are close to zero in many economies and cannot be lowered further.

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16

Fernández-Villaverde, Jesús, and Pablo A. Guerrón-Quintana. "Estimating DSGE Models: Recent Advances and Future Challenges." Annual Review of Economics 13, no. 1 (August 5, 2021): 229–52. http://dx.doi.org/10.1146/annurev-economics-081020-044812.

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We review the current state of the estimation of dynamic stochastic general equilibrium (DSGE) models. After introducing a general framework for dealing with DSGE models, the state-space representation, we discuss how to evaluate moments or the likelihood function implied by such a structure. We discuss, in varying degrees of detail, recent advances in the field, such as the tempered particle filter, approximated Bayesian computation, Hamiltonian Monte Carlo, variational inference, and machine learning. These methods show much promise but have not been fully explored by the DSGE community yet. We conclude by outlining three future challenges for this line of research.

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17

Ivashchenko, S. M. "DSGE Models: Problem of Trends." Financial Journal, no. 2 (2019): 81–95. http://dx.doi.org/10.31107/2075-1990-2019-2-81-95.

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18

Kawalec, Paweł. "Perspectival representation in DSGE models." Economics and Business Review 3 (17), no. 3 (2017): 80–99. http://dx.doi.org/10.18559/ebr.2017.3.5.

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19

Blanchard, Olivier. "¿Tienen futuro los modelos DSGE?" Revista de Economía Institucional 18, no. 35 (November 29, 2016): 39. http://dx.doi.org/10.18601/01245996.v18n35.03.

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Los modelos dsge actuales tienen graves defectos, pero son sumamente mejorables y centrales para el futuro de la macroeconomía. Para mejorarlos debe volverse menos insulares, utilizado un conjunto mucho más amplio de investigaciones económicas. También deben volverse menos imperialistas y compartir el escenario con otros enfoques de la modelación.

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20

An, Sungbae, and Frank Schorfheide. "Bayesian Analysis of DSGE Models." Econometric Reviews 26, no. 2-4 (April 12, 2007): 113–72. http://dx.doi.org/10.1080/07474930701220071.

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21

Milani, Fabio, and Dale J. Poirier. "Econometric Issues in DSGE Models." Econometric Reviews 26, no. 2-4 (April 12, 2007): 201–4. http://dx.doi.org/10.1080/07474930701220204.

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22

Chib, Siddhartha, and Srikanth Ramamurthy. "DSGE Models with Student-tErrors." Econometric Reviews 33, no. 1-4 (September 25, 2013): 152–71. http://dx.doi.org/10.1080/07474938.2013.807152.

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23

Iskrev, Nikolay. "Local identification in DSGE models." Journal of Monetary Economics 57, no. 2 (March 2010): 189–202. http://dx.doi.org/10.1016/j.jmoneco.2009.12.007.

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24

Tovar, Camilo E. "DSGE Models and Central Banks." Economics: The Open-Access, Open-Assessment E-Journal 3, no. 2009-16 (2009): 1. http://dx.doi.org/10.5018/economics-ejournal.ja.2009-16.

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25

Andreasen, Martin M. "Stochastic volatility and DSGE models." Economics Letters 108, no. 1 (July 2010): 7–9. http://dx.doi.org/10.1016/j.econlet.2010.03.007.

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26

Morris, Stephen D. "VARMA representation of DSGE models." Economics Letters 138 (January 2016): 30–33. http://dx.doi.org/10.1016/j.econlet.2015.11.027.

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27

Galvão, Ana Beatriz. "Data revisions and DSGE models." Journal of Econometrics 196, no. 1 (January 2017): 215–32. http://dx.doi.org/10.1016/j.jeconom.2016.09.006.

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28

Filippeli, Thomai, and Konstantinos Theodoridis. "DSGE priors for BVAR models." Empirical Economics 48, no. 2 (March 2, 2014): 627–56. http://dx.doi.org/10.1007/s00181-013-0797-z.

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29

Fernández-Villaverde, Jesús. "The econometrics of DSGE models." SERIEs 1, no. 1-2 (February 19, 2010): 3–49. http://dx.doi.org/10.1007/s13209-009-0014-7.

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30

Costa Junior, Celso J., and Alejandro C. Garcia-Cintado. "Teaching DSGE models to undergraduates." EconomiA 19, no. 3 (September 2018): 424–44. http://dx.doi.org/10.1016/j.econ.2018.11.001.

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31

Bošnjak, Nikolina. "The Public Expenditures and Economic Growth: DSGE model for Bosnia and Herzegovina." European Journal of Economics and Business Studies 6, no. 1 (December 1, 2016): 68. http://dx.doi.org/10.26417/ejes.v6i1.p68-79.

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The DSGE (Dynamic Stochastic General Equilibrium) methodology attempts to explain the behavior of aggregate economic phenomena, such as economic growth, business cycle, and the effects of monetary and fiscal policy, using macroeconomic models derived from microeconomic foundations. DSGE models study the economy evolution (dynamics) over time. They take into consideration the fact that economy may be affected with random (stochastic) shocks. Still, they include all markets in the economy and assume that those markets balance out rapidly (general equilibrium). DSGE models have become the main tool of macroeconomic analysis, and until now, a huge number of different DSGE models have been developed. They are used for forecasting, different economic policies analysis and giving policy advices. Due to data scarcity and lack of knowledge, indevelopment and many other reasnos, until now there was no application of DSGE models to Bosnia and Herzegovina case. That is why we were motivated to calibrate a small size DSGE model for Bosnia and Herzegovina. In this research we will calibrate a small open economy DSGE model for Bosnia and Herzegovina and use its results to give some advices for economic growth of Bosnia and Herzegovina improvement. The special attention will be given to Public expenditures and TFP influence on Bosnian macroeconomic variables.

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32

Drautzburg, Thorsten. "A narrative approach to a fiscal DSGE model." Quantitative Economics 11, no. 2 (2020): 801–37. http://dx.doi.org/10.3982/qe1083.

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Structural DSGE models are used for analyzing both policy and the sources of business cycles. Conclusions based on full structural models are, however, potentially affected by misspecification. A competing method is to use partially identified SVARs based on narrative shocks. This paper asks whether both approaches agree. Specifically, I use narrative data in a DSGE‐SVAR that partially identify policy shocks in the VAR and assess the fit of the DSGE model relative to this narrative benchmark. In developing this narrative DSGE‐SVAR, I develop a tractable Bayesian approach to proxy VARs and show that such an approach is valid for models with a certain class of Taylor rules. Estimating a DSGE‐SVAR based on a standard DSGE model with fiscal rules and narrative data, I find that the DSGE model identification is at odds with the narrative information as measured by the marginal likelihood. I trace this discrepancy to differences in impulse responses, identified historical shocks and policy rules. The results indicate monetary accommodation of fiscal shocks.

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33

Kiiashko, Sergii. "Applications of DSGE Models in Central Banking: Key Issues Explored During Research Workshop of the National Bank of Ukraine." Visnyk of the National Bank of Ukraine, no. 246 (December 28, 2018): 4–9. http://dx.doi.org/10.26531/vnbu2018.246.004.

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This paper reviews a research workshop that was held by the National Bank of Ukraine (NBU) in November 2018 on the application of DSGE models in central banking. We summarize the discussion of the advantages and drawbacks of DSGE modeling and potential ways to resolve issues and improve the models. Furthermore, this paper provides guidance on using DSGE models for forecasting and policy analysis.

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34

Slanicay, Martin. "Some Notes on Historical, Theoretical, and Empirical Background of DSGE Models." Review of Economic Perspectives 14, no. 2 (June 1, 2014): 145–64. http://dx.doi.org/10.2478/revecp-2014-0008.

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Abstract In this paper I present the historical, theoretical and empirical background of DSGE models. I show that the fundament of these models lies in optimizing agents framework and argue which impulses fueled the development of DSGE models. I demonstrate the evolution of DSGE models with an accent on the role and effects of the monetary policy, using distinction between RBC models and New Keynesian models. I explain the paradigm shift from the RBC models to the New Keynesian models by pointing out the main pitfalls of the RBC models and showing how adding nominal rigidities to the otherwise standard RBC models enhances empirical properties of these models. I also discuss how nominal rigidities are modeled in New Keynesian DSGE models and what the pros and cons of different approaches are. Finally, I review the most important New Keynesian theories of nominal rigidities and some of the empirical evidence on price and wage rigidities

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35

Shvets`, Serhii. "Development of the fundamentals of DSGE-modeling." Ekonomìčna teorìâ 2021, no. 01 (April 17, 2021): 67–85. http://dx.doi.org/10.15407/etet2021.01.067.

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This article attempts to analyze the evolution of approaches that constitute grounds for macro modeling. The counteraction to destructive consequences of crises assumes practical use of model apparatus as a necessary tool for preventing destabilization. The article aims to study the progressive stages and identify unsettled issues and promising ways to assist macro models' evolution. The fundamental Marshall's and Walras's platforms supported progressive changes following the destructive Great Depression and Great Inflation in the USA in 1920-1970 and marked a new trend in macro modeling called dynamic stochastic general equilibrium (DSGE) models. The new instrument is remarkable for a radical change in macro modeling approaches, where microeconomics comes to the fore. DSGE models debuted by invoking four essential ingredients: the Phillips curve, adaptive inflation expectations, anchoring nominal prices, and an endogenous production function. The progression stages of theoretical approaches to macro modeling incorporate the classical and Keynesian schools' advanced innovations. The evolution of macro modeling has five generations of models: Keynesian, classical, RBS, new Keynesian, and new Keynesian DSGE models. Among advantages of DSGE models are "political neutrality," distinguishing the shocks into economic and political ones, and establishing the upshots of significant structural changes in the economy. The next generation of macro models is called to solve four pressing issues: establishing financial frictions, relaxing rational expectations, introducing heterogeneous agents, and underpinning the framework with more appropriate microfoundations.

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36

Chin, Kuo-Hsuan. "Fiscal Stimulus on Bayesian DSGE Models." Prague Economic Papers 28, no. 6 (December 21, 2019): 688–708. http://dx.doi.org/10.18267/j.pep.708.

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37

Farmer, Roger, and Vadim Khramov. "Solving and Estimating Indeterminate DSGE Models." IMF Working Papers 13, no. 200 (2013): i. http://dx.doi.org/10.5089/9781475589214.001.

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38

Massaro, Domenico. "Heterogeneous expectations in monetary DSGE models." Journal of Economic Dynamics and Control 37, no. 3 (March 2013): 680–92. http://dx.doi.org/10.1016/j.jedc.2012.11.001.

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39

Farmer, Roger E. A., Vadim Khramov, and Giovanni Nicolò. "Solving and estimating indeterminate DSGE models." Journal of Economic Dynamics and Control 54 (May 2015): 17–36. http://dx.doi.org/10.1016/j.jedc.2015.02.012.

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40

Koop, Gary, M. Hashem Pesaran, and Ron P. Smith. "On Identification of Bayesian DSGE Models." Journal of Business & Economic Statistics 31, no. 3 (July 2013): 300–314. http://dx.doi.org/10.1080/07350015.2013.773905.

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Bårdsen, Gunnar, and Luca Fanelli. "Frequentist Evaluation of Small DSGE Models." Journal of Business & Economic Statistics 33, no. 3 (July 3, 2015): 307–22. http://dx.doi.org/10.1080/07350015.2014.948724.

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42

Hurtado, Samuel. "DSGE models and the Lucas critique." Economic Modelling 44 (2014): S12—S19. http://dx.doi.org/10.1016/j.econmod.2013.12.002.

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43

An, Sungbae, and Frank Schorfheide. "Bayesian Analysis of DSGE Models—Rejoinder." Econometric Reviews 26, no. 2-4 (April 12, 2007): 211–19. http://dx.doi.org/10.1080/07474930701220246.

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44

Kocięcki, Andrzej, and Marcin Kolasa. "Global identification of linearized DSGE models." Quantitative Economics 9, no. 3 (2018): 1243–63. http://dx.doi.org/10.3982/qe530.

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45

황영진. "Trend/Cycle Decomposition Using DSGE Models." KDI Journal of Economic Policy 34, no. 4 (November 2012): 117–56. http://dx.doi.org/10.23895/kdijep.2012.34.4.117.

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46

Mutschler, Willi. "Higher-order statistics for DSGE models." Econometrics and Statistics 6 (April 2018): 44–56. http://dx.doi.org/10.1016/j.ecosta.2016.10.005.

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47

Ca’ Zorzi, Michele, Marcin Kolasa, and Michał Rubaszek. "Exchange rate forecasting with DSGE models." Journal of International Economics 107 (July 2017): 127–46. http://dx.doi.org/10.1016/j.jinteco.2017.03.011.

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48

Sala, Luca. "DSGE Models in the Frequency Domains." Journal of Applied Econometrics 30, no. 2 (January 23, 2014): 219–40. http://dx.doi.org/10.1002/jae.2375.

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Ferroni, Filippo, Stefano Grassi, and Miguel A. León‐Ledesma. "Selecting structural innovations in DSGE models." Journal of Applied Econometrics 34, no. 2 (November 5, 2018): 205–20. http://dx.doi.org/10.1002/jae.2664.

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50

Coenen, Günter, Christopher J. Erceg, Charles Freedman, Davide Furceri, Michael Kumhof, René Lalonde, Douglas Laxton, et al. "Effects of Fiscal Stimulus in Structural Models." American Economic Journal: Macroeconomics 4, no. 1 (January 1, 2012): 22–68. http://dx.doi.org/10.1257/mac.4.1.22.

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The paper subjects seven structural DSGE models, all used heavily by policymaking institutions, to discretionary fiscal stimulus shocks using seven different fiscal instruments, and compares the results to those of two prominent academic DSGE models. There is considerable agreement across models on both the absolute and relative sizes of different types of fiscal multipliers. The size of many multipliers is large, particularly for spending and targeted transfers. Fiscal policy is most effective if it has moderate persistence and if monetary policy is accommodative. Permanently higher spending or deficits imply significantly lower initial multipliers.(JEL E12, E13, E52, E62)

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