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Artículos de revistas sobre el tema "Nonlinear Structural Vector AutoRegressions"

Autor: Grafiati

Publicado: 11 de marzo de 2023

Última modificación: 31 de julio de 2025

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1

Harris, Glen R. "Markov Chain Monte Carlo Estimation of Regime Switching Vector Autoregressions." ASTIN Bulletin 29, no. 1 (1999): 47–79. http://dx.doi.org/10.2143/ast.29.1.504606.

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AbstractFinancial time series data are typically found to possess leptokurtic frequency distributions, time varying volatilities, outliers and correlation structures inconsistent with linear generating processes, nonlinear dependence, and dependencies between series that are not stable over time. Regime Switching Vector Autoregressions are of interest because they are capable of explaining the observed features of the data, can capture a variety of interactions between series, appear intuitively reasonable, are vector processes, and are now tractable.This paper considers a vector autoregressio

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2

IWATA, SHIGERU, and SHU WU. "MACROECONOMIC SHOCKS AND THE FOREIGN EXCHANGE RISK PREMIA." Macroeconomic Dynamics 10, no. 4 (2006): 439–66. http://dx.doi.org/10.1017/s136510050606007x.

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In this paper we empirically examine the sources of the volatility of the foreign exchange risk premia. Using a nonlinear structural Vector Autoregression (VAR) model based on no-arbitrage condition to identify various macroeconomic shocks and the foreign exchange risk premia, we find that more than 80% of the volatilities of the currency risk premia can be accounted for by the standard macroeconomic shocks that drive output and inflation. By explicitly modelling the currency risk premia in the VAR system, we also offer a potential reconciliation for the seemingly contradicting observations fr

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3

Kumar, Nikeel, Ronald Ravinesh Kumar, Radika Kumar, and Peter Josef Stauvermann. "Is the tourism–growth relationship asymmetric in the Cook Islands? Evidence from NARDL cointegration and causality tests." Tourism Economics 26, no. 4 (2019): 658–81. http://dx.doi.org/10.1177/1354816619859712.

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We examine whether tourism sector development measured by visitor arrivals per capita has asymmetric growth effects in the Cook Islands using quarterly data from 2010Q1 to 2016Q3. Asymmetric cointegration, long-run elasticities, and dynamic multipliers are estimated using the nonlinear autoregressive distributed lag model developed by Shin et al. Asymmetric causality testing is done using the asymmetric vector autoregression approach with insights from Hatemi-J. We identify structural breaks using the Lee and Strazicich multiple endogenous structural break unit root test. The results indicate

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4

Iwata, Shigeru, and Shu Wu. "A NOTE ON FOREIGN EXCHANGE INTERVENTIONS AT ZERO INTEREST RATES." Macroeconomic Dynamics 16, no. 5 (2012): 802–17. http://dx.doi.org/10.1017/s1365100512000120.

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This note uses a nonlinear structural vector autoregression model to empirically investigate the effectiveness of official foreign exchange (FX) interventions in an economy when interest rates are constrained to the zero level, based on Japanese data in the 1990s. The model allows us to estimate the effects of FX interventions operating through different channels. We find that FX interventions are still capable of influencing the foreign exchange rate in a zero-interest-rate environment, even though their effects are greatly reduced by the zero lower bound on interest rates. Our results sugges

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5

Stock, James H., and Mark W. Watson. "Vector Autoregressions." Journal of Economic Perspectives 15, no. 4 (2001): 101–15. http://dx.doi.org/10.1257/jep.15.4.101.

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This paper critically reviews the use of vector autoregressions (VARs) for four tasks: data description, forecasting, structural inference, and policy analysis. The paper begins with a review of VAR analysis, highlighting the differences between reduced-form VARs, recursive VARs and structural VARs. A three variable VAR that includes the unemployment rate, price inflation and the short term interest rate is used to show how VAR methods are used for the four tasks. The paper concludes that VARs have proven to be powerful and reliable tools for data description and forecasting, but have been les

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6

Branch, William A., Troy Davig, and Bruce McGough. "ADAPTIVE LEARNING IN REGIME-SWITCHING MODELS." Macroeconomic Dynamics 17, no. 5 (2012): 998–1022. http://dx.doi.org/10.1017/s1365100511000800.

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We study adaptive learning in economic environments subject to recurring structural change. Stochastically evolving institutional and policymaking features can be described by regime-switching models with parameters that evolve according to finite state Markov processes. We demonstrate that in nonlinear models of this form, the presence of sunspot equilibria implies two natural schemes for learning the conditional means of endogenous variables: under mean value learning, agents condition on a sunspot variable that captures the self-fulfilling serial correlation in the equilibrium, whereas unde

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7

Kim, Tae Seog. "Contagion Effects of U.S. Business Cycle Regimes on the Korean Economy." Academic Society of Global Business Administration 22, no. 2 (2025): 207–36. https://doi.org/10.38115/asgba.2025.22.2.207.

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This study aims to empirically analyze the spillover effects of U.S. business cycle regime transitions on Korea’s real economy and financial markets. Using a Markov Switching Vector Autoregression (MS-VAR) model, U.S. economic regimes—expansion and recession—are identified endogenously, and the resulting regime-switching probabilities are introduced as exogenous variables into Markov Switching Regression and Structural VAR (SVAR) models. These models are used to evaluate the dynamic responses of key Korean macroeconomic indicators, including industrial production, consumer prices, unemployment

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8

Lanne, Markku, and Helmut Lütkepohl. "Structural Vector Autoregressions With Nonnormal Residuals." Journal of Business & Economic Statistics 28, no. 1 (2010): 159–68. http://dx.doi.org/10.1198/jbes.2009.06003.

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9

Zha, Tao. "Block recursion and structural vector autoregressions." Journal of Econometrics 90, no. 2 (1999): 291–316. http://dx.doi.org/10.1016/s0304-4076(98)00045-1.

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10

Lanne, Markku, Helmut Lütkepohl, and Katarzyna Maciejowska. "Structural vector autoregressions with Markov switching." Journal of Economic Dynamics and Control 34, no. 2 (2010): 121–31. http://dx.doi.org/10.1016/j.jedc.2009.08.002.

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11

Baumeister, Christiane, and James D. Hamilton. "Structural Vector Autoregressions with Imperfect Identifying Information." AEA Papers and Proceedings 112 (May 1, 2022): 466–70. http://dx.doi.org/10.1257/pandp.20221044.

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The problem of identification is often the core challenge of empirical economic research. The traditional approach to identification is to bring in additional information in the form of identifying assumptions, such as restrictions that certain magnitudes have to be zero. In this paper, we suggest that what are usually thought of as identifying assumptions should more generally be described as information that the analyst had about the economic structure before seeing the data. Such information is most naturally represented as a Bayesian prior distribution over certain features of the economic

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12

Waggoner, Daniel F., and Tao Zha. "A Gibbs sampler for structural vector autoregressions." Journal of Economic Dynamics and Control 28, no. 2 (2003): 349–66. http://dx.doi.org/10.1016/s0165-1889(02)00168-9.

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13

Breitung, Jörg. "A convenient representation for structural vector autoregressions." Empirical Economics 26, no. 2 (2001): 447–59. http://dx.doi.org/10.1007/s001810000065.

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14

Istiak, Khandokar, and Md Rafayet Alam. "US economic policy uncertainty spillover on the stock markets of the GCC countries." Journal of Economic Studies 47, no. 1 (2020): 36–50. http://dx.doi.org/10.1108/jes-11-2018-0388.

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PurposeThis study aims to investigate the nature and degree of US economic policy uncertainty spillover on the stock markets of a group of non-conventional economies like the Gulf Cooperation Council (GCC) countries, where a risk-sharing-based financial system is prominent and foreign investment, risk-free interest, derivatives, etc. are not as widespread as in the western economies.Design/methodology/approachthe monthly data of 1992–2018, linear and nonlinear structural vector autoregression (VAR) model, and an impulse response-based test to explore the nature and degree of US economic policy

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15

Banyk, Andrii, and Pavlo Mulesa. "Forecasting economic indicators with LSTM neural networks and graph-based correlation models." Management Information System and Devises, no. 184 (May 23, 2025): 22–39. https://doi.org/10.30837/0135-1710.2025.184.022.

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This paper explores the application of Long Short-Term Memory (LSTM) neural networks for forecasting Ukraine’s macroeconomic indicators, particularly in the context of structural changes caused by wartime disruptions. The limitations of traditional methods, such as Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR) models, which struggle to account for nonlinear dynamics and adapt to abrupt changes, are analyzed. The study substantiates the feasibility of using LSTM as a more flexible approach capable of capturing complex temporal dependencies and improving foreca

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16

Ghanem, Dalia, and Aaron Smith. "Causality in structural vector autoregressions: Science or sorcery?" American Journal of Agricultural Economics 104, no. 3 (2021): 881–904. http://dx.doi.org/10.1111/ajae.12269.

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17

Primiceri, Giorgio E. "Time Varying Structural Vector Autoregressions and Monetary Policy." Review of Economic Studies 72, no. 3 (2005): 821–52. http://dx.doi.org/10.1111/j.1467-937x.2005.00353.x.

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18

Lütkepohl, Helmut, and Aleksei Netšunajev. "Structural vector autoregressions with smooth transition in variances." Journal of Economic Dynamics and Control 84 (November 2017): 43–57. http://dx.doi.org/10.1016/j.jedc.2017.09.001.

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19

Smith, A. A. "Estimating nonlinear time-series models using simulated vector autoregressions." Journal of Applied Econometrics 8, S1 (1993): S63—S84. http://dx.doi.org/10.1002/jae.3950080506.

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20

Baumeister, Christiane, and James D. Hamilton. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information." Econometrica 83, no. 5 (2015): 1963–99. http://dx.doi.org/10.3982/ecta12356.

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21

Fry, Renée, and Adrian Pagan. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review." Journal of Economic Literature 49, no. 4 (2011): 938–60. http://dx.doi.org/10.1257/jel.49.4.938.

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The paper provides a review of the estimation of structural vector autoregressions with sign restrictions. It is shown how sign restrictions solve the parametric identification problem present in structural systems but leaves the model identification problem unresolved. A market and a macro model are used to illustrate these points. Suggestions have been made on how to find a unique model. These are reviewed. An analysis is provided of whether one can recover the true impulse responses and what difficulties might arise when one wishes to use the impulse responses found with sign restrictions.

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22

Canova, Fabio, and Fernando J. Pérez Forero. "Estimating overidentified, nonrecursive, time-varying coefficients structural vector autoregressions." Quantitative Economics 6, no. 2 (2015): 359–84. http://dx.doi.org/10.3982/qe305.

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23

Lanne, Markku, Mika Meitz, and Pentti Saikkonen. "Identification and estimation of non-Gaussian structural vector autoregressions." Journal of Econometrics 196, no. 2 (2017): 288–304. http://dx.doi.org/10.1016/j.jeconom.2016.06.002.

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24

RUBIO-RAMÍREZ, JUAN F., DANIEL F. WAGGONER, and TAO ZHA. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference." Review of Economic Studies 77, no. 2 (2010): 665–96. http://dx.doi.org/10.1111/j.1467-937x.2009.00578.x.

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25

Lütkepohl, Helmut, and Anton Velinov. "STRUCTURAL VECTOR AUTOREGRESSIONS: CHECKING IDENTIFYING LONG-RUN RESTRICTIONS VIA HETEROSKEDASTICITY." Journal of Economic Surveys 30, no. 2 (2014): 377–92. http://dx.doi.org/10.1111/joes.12100.

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26

Del Negro, Marco, and Giorgio E. Primiceri. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum." Review of Economic Studies 82, no. 4 (2015): 1342–45. http://dx.doi.org/10.1093/restud/rdv024.

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27

Krolzig, Hans-Martin. "General-to-Specific Model Selection Procedures for Structural Vector Autoregressions*." Oxford Bulletin of Economics and Statistics 65, s1 (2003): 769–801. http://dx.doi.org/10.1046/j.0305-9049.2003.00088.x.

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28

Chevillon, Guillaume, Sophocles Mavroeidis, and Zhaoguo Zhan. "ROBUST INFERENCE IN STRUCTURAL VECTOR AUTOREGRESSIONS WITH LONG-RUN RESTRICTIONS." Econometric Theory 36, no. 1 (2019): 86–121. http://dx.doi.org/10.1017/s0266466619000045.

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Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. We develop a method of inference that is robust to both weak identification and strong persistence. The method is based on a combination of the Anderson-Rubin test with instruments derived by filtering potentially nonstationary varia

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29

Neusser, Klaus. "A topological view on the identification of structural vector autoregressions." Economics Letters 144 (July 2016): 107–11. http://dx.doi.org/10.1016/j.econlet.2016.05.003.

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30

Lütkepohl, Helmut, and Tomasz Woźniak. "Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity." Journal of Economic Dynamics and Control 113 (April 2020): 103862. http://dx.doi.org/10.1016/j.jedc.2020.103862.

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31

Reale, M. "The sampling properties of conditional independence graphs for structural vector autoregressions." Biometrika 89, no. 2 (2002): 457–61. http://dx.doi.org/10.1093/biomet/89.2.457.

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32

Lütkepohl, Helmut, and Aleksei Netšunajev. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models." Econometrics and Statistics 1 (January 2017): 2–18. http://dx.doi.org/10.1016/j.ecosta.2016.05.001.

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33

Escanciano, Juan Carlos, Ignacio N. Lobato, and Lin Zhu. "Automatic Specification Testing for Vector Autoregressions and Multivariate Nonlinear Time Series Models." Journal of Business & Economic Statistics 31, no. 4 (2013): 426–37. http://dx.doi.org/10.1080/07350015.2013.803973.

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34

Bruns, Stephan B., Alessio Moneta, and David I. Stern. "Estimating the economy-wide rebound effect using empirically identified structural vector autoregressions." Energy Economics 97 (May 2021): 105158. http://dx.doi.org/10.1016/j.eneco.2021.105158.

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35

Karamé, F. "Impulse–response functions in Markov-switching structural vector autoregressions: A step further." Economics Letters 106, no. 3 (2010): 162–65. http://dx.doi.org/10.1016/j.econlet.2009.11.009.

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36

Angelini, Giovanni, and Luca Fanelli. "Exogenous uncertainty and the identification of structural vector autoregressions with external instruments." Journal of Applied Econometrics 34, no. 6 (2019): 951–71. http://dx.doi.org/10.1002/jae.2736.

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37

Baumeister, Christiane, and James D. Hamilton. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions." Journal of International Money and Finance 109 (December 2020): 102250. http://dx.doi.org/10.1016/j.jimonfin.2020.102250.

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38

Herwartz, Helmut, and Helmut Lütkepohl. "Structural vector autoregressions with Markov switching: Combining conventional with statistical identification of shocks." Journal of Econometrics 183, no. 1 (2014): 104–16. http://dx.doi.org/10.1016/j.jeconom.2014.06.012.

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39

Bazinas, Vassilios, and Bent Nielsen. "Causal Transmission in Reduced-Form Models." Econometrics 10, no. 2 (2022): 14. http://dx.doi.org/10.3390/econometrics10020014.

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We propose a method to explore the causal transmission of an intervention through two endogenous variables of interest. We refer to the intervention as a catalyst variable. The method is based on the reduced-form system formed from the conditional distribution of the two endogenous variables given the catalyst. The method combines elements from instrumental variable analysis and Cholesky decomposition of structural vector autoregressions. We give conditions for uniqueness of the causal transmission.

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40

Wolf, Christian K. "What Can We Learn from Sign-Restricted VARs?" AEA Papers and Proceedings 112 (May 1, 2022): 471–75. http://dx.doi.org/10.1257/pandp.20221045.

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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 ela

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41

Antolín-Díaz, Juan, and Juan F. Rubio-Ramírez. "Narrative Sign Restrictions for SVARs." American Economic Review 108, no. 10 (2018): 2802–29. http://dx.doi.org/10.1257/aer.20161852.

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We identify structural vector autoregressions using narrative sign restrictions. Narrative sign restrictions constrain the structural shocks and/or the historical decomposition around key historical events, ensuring that they agree with the established narrative account of these episodes. Using models of the oil market and monetary policy, we show that narrative sign restrictions tend to be highly informative. Even a single narrative sign restriction may dramatically sharpen and even change the inference of SVARs originally identified via traditional sign restrictions. Our approach combines th

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42

Arias, Jonas E., Juan F. Rubio-Ramírez, and Daniel F. Waggoner. "Uniform Priors for Impulse Responses." Econometrica 93, no. 2 (2025): 695–718. https://doi.org/10.3982/ecta21101.

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There has been a call for caution regarding the standard procedure for Bayesian inference in set‐identified structural vector autoregressions on the grounds that the common practice of using a uniform prior over the set of orthogonal matrices induces a non‐uniform prior for individual impulse responses or other quantities of interest. This paper challenges this call by formally showing that when the focus is on joint inference, the uniform prior over the set of orthogonal matrices is not only sufficient but also necessary for inference based on a uniform joint prior distribution over the ident

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43

Baumeister, Christiane, and James D. Hamilton. "Reprint: Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions." Journal of International Money and Finance 114 (June 2021): 102405. http://dx.doi.org/10.1016/j.jimonfin.2021.102405.

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44

Plagborg-Møller, Mikkel, and Christian K. Wolf. "Local Projections and VARs Estimate the Same Impulse Responses." Econometrica 89, no. 2 (2021): 955–80. http://dx.doi.org/10.3982/ecta17813.

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We prove that local projections (LPs) and Vector Autoregressions (VARs) estimate the same impulse responses. This nonparametric result only requires unrestricted lag structures. We discuss several implications: (i) LP and VAR estimators are not conceptually separate procedures; instead, they are simply two dimension reduction techniques with common estimand but different finite‐sample properties. (ii) VAR‐based structural identification—including short‐run, long‐run, or sign restrictions—can equivalently be performed using LPs, and vice versa. (iii) Structural estimation with an instrument (pr

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45

Mittnik, Stefan, and Willi Semmler. "OVERLEVERAGING, FINANCIAL FRAGILITY, AND THE BANKING–MACRO LINK: THEORY AND EMPIRICAL EVIDENCE." Macroeconomic Dynamics 22, no. 1 (2017): 4–32. http://dx.doi.org/10.1017/s1365100516000080.

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We analyze the consequences of overleveraging and the potential for destabilizing effects from financial- and real-sector interactions. In a theoretical model, we demonstrate that, in the presence of regime-dependent macro feedback relations, a highly leveraged banking system can result in instabilities and downward spirals. To investigate this question empirically, we analyze time series from eight advanced economies on industrial production and the components of the country-specific financial stress indices constructed by the IMF. Employing nonlinear, multiregime vector autoregressions, we e

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46

Hachula, Michael, Michele Piffer, and Malte Rieth. "Unconventional Monetary Policy, Fiscal Side Effects, and Euro Area (Im)balances." Journal of the European Economic Association 18, no. 1 (2019): 202–31. http://dx.doi.org/10.1093/jeea/jvy052.

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Abstract We study the macroeconomic effects of unconventional monetary policy in the euro area using structural vector autoregressions, identified with external instruments. The instruments are based on the common unexpected variation in euro area sovereign yields for different maturities on policy announcement days. We first show that expansionary monetary surprises are effective at lowering public and private interest rates and increasing economic activity, consumer prices, and inflation expectations. We then document that the shocks lead to a rise in primary public expenditures and a wideni

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47

Braun, Robin, George Kapetanios, and Massimiliano Marcellino. "Nonparametric Time Varying IV-SVARs: Estimation and Inference." Finance and Economics Discussion Series, no. 2025-004 (January 2025): 1–65. https://doi.org/10.17016/feds.2025.004.

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This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even in relatively small samples as long as the underlying parameter instabilities are sufficiently smooth. We illustrate the methods

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48

Malakhovskaya, O. "DSGE-based forecasting: What should our perspective be?" Voprosy Ekonomiki, no. 12 (December 20, 2016): 129–46. http://dx.doi.org/10.32609/0042-8736-2016-12-129-146.

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The article compares the accuracy of point forecasts made with a structural dynamic stochastic general equilibrium model (DSGE) to those made with vector autoregressions estimated by OLS (VAR) and by Bayesian methods (BVAR). The main question addressed in the article is whether DSGE-based forecasts are as accurate as non-structural model ones. The comparison is made on the ground of mean squared forecast errors. The results show that the forecasting ability of the DSGE model is in general inferior to that of the BVAR. However, the difference is not critical. Moreover, for some variables and fo

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49

Baumeister, Christiane, and James D. Hamilton. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks." American Economic Review 109, no. 5 (2019): 1873–910. http://dx.doi.org/10.1257/aer.20151569.

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Traditional approaches to structural vector autoregressions (VARs) can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. We use this approach to revisit the importance of shocks to oil supply and demand. Supply disruptions turn out to be a bigger factor in historical oil price movements and inventory accumulation a smaller factor than implied by earlier estimates. Supply shocks lead to a reduction in global econo

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50

Arias, Jonas E., Juan F. Rubio-Ram\’;irez, and Daniel F. Waggoner. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications." Econometrica 86, no. 2 (2018): 685–720. http://dx.doi.org/10.3982/ecta14468.

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