Готові списки джерел за темами / Nonlinear Structural Vector AutoRegressions / Статті в журналах
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Статті в журналах з теми "Nonlinear Structural Vector AutoRegressions"

Автор: Grafiati
Опубліковано: 11 березня 2023

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1

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

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Анотація:
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 autoregression subject to periodic structural changes. The parameters of a vector autoregression are modelled as the outcome of an unobserved discrete Markov process with unknown transition probabilities. The unobserved regimes, one for each time point, together with the regime transition probabilities, are determined in addition to the vector autoregression parameters within each regime.A Bayesian Markov Chain Monte Carlo estimation procedure is developed which efficiently generates the posterior joint density of the parameters and the regimes. The complete likelihood surface is generated at the same time, enabling estimation of posterior model probabilities for use in non-nested model selection. The procedure can readily be extended to produce joint prediction densities for the variables, incorporating both parameter and model uncertainty.Results using simulated and real data are provided. A clear separation of the variance between a stable and an unstable regime was observed. Ignoring regime shifts is very likely to produce misleading volatility estimates and is unlikely to be robust to outliers. A comparison with commonly used models suggests that Regime Switching Vector Autoregressions provide a particularly good description of the observed data.
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2

IWATA, SHIGERU, and SHU WU. "MACROECONOMIC SHOCKS AND THE FOREIGN EXCHANGE RISK PREMIA." Macroeconomic Dynamics 10, no. 4 (August 23, 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 from the previous VAR analysis of the exchange rate “overshooting” behavior under exogenous monetary innovations.
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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 (July 2, 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 that a 1% increase in visitor arrivals would increase gross domestic product (GDP) per capita by 0.92%, whereas a 1% decrease in visitor arrivals would decrease GDP per capita by 0.34%. The identified breaks, 2013Q2 and 2015Q3, are positive and significant in the short run only. The causality result confirms a bidirectional association, thus mutually reinforcing the asymmetric relationship between visitor arrivals and economic growth.
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4

Stock, James H., and Mark W. Watson. "Vector Autoregressions." Journal of Economic Perspectives 15, no. 4 (November 1, 2001): 101–15. http://dx.doi.org/10.1257/jep.15.4.101.

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Анотація:
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 less useful for structural inference and policy analysis.
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5

Iwata, Shigeru, and Shu Wu. "A NOTE ON FOREIGN EXCHANGE INTERVENTIONS AT ZERO INTEREST RATES." Macroeconomic Dynamics 16, no. 5 (September 7, 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 suggest that although it might be feasible to use the exchange rate as an alternative monetary policy instrument at zero interest rates as proposed by McCallum (Inflation Targeting and the Liquidity Trap, NBER working paper 8225, 2000), the exchange rate–based Taylor rule may not be very effective in achieving the ultimate policy goals.
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6

Branch, William A., Troy Davig, and Bruce McGough. "ADAPTIVE LEARNING IN REGIME-SWITCHING MODELS." Macroeconomic Dynamics 17, no. 5 (March 6, 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 under vector autoregression learning (VAR learning), the self-fulfilling serial correlation must be learned. We show that an intuitive condition ensures convergence to a regime-switching rational expectations equilibrium. However, the stability of sunspot equilibria, when they exist, depends on whether agents adopt mean value or VAR learning: coordinating on sunspot equilibria via a VAR learning rule is not possible. To illustrate these phenomena, we develop results for an overlapping-generations model and a New Keynesian model.
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7

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

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8

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

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9

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

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10

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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Анотація:
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 structure.
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11

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

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12

Breitung, Jörg. "A convenient representation for structural vector autoregressions." Empirical Economics 26, no. 2 (May 23, 2001): 447–59. http://dx.doi.org/10.1007/s001810000065.

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13

Ghanem, Dalia, and Aaron Smith. "Causality in structural vector autoregressions: Science or sorcery?" American Journal of Agricultural Economics 104, no. 3 (October 26, 2021): 881–904. http://dx.doi.org/10.1111/ajae.12269.

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14

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

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15

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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16

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 (February 21, 2020): 36–50. http://dx.doi.org/10.1108/jes-11-2018-0388.

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Анотація:
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 uncertainty spillover on the stock markets of the GCC countries.FindingsThis study finds that an unexpected increase in the US economic policy uncertainty significantly decreases the stock market index of all the GCC countries. This study also gets this relationship symmetric, meaning that the GCC stock market indices decrease and increase by the same amount when the US economic policy uncertainty increases and decreases, respectively.Originality/valueThis study investigates the characteristics of economic policy uncertainty spillover from the biggest economy of the world to the stock markets of the GCC region, which is new to the literature. The study results provide the first evidence that a risk-sharing based financial system does not necessarily protect the stock market from US uncertainty shock. However, the abundance of local investors, risk-sharing investment activities, the absence of derivatives, etc. may be responsible for the symmetric behavior of a stock market.
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17

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

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18

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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19

Fry, Renée, and Adrian Pagan. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review." Journal of Economic Literature 49, no. 4 (December 1, 2011): 938–60. http://dx.doi.org/10.1257/jel.49.4.938.

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Анотація:
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. (JEL C32, C51, E12)
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20

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

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21

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

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22

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 (April 2010): 665–96. http://dx.doi.org/10.1111/j.1467-937x.2009.00578.x.

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23

Lütkepohl, Helmut, and Anton Velinov. "STRUCTURAL VECTOR AUTOREGRESSIONS: CHECKING IDENTIFYING LONG-RUN RESTRICTIONS VIA HETEROSKEDASTICITY." Journal of Economic Surveys 30, no. 2 (December 5, 2014): 377–92. http://dx.doi.org/10.1111/joes.12100.

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24

Del Negro, Marco, and Giorgio E. Primiceri. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum." Review of Economic Studies 82, no. 4 (June 26, 2015): 1342–45. http://dx.doi.org/10.1093/restud/rdv024.

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25

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

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26

Chevillon, Guillaume, Sophocles Mavroeidis, and Zhaoguo Zhan. "ROBUST INFERENCE IN STRUCTURAL VECTOR AUTOREGRESSIONS WITH LONG-RUN RESTRICTIONS." Econometric Theory 36, no. 1 (March 5, 2019): 86–121. http://dx.doi.org/10.1017/s0266466619000045.

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Анотація:
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 variables to make them near stationary using the IVX instrumentation method of Magdalinos and Phillips (2009). We apply our method to obtain robust confidence bands on impulse responses in two leading applications in the literature.
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27

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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28

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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29

Reale, M. "The sampling properties of conditional independence graphs for structural vector autoregressions." Biometrika 89, no. 2 (June 1, 2002): 457–61. http://dx.doi.org/10.1093/biomet/89.2.457.

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30

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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31

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 (October 2013): 426–37. http://dx.doi.org/10.1080/07350015.2013.803973.

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32

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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33

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

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34

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

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35

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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36

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

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37

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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38

Bazinas, Vassilios, and Bent Nielsen. "Causal Transmission in Reduced-Form Models." Econometrics 10, no. 2 (March 24, 2022): 14. http://dx.doi.org/10.3390/econometrics10020014.

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Анотація:
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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39

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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40

Jang, Woon Wook. "Risk aversion, uncertainty, and monetary policy: Structural vector autoregressions identified with high-frequency external instruments." Economics Letters 186 (January 2020): 108675. http://dx.doi.org/10.1016/j.econlet.2019.108675.

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41

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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Анотація:
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.
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42

Antolín-Díaz, Juan, and Juan F. Rubio-Ramírez. "Narrative Sign Restrictions for SVARs." American Economic Review 108, no. 10 (October 1, 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 the appeal of narrative methods with the popularized usage of traditional sign restrictions. (JEL C32, E52, Q35, Q43)
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43

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 (May 1, 2019): 1873–910. http://dx.doi.org/10.1257/aer.20151569.

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Анотація:
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 economic activity after a significant lag, whereas shocks to oil demand do not. (JEL C32, L71, Q35, Q43)
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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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Анотація:
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 (proxy) can be carried out by ordering the instrument first in a recursive VAR, even under noninvertibility. (iv) Linear VARs are as robust to nonlinearities as linear LPs.
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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 (November 20, 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 examine how industrial production is affected by the individual risk drivers making up the indices. Our results strongly suggest that financial-sector stress has a substantial, nonlinear influence on economic activity and that individual risk drivers affect output rather differently across stress regimes and across groups of countries.
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46

Kwizera, Mr Placide Aime. "SMOOTH TRANSITION STRUCTURAL VECTOR AUTOREGRESSIONS: APPLICATION TO THE RELATION BETWEEN INFLATION AND OUTPUT GROWTH IN RWANDA." International Journal of Business Management and Economic Review 03, no. 01 (2020): 219–34. http://dx.doi.org/10.35409/ijbmer.2020.3152.

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47

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 (January 7, 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 widening of internal trade balances.
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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 forecasting horizons, the DSGE model produces the forecast with the lowest error among all three models in question.
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49

Zervas, Andreas. "Tax Elasticities and the Macroeconomic Effects of Fiscal Policy in Greece." Special Issue on Applied Macroeconomics, Finance, and Banking 64, no. 1 (January 1, 2018): 59–98. http://dx.doi.org/10.3790/aeq.64.1.59.

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Abstract In this paper I explore the effects of fiscal policy, in particular of both spending and taxes, on the Greek GDP, in the form of multipliers of GDP to a shock on the relevant fiscal instrument. A novel feature of this paper is that I try to estimate the effects of particular spending and tax components on GDP. I use Structural Vector Autoregression models and contemporaneous restrictions to identify fiscal shocks. A methodological difference with traditional SVARs is that I try to estimate the elasticities of the different taxes to GDP using the transitory components of the relevant time series. The results indicate that the macroeconomic effects of different fiscal instruments vary a lot, but spending on average has a higher multiplier than taxes, while personal income tax and fuel tax have the worst impact on the economy. JEL classifications: C32, E62, H2 Keywords: Fiscal Policy, Government Spending, Taxes, Macroeconomics, Structural Vector Autoregressions
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50

Sedláček, Petr. "Creative Destruction and Uncertainty." Journal of the European Economic Association 18, no. 4 (August 13, 2019): 1814–43. http://dx.doi.org/10.1093/jeea/jvz047.

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Abstract Uncertainty rises in recessions. But does uncertainty cause downturns or vice versa? This paper argues that counter-cyclical uncertainty fluctuations are a by-product of technology growth. In a firm dynamics model with endogenous technology adoption, faster technology growth widens the dispersion of firm-level productivity shocks, a benchmark uncertainty measure. Moreover, faster technology growth spurs a creative destruction process, generates a temporary downturn, and renders uncertainty counter-cyclical. Estimates from structural vector autoregressions (VARs) on U.S. data confirm the model’s predictions. On average, 1/4 of the cyclical variation in uncertainty is driven by technology shocks. This fraction rises to 2/3 around the “dot-com” bubble.
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