Error Bands for Impulse Responses

Sims, Christopher A.; Zha, Tao

doi:10.1111/1468-0262.00071

Cited by 753 publications

(491 citation statements)

References 25 publications

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“…As recommended by Sims and Zha (1999), we report the impulse response functions (solid line) together with 68% 12 probability bands (dashed lines). 16 The median value is chosen as the central measure of impulse responses.…”

Section: Stock Marketmentioning

confidence: 99%

“…[FIGURE 1 HERE] 16 According to Sims and Zha (1999), the traditional symmetric confidence intervals, reported as one or two standard errors around the point estimate of an impulse response function, may be misleading. Such error bands confound information regarding the location of the coefficient values with the information about the overall model fit.…”

Section: Stock Marketmentioning

confidence: 99%

See 1 more Smart Citation

Monetary policy and stock valuation: structural VAR identification and size effects

Kontonikas

Zekaite

2018

Quantitative Finance

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This paper examines the relationship between the US monetary policy and stock valuation using a structural VAR framework that allows for the simultaneous interaction between the federal funds rate and stock market developments based on the assumption of long-run monetary neutrality. The results confirm a strong, negative and significant monetary policy tightening effect on real stock prices. Furthermore, we provide evidence consistent with a delayed response of small stocks to monetary policy shocks relative to large stocks. § Corresponding author. Essex Business School, Finance Subject Group, University of Essex, Colchester, CO4 3SQ, UK, a.kontonikas@essex.ac.uk.‡ Adam Smith Business School, University of Glasgow, Glasgow, G12 8QQ, UK, z.zekaite.1@research.gla.ac.uk. We would also like to thank Tom Doan for providing the estimation code and helpful advice. 2 IntroductionFinance practitioners and academic researchers generally agree that monetary policy shifts have strong effects on financial markets and that there may be the role for asset prices in the monetary policy reaction function. 1 The empirical literature investigating the relationship between monetary policy and stock valuation in the United States goes back to 1970s. 2 In these early studies it is acknowledged that causality may run in both directions (Cooper, 1974;Smirlock and Yawitz, 1985). Given that stock prices, amongst other asset prices, feature in the monetary policy transmission mechanism, it is important to gain a thorough understanding of how monetary policy interacts with stock market developments (Mishkin, 2001;Bjornland and Jacobsen, 2013). This paper re-examines this relationship at both the market and portfolio level using data on portfolios formed on the basis of firm's size (market value). The empirical framework that we use allows for the short-run interaction between monetary policy and real stock price developments, assuming long-run monetary neutrality.Monetary policy may affect stock prices through its impact on the expected future net cash flows and the discount rate; the latter being equal to the sum of a risk-free interest rate and a risk premium (Smirlock and Yawitz, 1985; Kontonikas and Kostakis, 2013). According to the traditional interest rate channel, monetary policy tightening lowers the demand for loans due to higher costs of borrowing. In turn, consumption and investment spending decline leading to lower expected future net cash flows and, consequently, to lower current stock prices (Bernanke and Gertler, 1995;Ehrmann and Fratzscher, 2004). The credit channel operates via the impact of monetary policy on the external finance premium (Gertler and Gilchrist, 1994; Gertler, 1995, Kiyotaki and Moore, 1997 3 response to monetary policy tightening, the equity premium on stocks goes up either due to higher perceived riskiness of firms or increased risk aversion among investors. Consequently, current stock prices decline.Smaller firms are typically less immune to adverse conditions stemming from tighter monetary co...

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Section: Stock Marketmentioning

confidence: 99%

Section: Stock Marketmentioning

confidence: 99%

Monetary policy and stock valuation: structural VAR identification and size effects

Kontonikas

Zekaite

2018

Quantitative Finance

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“…Once more, the prior mean is assumed to be 10 When the prior in equation (2) has a symmetric structure (i.e., it differs by only a scale factor across the equations) the posterior conditional on A 0 is multivariate normal. See Kadiyala and Karlsson (1997), Sims and Zha (1999), and . 11 This does not mean we are assuming the data follow a random walk.…”

Section: Bayesian Structural Vector Autoregessionsmentioning

confidence: 99%

“…The degree of uncertainty about the dynamic (impulse) responses of medium and large scale systems inherits the serial correlation that is part of the endogenous systems of equations. Hence conventional methods for constructing error bands around them are inadequate (Sims and Zha 1999). 28 How then do we select from among competing theoretical and statistical specifications?…”

Section: Model Uncertaintymentioning

confidence: 99%

Modeling Macro-Political Dynamics

Brandt

Freeman²

2009

Polit. anal.

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Analyzing macro-political processes is complicated by four interrelated problems: model scale, endogeneity, persistence, and specification uncertainty. These problems are endemic in the study of political economy, public opinion, international relations, and other kinds of macro-political research. We show how a Bayesian structural time series approach addresses them. Our illustration is a structurally identified, nine equation model of the U.S. politicaleconomic system. It combines key features of Erikson, MacKuen and Stimson's model of the American macropolity (2002) with those of a leading macroeconomic model of U.S. Zha 1998 andLeeper, Sims, andZha 1996). This structural model, with a loose informed prior, yields the best performance in terms of a mean squared error loss criterion and new insights into the dynamics of the American political economy. The model 1) captures the conventional wisdom about the countercyclical nature of monetary policy (Williams 1990) 2) reveals informational sources of approval dynamics: innovations in information variables affect consumer sentiment and approval and the impacts on consumer sentiment feed-forward into subsequent approval changes, 3) finds that the real economy does not have any major impacts on key macropolity variables and 4) concludes that macropartisanship does not depend on the evolution of the real economy in the short or medium term and only very weakly on informational variables in the long term.

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“…For example, it can lead to sufficiently distorted CIs with such severe spurious asymmetry that the bootstrap CIs do not even include the estimated IRF. Sims andZha (1999, p. 1125, fn 13) note that some SVAR studies have found it necessary to "use a modification of [the bootstrap…”

Section: Introductionmentioning

confidence: 99%