Prevention Effect of News Shocks in Anti-Doping Policies

This paper contributes to the debate on anti-doping policies not by evaluating the policy itself but by evaluating the announcement of (new) policy measures. We develop a dynamic model for analyzing the effects of two different types of news shocks: (1) the preannouncement of improved drug testing technological opportunities and (2) the preannouncement of future increases in financial sanctions. We find that the anticipation of policy changes affects the behavior of potentially delinquent athletes. In both scenarios, our simulations show immediately reduced drug abuse among athletes. We conclude that authorities may consider news shocks as an anti-doping strategy.

Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news.

NEWS SHOCKS AND THE EFFECTS OF MONETARY POLICY

Traditionally identified monetary shocks in a structural vector autoregression (SVAR) model typically result in long-lasting effects on output and total factor productivity (TFP). In this paper, I argue that the typical monetary shock has been confounded with the news shock about future technology. I propose and implement a novel SVAR approach that effectively “cleans” the technology component from the traditional Cholesky monetary shock. With the new identification, I find that a monetary shock exerts smaller and less persistent effects on output and the level of measured TFP than a traditionally identified monetary shock. Finally, I show that the SVAR impulse responses can be replicated by augmenting the standard New Keynesian model with a time-varying inflation target and a non-Ricardian fiscal policy regime.

Unraveling News: Reconciling Conflicting Evidence

This paper addresses the lack of consensus in the empirical literature regarding the effects of technology diffusion news shocks. We attribute the conflicting evidence to the wide diversity in terms of variable settings, productivity series used, and identification schemes applied. We analyze the different identification schemes that have been employed in this literature. More specifically, we impose short- and medium-run restrictions to identify a news shock. The focus is on the medium-run identification maximizing at and over different horizons. We show that the identified news shock depends critically on the applied identification scheme and on the maximization horizon. We also investigate the importance of the information content of the model and of the productivity measure used. We find that models which either contain a large set of macroeconomic variables or include variables that are strongly forward looking deliver more robust results. Moreover, we show that the productivity series used may influence results, but there is convergence of findings for newer total factor productivity series vintages. Our conclusion is that news shocks have expansionary properties.

Identification and inference with ranking restrictions

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

Patent-Based News Shocks

What do patents tell us about the economic effects of future technological improvements? We identify aggregate and industry level patent-based news shocks by exploiting changes in stock market valuations of firms that obtain patent grants. Our shocks resemble diffusion news, as they do not affect total factor productivity in the short run but induce a strong permanent effect after five years. We find that patent-based news shocks produce positive comovement between consumption, output, investment, and hours. They also generate positive responses in inflation and in the federal funds rate, consistent with standard New Keynesian models.