RePEc: Research Papers in Economics
Research Papers in Economics is a collaborative effort of hundreds of volunteers in many countries to enhance the dissemination of research in economics.
NCER Working Paper Series
2016
-
#116Download full text
- JEL-Codes:
- C43; D72; L82
- Keywords:
- media bias, governmental capture, index
Does the 4th Estate Deliver? Towards a More Direct Measure of Political Media Bias
This contribution introduces a new direct measure of political media bias by analyzing articles and newscasts with respect to the tonality on political parties and politicians. On this basis we develop an index sorting the media in the political left to right spectrum. We apply the index to opinion‐leading media in Germany, analysing 7,203,351 reports on political parties and politicians in 35 media outlets from 1988 to 2012. With this approach, in contrast to other indexes, we are able to achieve a more direct and reliable measure of media bias. In addition, we apply the index to study whether the media fulfil their role as the fourth estate, i.e. provide another level of control for government, or whether there is evidence of government capture.
-
#115Download full text
- JEL-Codes:
- C53; F47; G15
- Keywords:
- Extreme risk, Co-movements, Multivariate Hawkes-POT, Point process, Value at Risk
Modelling Extreme Risks in Commodities and Commodity Currencies
This paper analyzes extreme co-movements between the Australian and Canadian commodity currencies, and the gold and oil markets respectively, within a multivariate extension of the Hawkes-POT model. The intensity of extreme events in the Australian dollar are influenced by extreme events in gold, while the size of extreme events in the Canadian dollar are driven by extreme events in crude oil. Models with both self-excitation and cross-excitation produce the most accurate predictions of extreme risk in these markets. The results of this paper will provide participants in the commodity and currency markets a deeper understanding of the risks they face.
-
#114Download full text
- Keywords:
- DSGE models, shocks
An Unintended Consequence of Using "Errors in Variables Shocks" in DSGE Models?
This note shows that the common practice of adding on measurement errors or "errors in variables" when estimating DSGE models can imply that there is a lack of co-integration between model and data variables and also between data variables themselves. An analysis is provided of what the nature of the measurement error would be if it was desired to ensure co-integration. It is very unlikely that it would be the white noise shocks that are commonly used.
-
#113Download full text
- JEL-Codes:
- C12; C15; C32, E47
- Keywords:
- Time-varying Granger causality, subsample Wald tests, Money-Income
Causal Change Detection in Possibly Integrated Systems: Revisiting the Money-Income Relationship
This paper re-examines changes in the causal link between money and income in the United States for over the past half century (1959 - 2014). Three methods for the data-driven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a recursive rolling algorithm and the rolling window algorithm all of which utilize subsample tests of Granger causality within a lag-augmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. The results from a suite of simulation experiments suggest that the rolling window algorithm provides the most reliable results, followed by the recursive rolling method. The forward expanding window procedure is shown to have worst performance. All three approaches find evidence of money-income causality during the Volcker period in the 1980s. The rolling and recursive rolling algorithms detect two additional causality episodes: the turbulent period of late 1960s and the starting period of the subprime mortgage crisis in 2007.
-
#112Download full text
- JEL-Codes:
- C30;C36;E13
- Keywords:
- Impulse Responses to DSGE, SVAR
Investigating the Relationship Between DSGE and SVAR Models
DSGE models often contain variables for which data is not observed when estimating. Although DSGE models generally imply that there is a finite order SVAR in all the variables this may no longer be true for SVARs just in observable variables, and so there is a VAR-truncation problem. The paper examines this issue. It looks at five different studies using DSGE models that appear in the literature. Generally it emerges that the truncation issue is probably not that important, except possibly in small open economy models with external debt. Even when there is no truncation problem in VARs which control the dynamics) the structural impulse responses from both models may be different due to differing initial responses. It is shown that DSGE models incorporate some strong restrictions on the nature of SVAR models and these would need to employed for the two approaches to give the same initial estimates.
-
#111Download full text
- JEL-Codes:
- C22; G11; G17
- Keywords:
- Volatility, multivariate GARCH, equicorrelation, portfolio allocation
Volatility Dependent Dynamic Equicorrelation
This paper explores the link between equicorrelation and market volatility. The standard equicorrelation model is extended to condition the correlation process on volatility, based on the Volatility Dependent Dynamic Conditional Correlation class of model. Analysis of this relationship is presented in two empirical examples, with both a national and international context studied. The various correlation forecasting methods are compared using a portfolio allocation problem, specifically the global minimum variance portfolio and Model Confidence Set. Relative economic value is also considered. In the case of U.S. equities, overall the equicorrelation models perform well and the inclusion of volatility in the equicorrelations performs well against the standard equicorrelated model. For large portfolios a simple specification such as constant conditional correlation seems sufficient, particularly during periods of market calm. Internationally, the equicorrelated models perform poorly against the dynamic conditional corelation-based models. Reasoning is provided that the information pooling advantage equicorrelation has over dynamic conditional correlation models is eroded when forecasting correlations between indices, rather than equities. In both applications, there appears to be no statistically significant difference between the standard equicorrelation model and the Volatility Dependent class although in general a volatility dependent structure leads to lower portfolio variances.