Does US partisan conflict matter for the Euro area?

Cheng, Chak Hung Jack; Hankins, William; Chiu, Ching‐Wai

doi:10.1016/j.econlet.2015.11.030

Cited by 44 publications

(21 citation statements)

References 7 publications

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“…These findings extend a growing literature examining the consequences of U.S. partisan conflict (see Azzimonti, ,b; Cheng, Hankins, & Chiu, ; Gupta, Lau, Miller, & Wohar, ; Gupta, Pierdzioch, Selmi, & Wohar, ). Some economists believe that the slow recovery from the Great Recession can be explained, in part, by a higher degree of uncertainty.…”

Section: Introductionsupporting

confidence: 82%

Corporate decision making in the presence of political uncertainty: The case of corporate cash holdings

Hankins

Stone

Cheng

et al. 2019

The Financial Review

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Using a quarterly panel of U.S. corporations over the period 1985-2014, we show that corporate managers respond to political uncertainty and economic policy uncertainty shocks in different ways. We proxy for political uncertainty using the Partisan Conflict Index and employ a prevalent empirical macroeconomic methodology to construct structural shocks that are orthogonal to shocks captured by the Economic Policy Uncertainty Index. Following a political uncertainty shock, corporations increase cash but do not adjust investment. Alternatively, following an economic policy uncertainty shock, firms appear to draw on cash and reduce capital spending to increase research and development spending.

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Section: Introductionsupporting

confidence: 82%

Corporate decision making in the presence of political uncertainty: The case of corporate cash holdings

Hankins

Stone

Cheng

et al. 2019

The Financial Review

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“…Second, we estimated a quantile vector autoregressive model (see for example, Gupta et al, (forthcoming a, b)), with PCI ordered first followed by industrial production growth, CPI inflation, shadow short rate and RV. The ordering is based on the assumption of degree of exogeneity and follows Cheng et al, (2016). When the PCI shock was identified using standard Choleski decomposition, based on the generated impulse response functions, we continued to find that PCI had a stronger negative impact on RV at the lower quantile combinations of the two variables.…”

Section: Data and Resultsmentioning

confidence: 88%

“…Modeling this type of dependence requires a QQ approach. Using the QQ approach, we are able to model the quantile of RV as a function of the quantile of lagged PCI, so that the relationship 2 In somewhat related studies by Cheng et al (2016) and Gupta et al (forthcoming c), PCI has been shown to predict stock returns in-and out-of-sample, based on conditional mean-based linear and nonlinear models. 3 Quantile regression was introduced in the seminal paper by Koenker and Bassett (1978).…”

Section: Methodsmentioning

confidence: 99%

Does partisan conflict predict a reduction in US stock market (realized) volatility? Evidence from a quantile-on-quantile regression model☆

Gupta

Pierdzioch

Selmi

et al. 2018

The North American Journal of Economics and Finance

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Theory suggests that partisan conflict negatively affects the possibility of economic policy change, implying that financial markets tend to operate under lower policy risk. Given that stockreturn volatility measures risk, if the gridlock argument holds, stock-market volatility should be lower under divided than under a unified government. Using a partisan conflict index (PCI), we empirically confirm this theoretical argument for the U.S. stock market based on quantiles-based regressions. In particular, quantile-on-quantile regressions show that PCI tends to predict reduced volatility, with the effect being stronger at levels of volatility that are moderately low (i.e., below the median, but not at its extreme) for an increase in the predictor, especially with moderately low and high initial values (i.e., when PCI is at quantiles around the median).

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“…In this regard, there are two approaches to measuring small-and large-scale structural models related to macroeconomics and finance. Irrespective of which approach (news or model based) is pursued, these studies, along with others that have used such indices (e.g., Bachmann & Bayer, 2011;Bachmann, Elstner, & Sims, 2013;Balcilar, Gupta, & Segnon, 2016;Benati, 2013;Caggiano, Castelnuovo, & Groshenny, 2014;Castelnuovo, Caggiano, & Pellegrino, 2015;Cheng, Hankins, & Chiu, 2016;Colombo, 2013;Jones & Olson, 2013;Kang, Lee, & Ratti, 2014;Karnizova & Li, 2014;Knotek & Khan, 2011) confirm the significant role of uncertainty in affecting economic activity.…”

Section: Introductionmentioning

confidence: 80%

Forecasting US GNP growth: The role of uncertainty

Segnon

Gupta

Bekiros

et al. 2018

Journal of Forecasting

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A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.

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Does US partisan conflict matter for the Euro area?

Cited by 44 publications

References 7 publications

Corporate decision making in the presence of political uncertainty: The case of corporate cash holdings

Corporate decision making in the presence of political uncertainty: The case of corporate cash holdings

Does partisan conflict predict a reduction in US stock market (realized) volatility? Evidence from a quantile-on-quantile regression model☆

Forecasting US GNP growth: The role of uncertainty

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