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This paper analyzes the relationship between stock returns and the inflation rates for the UK over a long time period (February 1790–February 2017) and at different frequencies, by employing a wavelet analysis. We also compare the results for the UK economy with those for the US and two developing countries (India and South Africa). Overall, our results tend to suggest that, while the relationship between stock returns and inflation rates varies across frequencies and time periods, there is no evidence of stock returns acting as an inflation hedge, irrespective of whether we look at the two developed or the two developing markets in our sample.

The global climate system poses important challenges for the perception and understanding of its functioning from policymakers and the general public. The aim of the paper is to model through gamification, the evolution of understanding regarding the dynamics of climate change and climate change adaptation. Using a framework with repeated feedback loops, the impact of the rate of carbon dioxide absorption by natural ecosystems, the stochastic nature of economic systems and the stochastic and irreversible nature of global climate are analyzed, with the Dynamic Climate Change Simulator with Stochastic and Irreversible Climate Change. The simulator game models one control variable, carbon dioxide emissions and one stock variable, carbon dioxide concentrations in the atmosphere. In addition, the rate of carbon dioxide absorption by natural ecosystems is modeled, with business cycle shocks and climate change tipping points. The ability to control carbon dioxide concentrations to a goal level is evaluated and policy insights are provided about how learning about the dynamics of the Earth’s climate through gamification can be advanced.

We provide a novel approach of estimating a regime-switching nonlinear and non-Gaussian state-space model based on a particle learning scheme. In particular, we extend the particle learning method in Liu, J., and M. West. 2001. “Combined Parameter and State Estimation in Simulation-Based Filtering.” In Sequential Monte Carlo Methods in Practice , 197–223. Springer. by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of US excess stock return.

It is widely-known that different methods of detrending data yield different business cycle features. The choice of the detrending method, however, is usually arbitrarily made. This paper aims at revealing potential pitfalls of different detrending methods for the estimation of a standard medium-scale DSGE model. By comparing nine popular detrending methods, we find that model parameter estimates, variance decompositions, optimal monetary policies, and out-of-sample forecasting performances of the model are all sensitive to how the data are detrended. We also discuss some possible criteria to choose among different methods.

The Heterogeneous Autoregressive (HAR) model is commonly used in modeling the dynamics of realized volatility. In this paper, we propose a flexible HAR(1, . . . , p ) specification, employing the adaptive LASSO and its statistical inference theory to see whether the lag structure (1, 5, 22) implied from an economic point of view can be recovered by statistical methods. The model differs from Audrino and Knaus (2016) [Audrino, F. and S. D. Knaus. 2016. “Lassoing the HAR model: A model selection perspective on realized volatility dynamics.” Econometrics Review 35: 1485–1521]. where the authors apply LASSO on the AR(p) model, which does not necessarily lead to a HAR model. Adaptive LASSO estimation and the subsequent hypothesis testing results fail to show strong evidence that such a fixed lag structure can be recovered by a flexible model. We also apply the group LASSO and related tests to check the validity of the classic HAR, which is rejected in most cases. The results justify our intention to use a flexible lag structure while still keeping the HAR frame. In terms of the out-of-sample forecasting, the proposed flexible specification works comparably to the benchmark HAR(1, 5, 22). Moreover, the time-varying model combinations show that when the market environment is not stable, the fixed lag structure (1, 5, 22) is not particularly accurate and effective.