IS THE REPORTS-BASED MEASURE OF UNCERTAINTY STATIONARY? EVIDENCE FROM A NEW PANEL RESIDUAL AUGMENTED LEAST SQUARES UNIT ROOT TEST

To avoid spurious inferences, researchers analyzing the dimensions of uncertainty need to determine whether it is nonstationary. The degree of persistence of uncertainty also indicates the duration of the negative impact of an uncertainty shock on the economy. We use a new panel residual augmented least squares unit root test that allows for heterogeneous structural breaks in both intercepts and slopes of a series to determine the degree of persistence of the reports-based measure of uncertainty and whether it is nonstationary for 143 countries. This group of countries accounts for 99% of the world’s gross domestic product (GDP). To assess the robustness of our results, we also use recently developed univariate time-series unit root tests that allow for structural breaks and panel unit root tests that accommodate cross-sectional dependence and nonlinearity. Furthermore, an autoregressive wild bootstrap approach is utilized to examine the stationarity of the series. The results are virtually unambiguous in indicating that the reports-based measure of uncertainty is stationary in all countries considered. The results also suggest that uncertainty has a negative impact on the growth rate of GDP. The policy implications of the results are also discussed.

POLICY UNCERTAINTY AND THE DEMAND FOR MONEY IN SINGAPORE: AN ASYMMETRIC ANALYSIS

In search of a stable demand for money, almost all previous studies include two uncertainty measures captured by the volatility of the money supply and output. While in some countries, this yielded a stable demand for money, in some others, it did not. The latter was the case for Singapore. In this paper, we use a relatively more new and comprehensive measure of uncertainty known as policy uncertainty that is a news-based measure, and revisit the demand for money in Singapore. Our approach not only yields a stable demand for money in Singapore, but also reveals that the long-run effects of policy uncertainty on the demand for money are asymmetric. While increased uncertainty induces the public in Singapore to hold more money, decreased uncertainty does not affect.

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.

Regime Uncertainty and Interstate Conflict

Regime type is an important variable in international relations. Numerous scholars have theorized its effects on actors’ crisis behavior and outcomes. Despite regime type's importance, the literature has not focused on the role its uncertainty might play in interstate politics. This is in stark contrast to the scholarly attention given to uncertainty about other similarly important variables like actor capabilities, intentions, or fighting costs. In this paper, we aim to address this gap in the literature by providing a theory of regime uncertainty's effects on conflict and developing a novel measure of uncertainty about regime type in interstate relations to test our hypotheses. We find that regime uncertainty breeds caution rather than conflict: higher uncertainty about the opponent's regime type makes conflict initiation and escalation less likely in disputes, and dyads with more uncertainty are less likely to experience conflict onset.

Advances in the Stochastic Modeling of Constitutive Laws at Small and Finite Strains

The characterization and identification of uncertainties in the physical properties of complex materials have been the subjects of longstanding interest in both research and engineering. These efforts were supported by the growing interest in Uncertainty Quantification (UQ) where predominating system-parameter and model-form uncertainties are integrated in a unified mathematical treatment to endow predictions with some statistical measure of uncertainty (fidelity) [7] (see also [23, 30]). Once properly modeled and calibrated, these uncertainties can then be propagated to the structural response, following for instance the spectral approach introduced in the celebrated monograph by Ghanem and Spanos [8] (see also [13]). Such stochastic simulations are then purposely used in order to increase the robustness of the computational models and design procedures, especially when the mechanical models are highly nonlinear (in which case small variations in the inputs can have dramatic effects on the predicted outputs and thus, on design procedures). They also enable a deeper understanding of the critical mechanisms governing the physics (associated with damage propagation, for instance) at relevant scales.

Does Bitcoin Hedge Categorical Economic Uncertainty? A Quantile Analysis

This paper examines the hedge and safe-haven abilities of Bitcoin against U.S. aggregate and categorical economic policy uncertainty (EPU) via the application of quantile regression model augmented with a dummy and some control variables. Using monthly data from September 2011 to December 2019, empirical results indicate that Bitcoin does not act as a strong hedge against the aggregate U.S. EPU. However, it acts as a strong safe-haven for this aggregate measure of uncertainty when the Bitcoin market is bearish. Looking deeper into the disaggregated level of the U.S. EPU data, the analyses involving categorical EPU data indicate the ability of Bitcoin to act as a strong hedge and safe-haven against specific uncertainties related to fiscal policy, taxes, national security, and trade policy.

Results on Varextropy Measure of Random Variables

In 2015, Lad, Sanfilippo and Agrò proposed an alternative measure of uncertainty dual to the entropy known as extropy. This paper provides some results on a dispersion measure of extropy of random variables which is called varextropy and studies several properties of this concept. Especially, the varextropy measure of residual and past lifetimes, order statistics, record values and proportional hazard rate models are discussed. Moreover, the conditional varextropy is considered and some properties of this measure are studied. Finally, a new stochastic comparison method, named varextropy ordering, is introduced and some of its properties are presented.

Possibilistic Uncertainty Quantification in 1-D Consolidation Problems

In this study, we use possibility distribution as a basis for parameter uncertainty quantification in one-dimensional consolidation problems. A Possibility distribution is the one-point coverage function of a random set and viewed as containing both partial ignorance and uncertainty. Vagueness and scarcity of information needed for characterizing the coefficient of consolidation in clay can be handled using possibility distributions. Possibility distributions can be constructed from existing data, or based on transformation of probability distributions. An attempt is made to set a systematic approach for estimating uncertainty propagation during the consolidation process. The measure of uncertainty is based on Klir's definition (1995). We make comparisons with results obtained from other approaches (probabilistic…) and discuss the importance of using possibility distributions in this type of problems.