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This paper aims to investigate how different uncertainty shocks affect international housing prices.

The authors set up a model of housing price instability with four uncertainty variables and apply the panel generalized method of moments method and quantile regression to estimate the linear and non-linear linkages among the variables based on data of 56 countries from 2001Q1 to 2018Q2.

Some empirical findings are as follows. Higher macroeconomic uncertainty and global economic policy risk increase housing price instability, whereas greater financial uncertainty and geopolitical risk present reverse effect. Four uncertainty variables are good signals for housing price changes in Asia, and geopolitical risk takes leading role in Europe. Macroeconomic uncertainty positively impacts housing price instability only at a low or middle level in all regions, as financial uncertainty, global economic policy uncertainty and geopolitical risk effects in all regions are smaller at the middle or high level of housing price instability; this confirms the existence of non-linear correlation between each variable.

The findings help investors and policymakers gain a better notion of housing price instability and control into uncertainty signal that could cause housing price instability crash.

This paper examines the relationship between environmental uncertainty and tax avoidance at the firm level. We posit that managers faced with more uncertain environments are likely to engage in more tax avoidance activities. We find a significant and negative relationship between environmental uncertainty and effective tax rates, and our results persist through a battery of robust checks. We further find that managerial ability mitigates the above relationship. Moreover, we find that small, highly leveraged, and innovative firms operating in uncertain environments engage in more tax avoidance.

This chapter develops a return forecasting methodology that allows for instability in the relationship between stock returns and predictor variables, model uncertainty, and parameter estimation uncertainty. The predictive regression specification that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values by employing Bayesian model averaging. The implications of these three sources of uncertainty and their relative importance are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical investor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.

As an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.

This study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.

First, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.

The scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

This chapter provides further evidence on the role of uncertainty and job complexity in pay-for-performance and autonomy decisions. It proposes an encompassing econometric approach in order to explain the differences in previous outcomes that may be due to differing methodological approaches. The main stylized fact is that autonomy and pay-for-performance are positively associated. Additionally, autonomy is positively related to job complexity and uncertainty suggesting that the relationship between these latter variables and pay-for-performance could be through autonomy. After controlling for autonomy, the positive relationship between pay-for-performance and job complexity disappears, while that between pay-for-performance and uncertainty becomes more negative.

Manufacturing strategy has gained increasing attention in recent years. However, the development of theory is inadequate. In addition, there has been insufficient development and validation of operational measures for theoretical constructs derived from a particular conceptualization of strategy. Makes a contribution to theory development of manufacturing strategy by presenting a path analytic model which describes a sequential relationship between uncertainty, risk, manufacturing strategy and performance constructs. The manufacturing strategy constructs include both strategic content and strategic process, in which the content is specified by manufacturing flexibility and the process is specified by four organizational process variables. Using data from three types of industries with 25 manufacturers in each, it was found that both environmental uncertainty and risk taking influenced manufacturing strategy constructs, e.g. the role of the manufacturing function, manufacturing planning activities, environmental interaction and flexibility. A low level of risk taking was facilitative in the formulation of manufacturing strategy constructs. Consequently, the manufacturing strategy constructs influenced business performance.

This paper aims to develop a method for evaluating the failure probability and global sensitivity of multiple failure modes based on convex-probability hybrid uncertainty.

The uncertainty information of the input variable is considered as convex-probability hybrid uncertainty. Moment-independent variable global sensitivity index based on the system failure probability is proposed to quantify the effect of the input variable on the system failure probability. Two-mode sensitivity indices are adopted to characterize the effect of each failure mode on the system failure probability. The method based on active learning Kriging (ALK) model with a truncated candidate regions (TCR) is adopted to evaluate the systems failure probability, as well as sensitivity index and this method is termed as ALK-TCR.

The results of five examples demonstrate the effectiveness of the sensitivity index and the efficiency of the ALK-TCR method in solving the problem of multiple failure modes based on the convex-probability hybrid uncertainty.

Convex-probability hybrid uncertainty is considered on system reliability analysis. Moment-independent variable sensitivity index based on the system failure probability is proposed. Mode sensitivity indices are extended to hybrid uncertain reliability model. An effective global sensitivity analysis approach is developed for the multiple failure modes based on convex-probability hybrid uncertainty.

This study aims to examine short- and long-run effects of specific macroeconomic conditions on risk premium estimates on lending.

Empirical estimates are based on error correction and autoregressive distributed lag models.

The results suggest that, in the short run, inflation expectations, recession expectations and actual inflationary conditions tend to have a significant impact on risk premium estimates; in the long run, however, only inflation expectations and recession expectations are significant in risk premium estimates on lending.

This study examines how specific conditions of uncertainty and expectations influence variability in risk premium estimates on lending in the US economy.

This study examines how specific micro-level macroeconomic indicators influence corporate performance volatility among US corporate bodies in the short run.

The study employs error correction autoregressive distributed lagged (ARDL) model (ECM) to examine how micro-level variables influence volatility associated with corporate performance in the short run.

This paper finds that disaggregated or micro-level variables examined, tend to exhibit features that are not readily apparent from the aggregate variable from which such variables are derived. For instance, reported empirical estimate suggests that, growth in expenditures on services and nondurable goods tend to lower volatility associated with corporate performance, whereas government expenditures and expenditures on durable goods rather worsens volatility associated with corporate performance, all things being equal. Additionally, presented empirical estimates further provide evidence suggesting that macroeconomic uncertainty and inflation uncertainty significantly moderate or influence the extent to which disaggregated variables impact corporate performance volatility.

Compared to related studies in the reviewed literature, this study rather examines volatility associated with corporate performance instead of the corporate performance indicator itself. Additionally, this paper also examines how disaggregated variable instead of aggregate variables impact such volatility. Finally, the moderating role of key macroeconomic conditions in such a relationship is also examined.

For fast calculation of complex structure in engineering, correlations among input variables are often ignored in uncertainty propagation, even though the effect of ignoring these correlations on the output uncertainty is unclear. This paper aims to quantify the inputs uncertainty and estimate the correlations among them acorrding to the collected observed data instead of questionable assumptions. Moreover, the small size of the experimental data should also be considered, as it is such a common engineering problem.

In this paper, a novel method of combining p-box with copula function for both uncertainty quantification and correlation estimation is explored. Copula function is utilized to estimate correlations among uncertain inputs based upon the observed data. The p-box method is employed to quantify the input uncertainty as well as the epistemic uncertainty associated with the limited amount of the observed data. Nested Monte Carlo sampling technique is adopted herein to ensure that the propagation is always feasible. In addition, a Kriging model is built up to reduce the computational cost of uncertainty propagation.

To illustrate the application of this method, an engineering example of structural reliability assessment is performed. The results indicate that it may significantly affect output uncertainty whether to quantify the correlation among input variables. Furthermore, an additional advantage for risk management is obtained in this approach due to the separation of aleatory and epistemic uncertainties.

The proposed method takes advantage of p-box and copula function to deal with the correlations and limited amount of the observed data, which are two important issues of uncertainty quantification in engineering. Thus, it is practical and has the ability to predict accurate response uncertainty or system state.