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The purpose of this paper is to analyze the changes in its importance due to the maintenance and repair of components.

In this paper, a concept of time-varying importance measure is proposed to solve the problem of component importance change caused by maintenance. When the system is broken-down, the probability difference between the component works well after repairing and the component break down before repairing is solved, this difference is measured as an index of time-varying importance method. Then, the approach has been verified by the CNC machine tool.

The paper provides a method to analyze the importance of changes of components in the system due to maintenance. The time-varying importance measure can evaluate the component importance anytime during its whole life span, and it has the ability to find out the most responsible component for a system failure in the actual production process. What is more, it provides guidance for the next maintenance work.

The proposed method can guide the next maintenance time according to the change of component performance caused by each maintenance activity of the manufacturing system, and avoid the waste of resources caused by repeated maintenance.

This paper seeks to assess the importance of time‐varying regional patterns to countries' per capita growth rates, and their effect on the conclusions that can be drawn from growth regressions.

Pooled ordinary least squares on a data set of five‐year average growth rates for 101 countries over the period 1960‐1999 are used.

It is shown that time‐varying regional dummies explain more of the variance of per capita growth rates than do commonly used independent variables. This may indicate a problem of omitted variables with a strong regional dimension, or alternatively that growth is highly “contagious” within a region, perhaps through trade. Variables such as the growth of neighbouring countries or trading partners appear to be highly statistically significant when time‐varying regional effects are ignored, but are much less so when they are properly controlled for, and may simply be capturing unobserved regional effects. There is evidence that these variables reflect international business cycle correlation rather than the advantages of trading with, or being close to, faster‐growing countries.

There are strong regional patterns to growth of which one is still far from a full understanding.

Growth of neighbouring countries or trading partners may be much less important for a country's per capita growth than is sometimes claimed.

The findings show the importance of applying appropriate robustness checks to empirical results, and (in a two‐dimensional data set) of exploring whether variables explain the time‐series or the cross‐section dimension.

This paper analyzes the potential presence of time-varying asymmetries in the preference parameters of the Central Bank of Brazil during the inflation targeting regime.

Given the econometric issues inherent to classical time-varying parameter (TVP) regressions, a Bayesian estimation procedure is implemented in order to provide more robust parameter estimates. A stochastic volatility specification is also included to take into account the potential presence of conditional heteroskedasticity.

The obtained results show that the reduced form and structural parameters were not constant during the period considered. Moreover, the subsequent analysis of the preference parameters provided evidences of short periods in which asymmetry was an important feature to the conduction of monetary policy in Brazil. Yet, during most of the sample period, the loss function was considered to be symmetrical.

This paper aims to contribute to the rather scarce monetary debate on time-varying central bank preferences. The study of Lopes and Aragón (2014) is, to the best of the authors’ knowledge, the only study for Brazil considering specifically TVPs. The authors applied Kalman filter estimation to data from 2000:M1 to 2011:M12. Despite the similar structure of TVPs, the present paper extends the latter study by controlling for stochastic volatility. Ignoring conditional heteroskedasticity might lead to spurious movements in time-varying variables and inaccurate inference (Hamilton, 2010). Thus, the stochastic volatility specification is included to take this issue into account. The authors follow the theoretical scheme put forward by Surico (2007) and Aragón and Portugal (2010), in which the economy is modeled from a New Keynesian perspective and the central bank loss function is assumed to be asymmetric regarding the responses to inflation and output deviations from their targets. On the empirical side, the authors propose a TVP univariate regression with stochastic volatility for the Brazilian reduced-form reaction function, following closely the Bayesian econometric procedure developed by Nakajima (2011). Given the nonlinear non-Gaussian nature of the TVP regression with stochastic volatility, the choice of a nonlinear Bayesian approach using the Markov chain Monte Carlo (MCMC) method is justified due to the intractability of the associated likelihood function (Primiceri, 2005). Finally, based on the theoretical model specification, the authors intend to recover the central bank preference parameters as to further evaluate the degree of asymmetry and its potential time-variation under the inflation targeting regime.

Company intellectual capital (IC) is nowadays considered as a key resource that can transform a company’s value. For this reason, the efficiency of IC is crucial for all stakeholders. Evaluating efficiency is difficult, because IC is partly unobservable and its efficiency varies across time. The aim of this study is to suggest a methodology for estimating the dynamic efficiency of a company’s intellectual resources.

The panel data model suggested by Kneip et al. (2012) is used to estimate dynamic efficiency. The main feature of this model is that the unobservable component has a multi-dimensional factor structure. Taking advantage of the ability of this model to control for unobserved complex heterogeneity, the authors use the results in further stochastic frontier analysis. A data set containing information about Russian companies for the period from 2001 to 2010 is used.

In this paper, the dynamic efficiency of Russian companies is estimated. It is shown that, using the traditional efficiency estimate, companies can be overestimated.

The main limitation of the suggested methodology is that it is necessary to have a long panel data structure.

Taking advantage of time-varying efficiency, one can estimate the efficiency growth rate as a measure of performance, standard deviation as a measure of risk and autocorrelation as a measure of stability.

This is the first study to present clear evidence of the time-varying nature of IC efficiency. On the methodological side, the paper presents a fairly simple method capable of estimating various indicators of a company’s efficiency.

The purpose of this paper is to evaluate whether the effect of inflation uncertainty on inflation has changed over time in Latin America.

The authors use a stochastic volatility in mean (SVM) model with time-varying parameters (TVP) as proposed by Chan (2017).

Considering inflation series for the last two decades, we report evidences of high uncertainty from 1996 to early 2000s. Moreover, despite being positive throughout the sample, the overall relationship between inflation uncertainty and inflation has changed over the years in Latin America, underscoring the importance of our time-varying specification.

There are evidences of a greater volatile inflation behavior in the beginning of the sample period in comparison to the last few years. Overall, the considered Latin American economies seem to have endured relatively well the external adverse shocks from the 2008 global financial crisis.

The use of an SVM model with TVP in order to assess the effect of inflation uncertainty on inflation is new to the Latin America literature.

This paper aims to examine whether the volatility of returns in commodity (gold, oil), bond and forex markets is related over time to the volatility of returns in equity markets of Bangladesh, Indonesia, Pakistan, Philippines, Turkey and Vietnam. In addition, the authors analyze the integration of the commodity, bond, forex and equity markets across these markets.

The dynamic conditional correlation GARCH (DCC-GARCH) model is used to capture the time-varying conditional correlation among markets. The authors use daily data of stock prices, oil prices, gold prices, exchange rates and 10 years' bond yields of the six countries from Datastream and investing.com from January 2001 to April 2021.

Findings reveal that the parameters of dynamic correlation are statistically significant which indicates the importance of time-varying co-movements. Estimation of the DCC-GARCH model suggests that the stock market is significantly correlated with bond, forex, gold and oil markets in all six countries.

This study has practical implications for policymakers and investment professionals. A better understanding of dynamic linkages among the markets would help in constructing effective hedging and portfolio diversification strategies. Policy makers can get insight to build proper strategies in order to insulate the economy from factors that cause volatility.

Several studies have investigated the linkage between commodity and stock markets and the volatility spillover effect, but very little attention is given to study the interrelationship between groups of market segments of different economies. No study has comparatively examined the dynamic relationship of multiple markets of a group of emerging countries simultaneously.

The purpose of this paper is to apply duration methods to a sample of Farm Service Agency (FSA) direct, seven‐year operating loans to identify those variables that influence the time to loan termination and type of termination. Variables include both those known at time of loan origination and those that characterize the changing economic environment over the life of the loan. Also, to examine the impact of various FSA programs promoting policy objectives.

A systematic sample of 877 seven‐year, FSA direct loans originated between October 1, 1993 and September 30, 1996 was collected. Cox regression, competing risks models are estimated as a function of borrower and loan characteristics observable at loan origination. Economic indicator variables emphasizing the farm economy and observed quarterly over the life of the loan are also included as explanatory variables.

Loan characteristics, borrower financial characteristics and degree of borrower interaction with FSA observable at origin are significant variables in determining type of loan outcome (default or paid‐in‐full) and time to outcome. Changes in the economic environment and farm economy during the life of the loan are significant.

The sample consists only of FSA direct loans which implies borrowers are at financial margin. Application of method to agricultural loans from conventional commercial lenders could identify different significant factors.

Using length of time to loan termination instead of just type of outcome provides for a richer analysis of loan performance. Loan performance over time is influenced by the larger economy and should be incorporated into loan performance modeling.

The study described in the paper demonstrates use of competing risks models on intermediate agricultural loans and develops how this technique can be used to learn about dynamic aspects of loan performance. Sample consists of observations on individual FSA direct loan borrowers. The FSA direct loan program is the major source of credit for agricultural borrowers at the financial margin.

This study examines the risk spillovers between Indonesian sectorial stocks (Energy, Basic Materials, Industrials, Consumer Cyclicals, Consumer Non-cyclical and Financials), the aggregate index (IDX) and two commodities (gold and West Texas Intermediate Crude Oil [WTI] futures).

The study uses two methodologies: the TVP-VAR model of Antonakakis and Gabauer (2017) and the quantile connectedness approach of Ando et al. (2022). The data cover the period from October 04, 2010, to April 5, 2022.

The results show that the IDX, industrials and materials are net transmitters, while the financials, consumer noncyclical and energy sectors are the dominant shock receivers. Using the quantile connectedness approach, the role of each sector is heterogeneous and asymmetric, and the return spillover is stronger at lower and higher quantiles. Furthermore, the portfolio hedging results show that oil offers more diversification gains than gold, and hedging oil is more effective during the pandemic.

This study provides valuable insights for investors to diversify their portfolios and for policymakers to develop policies, regulations and risk management tools to promote stability in the Indonesian stock market. The results can inform the design of market regulations and the development of risk management tools to ensure the stability and resilience of the market.

This study is the first to examine the spillovers between commodities and Indonesian sectors, recognizing the presence of heterogeneity in the relationship under different market conditions. It provides important portfolio diversification insights for equity investors interested in the Indonesian stock market and policymakers.

The paper examines the conditional capital asset pricing model (CCAPM) of Jagannathan and Wang using the UK data and develops a data-driven measure of beta instability risk that is pertinent to the UK stock market. In contrast to the view that the main part of the Jagannathan and Wang's model is the inclusion of human capital, however, the paper finds that human capital remains insignificant in most tests.

Data were taken from the London Share Price Database and Datastream. This paper therefore examines the premium labour (PL) model of Jagannathan and Wang using the UK data, while the paper attaches particular importance to the measure of beta instability as a source of time variation in betas. In analysing the measure of beta instability risk, this study considers a testable measure of instability risk that varies across markets and across time as the interaction between the stock market and the economy varies across different time periods. Hence, this paper develops a data-driven measure of beta instability risk that is pertinent to the UK stock market.

The results confirm the premium version of the model, that is, the CCAPM without a proxy for human capital. In particular, the paper finds that over the entire time period of this study, the measure for beta instability risk and market portfolio has significant explanatory power for the variations of returns. More specifically, when using the average earnings index as a proxy for human capital in the PL model, the premium model performs better than the PL model. When total income from employment is used as a proxy for human capital, the performance of the PL model improves for the full period. However, the results for the two sub-periods are less favourable for the PL model as, again, labour income is not priced for these periods. These results indicate that the PL model is sensitive to proxies used for human capital.

The results revive the importance of beta instability risk in CCAPM of Jagannathan and Wang's model and suggest that the beta instability drives this model.

Even though Bitcoin has been often labelled as a safe haven asset class in the literature, the influence of economic policy uncertainty (EPU) on the diversifying opportunities offered by Bitcoin in relation to other assets needs to be investigated. This paper aims to investigate how the EPU affects diversification of commodity, conventional, Islamic and sustainable equity returns in relation to its impact on Bitcoin returns.

The authors use advanced time-series econometrics, namely, multivariate generalized autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transformation, for the analysis of the daily returns for the aforementioned assets between 01 August 2011 and 01 September 2019.

First, the authors found a strong evidence of Bitcoin’s mean reverting trend in the long run while its volatility has decreased significantly since 2013. After separating the EPU into two regimes (high and low), diversification opportunities with Bitcoin seems to disappear in a high EPU period, while the hedging opportunity tends to prevail in a low EPU period for all classes of assets. Importantly, the findings indicate that Bitcoin offers short-term diversification for sustainable and Islamic equity as well as energy stocks during a low uncertainty period. Consequently, in relation to the policy uncertainty, Bitcoin provides similar hedging opportunities than commodities like Gold and Silver. Overall, the study shows that EPU is remarkably important in explaining the average portfolio returns of Bitcoin, suggesting that this indicator can be perceived as a decent explanatory factor for portfolio diversification.

The study significantly extends the empirical literature of Bitcoin’s portfolio diversification by taking EPU into consideration. To the best of authors’ knowledge, this is one of the few studies to investigate the asymmetric effects of US EPU on Bitcoin’s hedging capabilities by taking into account major conventional equity, sustainable equity, Islamic equity, gold, silver and oil.