Explore the vast range of titles available.

In this paper, we empirically investigate the effects of oil price changes on inflation over the period 1991–2016 for eight industrial countries: the United States, Canada, Japan, Australia, Germany, France, Italy, and the UK. In doing so, we use an oil-augmented Phillips curve with unobserved components and we consider time-varying coefficients. The results show that even over a period of low and stable inflation, oil prices play a significant role in the dynamics of inflation. In all the countries except Germany, oil pass-through into inflation increased from the early 2000s up until the global financial crisis. In the United States it has nearly doubled in the last fifteen years. These findings suggest that central banks must continue to monitor oil prices closely.

This paper develops a multivariate filter based on an unobserved component model to estimate the financial cycle. Our model features: (1) a dynamic relationship between the financial cycle and key variables; (2) time-varying shock volatility for trend and cycle components. We demonstrate that our approach not only exhibits superior early warning properties for banking crises but also outperforms commonly used indicators in terms of data fit for decomposition exercises, as evidenced by the higher marginal likelihood. We document three important properties of the financial cycle. First, the sensitivity of the financial cycle to changes in real estate valuations increased during the post-90s period. Second, the sensitivity of the cycle to changes in financial conditions displays volatility and country specificities. Finally, our reduced form estimates suggest that the banking crisis of 1988 was preceded by positive contributions from the risk appetite shock, while the primary source of vulnerabilities emanated from the housing market in the run-up to the Global Financial Crisis.

Reconciling economic growth with environmental sustainability and energy security is a defining challenge for resource-constrained emerging economies. This study examines whether Jordan follows the Environmental Kuznets Curve (EKC) and the Energy Kuznets Curve (EnKC)—two hypotheses positing that as an economy grows, its environmental degradation and energy consumption follow an inverted U-shaped curve in relation to per capita GDP—as counterparts to the original Kuznets curve. While these relationships have been investigated in cross-country settings, little attention has been given to individual emerging economies such as Jordan, where energy and environmental issues are among the most pressing challenges of the new century. The existence of EKC and EnKC curves is tested using a “time-varying parametric (TVP) framework”—specifically, the unobserved components model (UCM), utilizing annual data from 1980 to 2024. Further tests are carried out to validate the nonlinearity hypothesis using the variable-addition test and non-nested model selection tests. Moreover, we augment the EKC and EnKC by incorporating trade openness and urbanization as control variables. For robustness, we support the UCM results with the Dynamic OLS (DOLS) long-run estimator. The results support the EnKC across the entire battery of tests, with a turning point of roughly USD 4000–4650 depending on specification. For the EKC, the OLS quadratic estimation does not exhibit a clear inverted-U; however, once a stochastic trend (UCM) or appropriate covariates (Trade, Urban) are introduced, the inverted-U re-emerges with a turning point near USD 4149–4874. This study contributes novel empirical evidence on the EKC and EnKC for Jordan using a TVP framework. Whereas prior studies have explored the EKC in Jordan, this study systematically validates both the energy and environmental variants of the Kuznets curve using robust econometric strategies. The results offer valuable policy insights for sustainable development in Jordan and other resource-constrained emerging economies facing analogous development–environment trade-offs within international climate transition frameworks.

The paper derives monthly estimates of business sector output in the UK from rolling quarterly value-added tax based turnover data. The administrative nature of the value-added tax data implies that their use could ultimately yield a more precise and granular picture of output across the economy. However, they show two particular features which complicate their exploitation: they are overlapping and subject to substantial noise. This motivates our choice of a multivariate unobserved components model for filtering and disaggregating temporally the aggregate figures. After illustrating our method by using one industry as a case-study, we estimate monthly seasonally adjusted gross output figures for the 75 industries for which the data are available. Our results show material differences from the existing output profile.

This paper explores how financial development and income inequality interact across different country income groups and what this means for business environments and market participation in both emerging and advanced economies. Using an Unobserved Components Model (UCM) with time-series data covering 1990–2023, the analysis shows that the link between finance and inequality varies markedly with the level of economic development. An inverted U-shaped relationship appears only in high-income and upper-middle-income countries, suggesting that once financial systems reach a certain level of maturity, further deepening tends to support more inclusive outcomes. By contrast, in lower-middle-income countries, financial development is associated with a positive and monotonic increase in inequality, while in low-income countries, the relationship remains weak, unstable, and statistically insignificant. A closer breakdown indicates that financial markets, rather than financial institutions, play a stronger role in influencing inequality in higher-income economies. Overall, the findings highlight that the distributional impact of financial development—and its implications for business conditions, market access, and investment incentives—is strongly income-dependent, reinforcing the need for financial frameworks that align with countries’ stages of development.

IntroductionSince the 1960s, global agricultural prices have exhibited significant episodic volatility. This study aims to conduct a trend-cycle decomposition of these prices to identify the cyclical patterns of their long-run dynamic evolution. Furthermore, it investigates the underlying drivers of long-term price dynamics and their structural transformations over time.MethodsThis study employs two distinct econometric approaches to analyze global agricultural price dynamics. First, we utilize a refined Beveridge–Nelson (BN) filter to decompose global agricultural prices into their trend and cyclical components. Second, to investigate the sources of long-term fluctuations, we disaggregate agricultural commodities into food and non-food sectors and construct a two-sector unobserved components model with stochastic volatility (UC-SV).ResultsThe analysis yields the following key findings: Overall Pattern: Since the 1960s, global agricultural prices have undergone two episodes of rapid growth and three distinct adjustment cycles. Trend-Realization Divergence: Following the turn of the 21st century, global agricultural trend inflation diverged markedly from realized inflation. Structural Shift: Variance decomposition reveals that prior to the 21st century, trend inflation volatility was jointly driven by food prices, non-food prices, and their co-movement. However, post-2000, the contribution from non-food prices diminished, while the share attributable to food prices intensified considerably. Intra-food Dynamics: Further decomposition of food trend inflation variance indicates that the covariance between cereal and non-cereal prices, along with the direct contribution of non-cereals, constitutes a substantial portion of the volatility.DiscussionThe findings reveal a profound transformation in the price formation mechanism of global agricultural commodities. The observed asymmetry between trend and realized inflation suggests that high-frequency, short-term factors now exert a significant and intensifying influence, thereby amplifying the “noise” component in price movements. Moreover, the post-2000 dominance of food prices and the prominence of cross-commodity linkages underscore the critical role of inter-commodity correlations in driving long-run food price volatility.

This paper investigates the notion of the financial Kuznets curve in an emerging country-Jordan. Both variants of the financial Kuznets curve (growth financial Kuznets curve and inequality financial Kuznets curve) have been examined using different time series methodologies applying to a sample period from 1993 to 2017. The unobserved components model results provide evidence for both variants of the financial Kuznets curve when using private credit to GDP as a proxy for financial-sector development. Moreover, non-nested model tests suggest that financial intermediaries are relatively more important than stock markets for income inequality. Overall, this paper provides evidence for the financial Kuznets curve in emerging countries. Moreover, it provides new insights for policymakers in Jordan in their challenge to boost economic growth and decelerate income inequality, by reversing the trend towards the concentration of power in the financial sector and creating public-financial institutions that provide affordable credit to small businesses and households.

Predicting building energy consumption is an essential part of demand-side management since it enables cost-effective building operation under limited resources. Recent prediction models have adopted deep learning networks due to their high capabilities in extracting occupants’ energy use patterns from historical data. Additionally, augmenting historical data by decomposing existing input times-series into several temporal components can enhance prediction performance, particularly for new buildings. However, it still remains unclear how newly created predictors, through the decomposition of existing time-series, affect the performance of building energy use prediction. Therefore, to address this gap, this study proposed a deep learning-based energy use prediction framework that employs the Unobserved Component Model to create new input time-series based on existing ones. Then, the performance of the proposed prediction framework was evaluated using two years of historical data collected from a case building. The main findings are threefold. First, deep learning networks achieved a higher prediction performance during training than during testing. Second, testing performance was generally better when using the augmented dataset than the raw dataset. Third, the proposed data augmentation method contributes to a 1.26% decrease in MAPE and a 3.42% decrease in CvRMSE. This suggests that the proposed prediction framework can be applied to simulations of buildings with limited time series dataset to more accurately predict energy consumption at the building level.

This paper applies the common stochastic trends representation approach to the time series of total factor productivity (TFP) and the relative price of investment (RPI) to investigate the modeling of neutral technology (NT) and investment-specific technology (IST) and its econometric ramifications on the analysis of general equilibrium model. The permanent and transitory movements in both series are estimated efficiently via Markov chain Monte Carlo methods using band matrix algorithms. The results indicate that TFP and the RPI are, each, well represented by a differenced first-order autoregressive process. In addition, their time series share a common trend component that we interpret as reflecting changes in general purpose technology. These results are consistent with studies that suggest that (1) the traditional view of assuming that NT and IST follow independent processes is not supported by the features of the time series and (2) improper specification of secular trends may distort estimation and inference. Notably, the findings provide some guidance to minimize the effect of idiosyncratic and common trend misspecifications on the analysis of impulse dynamics and propagation mechanisms in macroeconomic models.

This article proposes a new method for estimating potential output in which potential real gross domestic product (GDP) is modeled as an unobserved stochastic trend, and deviations of GDP from potential affect inflation through an aggregate supply relationship. The output and inflation equations together form a bivariate unobserved-components model which is estimated via maximum likelihood through the use of the Kalman-filter algorithm. The procedure yields a measure of potential output and its standard error and an estimate of the quantitative response of inflation to real growth and the output gap.