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The purpose of this paper is to propose a new temporal disaggregation method for time series based on the accumulated and inverse accumulated generating operations in grey modeling and the interpolation method.

This disaggregation method includes three main steps, including accumulation, interpolation, and differentiation (AID). First, a low frequency flow series is transformed to the corresponding stock series through accumulated generating operation. Then, values of the stock series at unobserved time is estimated through appropriate interpolation method. And finally, the disaggregated stock series is transformed back to high frequency flow series through inverse accumulated generating operation.

The AID method is tested with a sales series. Results shows that the disaggregated sales data are satisfactory and reliable compared with the original data and disaggregated data using a time series model. The AID method is applicable to both long time series and grey series with insufficient information.

The AID method can be easily used to disaggregate low frequency flow series.

The AID method is a combination of grey modeling technique and interpolation method. Compared with other disaggregation methods, the AID method is simple, and does not require auxiliary information or plausible minimizing criterion required by other disaggregation methods.

This study aims to examine several hypotheses, in conjunction with fundamental accounting concepts, to explain variations in the explanatory power of earnings for returns.

The authors explore three factors for their impact on the explanatory power of earnings. First, the accounting period preceding the earnings report is characterized by distinct intratemporal subperiod behavior. Recognizing this intratemporal nonstationarity is hypothesized to increase the explanatory power of earnings. Second, disaggregation of earnings into operating components is hypothesized to increase the explanatory power of earnings. Moreover, joint consideration of these first two factors is investigated. Third, the authors hypothesize that recognizing fundamental accounting concepts such as timeliness, predictive value, objectivity and verifiability offer key insights into the explanatory power of earnings.

The authors explore a sample of firms with management forecasts, which yields natural intratemporal subperiods – preforecast, forecast and realization periods – to generate hypotheses rooted in fundamental accounting concepts. The empirical evidence shows that recognition of nonstationary intratemporal behavior and earnings disaggregation yields a significant increase in the explanatory power of earning for returns. These findings are linked to fundamental concepts of accounting information.

This study is unique as it examines the joint role of nonstationarity and disaggregation in assessing the information conveyed in earnings. Importantly, results on these factors are linked to fundamental accounting concepts of timeliness, predictive value, objectivity and verifiability, along with their inherent trade-offs.

The purpose of this paper is to test whether temporal aggregation matters when constructing hedonic house price indices for developing markets using Ghana as a case study.

Monthly, quarterly, semi-yearly and yearly hedonic price indices are constructed and six null hypotheses are tested using the F-ratios to examine the temporal aggregation effect.

The results show that temporal aggregation may not be a serious issue when constructing hedonic house price indices for developing markets as a result of the smaller sample size which these markets normally have. At even 10 per cent significance level, none of the F-ratios estimated is statistically significant. Analysis of the mean returns and volatilities reveal that indices constructed at the lower level of temporal aggregation are very volatile, suggesting that the volume of transactions can affect the level of temporal aggregation, and so, the temporal aggregation level should not be generalised, as is currently observed in the literature.

The diversification importance of real estate and the introduction of real estate derivatives and home equity insurance as financial products call for the construction of robust and accurate real estate indices in all markets. While almost all empirical research recommends real estate price indices to be conducted at the lower level of temporal aggregation, these studies are largely conducted in developed markets where transactions take place frequently and large transaction databases exist. Unfortunately, little is known about the importance of temporal aggregation effect when constructing indices for developing real estate markets. This paper contributes to fill these gaps.

Structural models' inflation forecasts are often inferior to those of naïve devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, helping to interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.

How does state repression influence levels of mobilization in authoritarian regimes? This study argues that the relationship between repression and protest is temporally dynamic. Specifically, the short- and long-term effects of autocrats' coercive actions differ conditionally on each phase of the contentious cycle. This argument is tested taking advantage of an original database of protest events in Pinochet's Chile between 1982 and 1989. Using an Interrupted Time Series design, the results show that the State of Siege declarations issued in 1984 and again in 1986 had divergent short- and long-term influence. When the cycle was on an expansive stage, the State of Siege shows no immediate influence on the protests, followed by an increase in long-term mobilization. However, when the mobilization was declining, the State of Siege was associated with an immediate and prominent drop in mobilization, followed by a progressive decrease in the number of protests over the long term. This chapter contributes to the literature on the protest–repression nexus by providing new evidence on the dynamics shaping the relationship between state repression and civil disobedience in authoritarian regimes.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

The purpose of this study is to construct a new index to assess the impact of an energy price shock on macroeconomic indicators of India. This paper also shows a comparative analysis of the constructed index along with pre-existing World Bank and International Monetary Fund indices on energy.

This paper uses three vector autoregressions and compute the long-term impact of the indices on the considered macroeconomic variables through impulse response functions.

This paper finds that an energy price shock has a detrimental impact on the macroeconomic indicators of India in the long run. This study also finds that the constructed index acts as a relatively more sensitive index in comparison to the International Monetary Fund and World Bank indices, which is bespoke to a developing economy case. This sensitivity is ascribed to dynamic weighting for a different basket of energy components, which are more pertinent to an Indian context.

The novelty of this research lies in the construction of a new index and its comparison to the existing ones. This study justifies why a developing economy would require a different measure of energy as opposed to the existing indices.