Labour productivity trends in Australian manufacturing : some time series properties

Labour productivity plays a significant role in economic growth, labour demand and employment situation of a particular economy. In this light, the presence of a structural break in productivity, and its unit root property, has important consequences for the overall economy and in major sectors such as manufacturing. In this article, using some recently developed unit root tests, we examine: (i) the null hypothesis of a unit root in the log-level of labour productivity for 38 manufacturing subdivisions against the alternative of trend stationarity over a three-decade period; and (ii) the presence of a structural break in the series, and whether the break has had a permanent or a transitory effect on manufacturing labour productivity. Our main finding is that shocks to labour productivity have had a transitory effect, implying that policies are likely to have only short-term effects.

Structural breaks and unit roots in Australian macroeconomic time series

We examine the unit root properties of 16 Australian macroeconomic time series using monthly data spanning the period 1960–2004. In addition to the standard Augmented Dickey Fuller (ADF) test, we implement one- and two-break endogenous structural break ADF-type unit root tests as well as one- and two-break Lagrange multiplier (LM) unit root tests. While the ADF test provides relatively little evidence against the unit root null hypothesis, once we allow for structural breaks we are able to reject the unit root null for just under half of the variables at the 10% level or better.

What determines bilateral aid allocations? New evidence for time series data

This paper models the allocation of bilateral foreign development aid to developing countries. A simple theoretical framework is developed, in which aid is treated as a private good of a donor country bureaucratic group responsible for bilateral aid allocation. This model is applied to time series data for ten principal recipients of bilateral official development assistance. Features of this application are that it caters for the joint determination of aid allocations and for donor allocation behavior to differ among individual recipient countries. Results indicate that both recipient need and donor interest variables determine the amount of foreign aid to developing countries, and that donor allocation behavior often differs markedly among recipients.

The application of an ensemble of boosted elman networks to time series prediction : a benchmark study

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.

Faster and parameter-free discord search in quasi-periodic time series

Time series discord has proven to be a useful concept for time-series anomaly identification. To search for discords, various algorithms have been developed. Most of these algorithms rely on pre-building an index (such as a trie) for subsequences. Users of these algorithms are typically required to choose optimal values for word-length and/or alphabet-size parameters of the index, which are not intuitive. In this paper, we propose an algorithm to directly search for the top-K discords, without the requirement of building an index or tuning external parameters. The algorithm exploits quasi-periodicity present in many time series. For quasi-periodic time series, the algorithm gains significant speedup by reducing the number of calls to the distance function.

Parameter-free search of time-series discord

Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurrence plot shows the advantage of being fast and parameter free. The direct search algorithm, however, relies on quasi-periodicity in input time series, an assumption that limits the algorithm's applicability. In this paper, we eliminate the periodicity assumption from the direct search algorithm by proposing a reference function for subsequences and a new sampling strategy based on the reference function. These measures result in a new algorithm with improved efficiency and robustness, as evidenced by our empirical evaluation.