Underlying Inflation: Concepts, Measurement and Performance

This paper explores the concept of underlying inflation and the properties of various measures of underlying inflation in the Australian context. Underlying inflation measures are routinely calculated and monitored by central banks in many countries, including the Reserve Bank of Australia. Alternative measurement concepts are explored, and a range of measures that have been calculated for Australia are discussed and evaluated on the basis of statistical criteria. These criteria capture the intuition that a good measure of underlying inflation should be less volatile than CPI (or headline) inflation, be unbiased with respect to CPI inflation, and capture the ‘trend’ in CPI inflation so that, on average, CPI inflation will tend to adjust towards the measure of underlying inflation. In the Australian context, statistical measures of underlying inflation, such as the trimmed mean or weighted median, perform fairly satisfactorily against these criteria. The performance of these measures can be further improved by seasonally adjusting prices at the CPI component level. Although underlying inflation measures have become less necessary in the past decade as inflation itself has become less volatile, these findings suggest that underlying inflation measures can still add value to the analysis of inflationary trends.underlying inflation; core inflation; trimmed mean; weighted median; volatility-weighted measures

Sources of Chinese Demand for Resource Commodities

Is China’s demand for resources driven predominantly by domestic factors or by global demand for its exports? The answer to this question is important for many resource-exporting countries, such as Australia, Brazil, Canada and India. This paper provides evidence that China’s (mainly manufacturing) exports have been a significant driver of its demand for resource commodities over recent decades. First, it employs input-output tables to demonstrate that, historically, manufacturing has been at least as important as construction as a driver of China’s demand for resource-intensive metal products. Second, it shows that global trade in non-oil resource commodities can be described by the gravity model of trade. Using this model it is found that, controlling for domestic expenditure (including investment), exports are a sizeable and significant determinant of a country’s resource imports, and that this has been true for China as well as for other countries.China; trade; investment; resource commodities; gravity model

China's Steel Industry

China's steel industry has grown rapidly in recent decades, with China now the world's largest producer and consumer of steel. This has resulted in a sharp increase in demand for iron ore and coal, Australia's two largest exports, which are key inputs for the steelmaking process. This article discusses the growth of the Chinese steel industry over the past couple of decades.China; steel industry; Chinese steel industry; iron ore; coking coal

China’s Exchange Rate Policy: The Case For Greater Flexibility

Since the Asian crisis, the merit of the Chinese government’s de facto peg to the US dollar has been the subject of widening debate. This paper reviews the issues surrounding China’s currency regime choice and assesses the case for greater flexibility. Reform era exchange rate policies are examined along with the performance of the economy during and since the Asian crisis. In the Chinese context the arguments for and against fixed exchange rates are then explained and assessed. Finally, an elemental comparative static macroeconomic model is used to examine the implications of domestic and external shocks under different exchange rate regimes and with differing degrees of capital mobility. The results support the view that more flexibility would be beneficial to China and that this benefit can be expected to increase as capital mobility increases

Mosquito detection with low-cost smartphones: data acquisition for malaria research

Mosquitoes are a major vector for malaria, causing hundreds of thousands of deaths in the developing world each year. Not only is the prevention of mosquito bites of paramount importance to the reduction of malaria transmission cases, but understanding in more forensic detail the interplay between malaria, mosquito vectors, vegetation, standing water and human populations is crucial to the deployment of more effective interventions. Typically the presence and detection of malaria-vectoring mosquitoes is only quantified by hand-operated insect traps or signified by the diagnosis of malaria. If we are to gather timely, large-scale data to improve this situation, we need to automate the process of mosquito detection and classification as much as possible. In this paper, we present a candidate mobile sensing system that acts as both a portable early warning device and an automatic acoustic data acquisition pipeline to help fuel scientific inquiry and policy. The machine learning algorithm that powers the mobile system achieves excellent off-line multi-species detection performance while remaining computationally efficient. Further, we have conducted preliminary live mosquito detection tests using low-cost mobile phones and achieved promising results. The deployment of this system for field usage in Southeast Asia and Africa is planned in the near future. In order to accelerate processing of field recordings and labelling of collected data, we employ a citizen science platform in conjunction with automated methods, the former implemented using the Zooniverse platform, allowing crowdsourcing on a grand scale.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the Developing Worl

What do Sentiment Surveys Measure?

Indices of business and consumer sentiment receive widespread media coverage and are closely watched by market economists despite their limited success as leading indicators. In this paper we ask what explains ‘sentiment’ and find that lagged economic indicators (such as changes in GDP, job vacancies and the cash rate) can explain a substantial proportion of the variation in a number of backward and forward-looking sentiment indices. This does not rule out the possibility that they may be useful for forecasting. We find, however, that when currently available economic information is appropriately ‘filtered’ from the sentiment indices, in most cases they fail even rudimentary Granger-causality tests of predictive ability. On a more positive note, we find that the Roy Morgan consumer confidence rating, NAB actual business conditions, NAB expected employment outlook over the next three months and the second question in the Roy Morgan and Westpac/MI consumer surveys all provide some, albeit small, contribution to forecasting employment growth. The second question of both consumer confidence surveys (which asks about anticipated personal financial conditions over the coming year) also appears to have some ability to predict recessions. Outside of these results there is little evidence that the surveys tell us anything we didn’t already know. Thus, there is reason to suspect that surveyed respondents’ forecasts offer little more information about the future path of the economy than a weighted average of lagged economic variables.business; confidence; consumer; forecasting; predictive ability; sentiment; survey

A Select Bibliography of Published Research by Staff of the Reserve Bank of Australia: 1991–2001

This bibliography provides a list of the major items of published research produced by staff of the Reserve Bank of Australia since 1991. It is a sequel to the bibliography compiled by Suzanna Chiang and Michael Power (1990), which listed work published over the period 1969–1990. The bibliography includes Research Discussion Papers, Occasional Papers, Conference Volumes, and other publications in books and economics journals.bibliography

Mosquito Detection with Neural Networks: The Buzz of Deep Learning

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.Comment: For data and software related to this paper, see http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201