Daniel Melser
Monash University
44 Papers
150 Citations
Daniel Melser is an academic researcher from Monash University. The author has contributed to research in topics: Price index & Hedonic regression. The author has an hindex of 9, co-authored 40 publications. Previous affiliations of Daniel Melser include University of New South Wales & Monash University, Caulfield campus.
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Papers
The impact of immigration on housing prices in Australia
Morteza Moallemi,Daniel Melser +1 more
TL;DR: In this article, the authors investigated the effect of immigration on Australian housing prices at the postcode level and found that an immigrant inflow of 1% of a postcode's population raises housing prices by around 0.9% per year.
41
Eco-labelling and the Trade-Environment Debate
Daniel Melser,Peter E. Robertson +1 more
TL;DR: In this paper, the potential global environmental benefits of eco-labelling programs, paying attention in particular to internationally traded commodities, are considered, where the authors argue that in some instances the impetus behind the introduction of ecolabels is that they are seen as an alternative to more trade restrictive environmental policies, such as import bans or tariffs on goods with harmful environmental effects.
30
Accounting for the effects of new and disappearing goods using scanner data
TL;DR: In this article, the authors used the Constant Elasticity of Substitution cost function to calculate the exact cost-of-living index even when the domain of goods is changing over time.
26
Life Cycle Price Trends and Product Replacement: Implications for the Measurement of Inflation
Daniel Melser,Iqbal A. Syed +1 more
TL;DR: In this paper, the authors explored the extent to which products follow systematic pricing patterns over their life cycle and the impact this has on the measurement of inflation and found that the life cycle bias leads to the underestimation of inflation by around 0.30 percentage points each year for the products examined.
22
Scanner Data Price Indexes: Addressing Some Unresolved Issues
TL;DR: In this article, a novel linking method is proposed along with the use of weighted GEKS as opposed to a fixed window, which is illustrated empirically on a large scanner dataset and perform well.