The Response of Housing Construction to a Copper Price Shock in Chile (2009–2020)
Open Access
- 28 June 2021
- Vol. 9 (3), 98
- https://doi.org/10.3390/economies9030098
Abstract
The copper price is a leading indicator of real estate activity. Price increases are statistically related to increasing numbers of applications for residential building permits. However, this reciprocity is not instantaneous as permit numbers lag price rises by 9 to 10 months. This dynamic is implicit in various transmission channels: from the first effects on investment plans and demand for durable goods due to better expectations from investors and consumers to the real impact of higher copper revenues on the economy’s aggregate production and demand (multiplier or second-round effect). In this paper, we proposed the impulse-response functions of a vector autoregressive model to capture the dynamic between the copper price and house building permits. Therefore, it would be expected that the recent copper price increase will boost construction and real estate activity. The effects could materialize this year and extend into early 2022.Funding Information
- Fondo Nacional de Desarrollo Científico y Tecnológico (11190116)
This publication has 16 references indexed in Scilit:
- Backcasting and forecasting time series using detrended cross-correlation analysisPhysica A: Statistical Mechanics and its Applications, 2020
- Copper Price Variation Forecasts Using Genetic AlgorithmsPublished by Springer Science and Business Media LLC ,2020
- Asymmetric heavy-tailed vector auto-regressive processes with application to financial dataJournal of Statistical Computation and Simulation, 2020
- Backcasting cement production and characterizing cement’s economic cycles for Chile 1991–2015Empirical Economics, 2018
- Statistical analysis of autoregressive fractionally integrated moving average models in RComputational Statistics, 2013
- An analysis of choice criteria in the home loans marketInternational Journal of Bank Marketing, 2002
- Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root HypothesisJournal of Business & Economic Statistics, 2002
- Calculating and analyzing impulse responses for the vector ARFIMA modelEconomics Letters, 2001
- Testing for a unit root in time series regressionBiometrika, 1988
- A new look at the statistical model identificationIEEE Transactions on Automatic Control, 1974