Forecasting Australia's real house price index: A comparison of time series and machine learning methods

Milunovich, George

doi:10.1002/for.2678

Cited by 48 publications

(34 citation statements)

References 57 publications

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“…Ng (2014), Döpke et al (2017), and Medeiros et al (2019) improve forecast accuracy with random forests and boosting, while Yousuf and Ng (2019) use boosting for high-dimensional predictive regressions with time varying parameters. Others compare machine learning methods in horse races (Ahmed et al, 2010;Chen et al, 2019;Kim & Swanson, 2018;Li & Chen, 2014;Milunovich, 2020;Smeekes & Wijler, 2018;Stock & Watson, 2012b). Works such as Joseph (2019) and Zhao and Hastie (2019) contribute to the interpretability of a given model.…”

Section: Introductionmentioning

confidence: 99%

“…Ng (2014), Döpke et al (2017) and Medeiros et al (2019) improve forecast accuracy with random forests and boosting, while Yousuf and Ng (2019) use boosting for high-dimensional predictive regressions with time varying parameters. Others compare machine learning methods in horse races (Ahmed et al, 2010;Stock and Watson, 2012b;Li and Chen, 2014;Kim and Swanson, 2018;Smeekes and Wijler, 2018;Chen et al, 2019;Milunovich, 2020).…”

Section: Introductionmentioning

confidence: 99%

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How is machine learning useful for macroeconomic forecasting?

Coulombe

Leroux

Stevanović

et al. 2022

J of Applied Econometrics

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We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by adding the how. The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. To the contrary, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so-called features (nonlinearities, regularization, cross-validation, and alternative loss function) and study their behavior in both the data-rich and data-poor environments. To do so, we design experiments that allow to identify the "treatment" effects of interest. We conclude that (i) nonlinearity is the true game changer for macroeconomic prediction, (ii) the standard factor model remains the best regularization, (iii) K-fold cross-validation is the best practice, and (iv) the L 2 is preferred to the ε-insensitive in-sample loss. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, financial stress and housing bubble bursts. Furthermore, ML nonlinearities are helpful when considering density forecasts. KEYWORDSbig data, forecasting, machine learning *** 1% significance of Diebold-Mariano test. ** 5% significance of Diebold-Mariano test. * 10% significance of Diebold-Mariano test. * p < 0.05. ** p < 0.01.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How is machine learning useful for macroeconomic forecasting?

Coulombe

Leroux

Stevanović

et al. 2022

J of Applied Econometrics

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show abstract

“…7 A related literature considers the ability of machine learning and time series models to forecast house prices. See, e.g., Milunovich (2020 Little prior work uses the highly geographically disaggregated Australian data adopted in the current paper to study heterogeneous effects across housing markets. However, La Cava and He (2021) makes progress in this direction.…”

Section: (I) Related Literaturementioning

confidence: 99%

House Prices, Monetary Policy and Commodities: Evidence from Australia*

Graham

Read

2022

Economic Record

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Monetary policy and commodity prices are key drivers of Australian house prices, but endogeneity between these variables complicates empirical research. To address this, we use local projection methods and instrumental variables for estimation. We find that one standard deviation expansionary monetary and commodity shocks increase house prices at peak by 1.5 per cent and 1.4 per cent, respectively. Using geographically disaggregated panel data, we exploit the heterogeneity in house price responses to study the different channels through which these shocks affect housing markets. We find that both monetary policy and commodity price shocks have significant effects on housing markets through income channels.

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“…Results of numerous empirical studies have shown that ML approaches outperform time series models in forecasting different financial assets [ 13 ]. A comparative analysis of statistical models and machine learning techniques can be found in [ 14 ]. Among the ML techniques, Artificial Neural Network (ANN), Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) etc.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

et al. 2022

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Background Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. Methods In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. Results An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. Conclusions Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

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Forecasting Australia's real house price index: A comparison of time series and machine learning methods

Cited by 48 publications

References 57 publications

How is machine learning useful for macroeconomic forecasting?

How is machine learning useful for macroeconomic forecasting?

House Prices, Monetary Policy and Commodities: Evidence from Australia*

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

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