Forecasting house-price growth in the Euro area with dynamic model averaging

Risse, Marian; Kern, Martin

doi:10.1016/j.najef.2016.08.001

Cited by 38 publications

(23 citation statements)

References 42 publications

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“…Finally, it is worth to notice that DMA was already applied to several markets. Except the already mentioned oil market, this method was used in forecasting gold price [149][150][151], cooper price [152], carbon market [153], inflation [154][155][156][157][158][159], GDP [158,159], real estate markets [160][161][162], exchange rates [163,164] and stock markets [151,[165][166][167][168]. It is also clear that this method has gained an increasing interest in economics since 2015.…”

Section: Policy Uncertaintymentioning

confidence: 99%

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Drachal

2020

Signals

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The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance.

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Section: Policy Uncertaintymentioning

confidence: 99%

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Drachal

2020

Signals

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“…For instance, going back to the generalized Phillips curve mentioned above, there are a myriad of theories that suggest a link between variables, such as the unemployment rate, T-bill rates, level of economic activity, house prices, and the rate of inflation. Therefore, the practitioner is faced with the situation, where he (she) the purpose of our study is not to list every single publication (or working paper) that applies DMA in one way or another, we can list the following interesting applications: Dangl and Halling (2012), Liu et al (2015), and Naser and Alaali (2018) with regard to predicting aggregate equity returns; Koop and Tole (2013) in the context of forecasting the spot price of carbon permits; Buncic and Moretto (2015), Drachal (2016), and Naser (2016) with regard to predicting commodity prices; Bruyn et al (2015), Beckmann and Schüssler (2016), Byrne et al (2018), and Beckmann et al (2020) in the context of forecasting exchange rates; Gupta et al (2014) with regard to forecasting foreign exchange reserves; Bork and Møller (2015), Risse and Kern (2016), and Wei and Cao (2017) in the context of forecasting house price changes; Aye et al (2015) and Baur et al (2016) with regard to predicting the rate of return on the price of gold; Koop and Korobilis (2011) and Filippo (2015) with regard to forecasting non-U.S. rate of inflation; Byrne et al (2017) with respect to forecasting the term structure of government bond yields; and Wang et al (2016), Liu et al (2017), Nonejad (2017b), and Ma et al (2018) with respect to forecasting equity return and commodity price volatility.…”

Section: Introductionmentioning

confidence: 99%

“…(2020) in the context of forecasting exchange rates; Gupta et al . (2014) with regard to forecasting foreign exchange reserves; Bork and Møller (2015), Risse and Kern (2016), and Wei and Cao (2017) in the context of forecasting house price changes; Aye et al . (2015) and Baur et al .…”

Section: Introductionmentioning

confidence: 99%

An Overview of Dynamic Model Averaging Techniques in Time‐series Econometrics

Nonejad¹

2021

Journal of Economic Surveys

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Dynamic model averaging (DMA) has become a widely used estimation technique in macroeconomic applications. Since its introduction in econom(etr)ics by Gary Koop and Dimitris Korobilis in 2009, applications of DMA have increased in unimaginable ways. Besides applying the original (univariate) framework suggested by Koop and Korobilis on the data of interest, for example, the inflation rate of the country of choice or return on the rate of equity, practitioners have been able to use DMA-based techniques to extend current models, thereby further improving out-of-sample forecast accuracy, overcome computational bottlenecks, and even help improve our understanding of economic phenomena by introducing new models. These include using Google search data in combination with the predictive likelihood to govern switching between different predictive regressions in the model set or specifying large time-varying parameter vector autoregressions that can be estimated without resorting to simulation-based techniques. This study provides an overview of DMA techniques and the ways in which they have evolved since the contribution of Koop and Korobilis.

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“…In fact, the uncontrollable housing prices may have a negative effect on the long-term macro-economic development. The boom-and-bust cycles in housing prices may significantly harm economic stability, which became evident during the Subprime Mortgage Crisis of 2007/2008 [6]. During times of economic growth, an increasing demand for housing pushes up investment in residential properties and employment, and thus reinforces aggregate demand.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development

2019

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The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions.

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Forecasting house-price growth in the Euro area with dynamic model averaging

Cited by 38 publications

References 42 publications

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

An Overview of Dynamic Model Averaging Techniques in Time‐series Econometrics

Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development

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