Forecasting the U.S. real house price index

Plakandaras, Vasilios; Gupta, Rangan; Gogas, Periklis; Papadimitriou, Theophilos

doi:10.1016/j.econmod.2014.10.050

Cited by 86 publications

(41 citation statements)

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“…SVR is a direct extension of the classic support vector machine algorithm. The specific machine‐learning methodology has attracted significant interest in forecasting economic and financial time series (Rubio et al ., ; Härdle et al ., ; Öğüt et al ., ; Khandani et al ., ; Plakandaras et al ., ). The algorithm proposed by Vapnik et al .…”

Section: Methodology and Datamentioning

confidence: 97%

The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach

Gogas

Papadimitriou

Plakandaras

et al. 2016

Journal of Forecasting

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The difficulty in modelling inflation and the significance in discovering the underlying data‐generating process of inflation is expressed in an extensive literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting US inflation based on autoregressive and structural models of the term structure. We employ two nonlinear methodologies: the econometric least absolute shrinkage and selection operator (LASSO) and the machine‐learning support vector regression (SVR) method. The SVR has never been used before in inflation forecasting considering the term spread as a regressor. In doing so, we use a long monthly dataset spanning the period 1871:1–2015:3 that covers the entire history of inflation in the US economy. For comparison purposes we also use ordinary least squares regression models as a benchmark. In order to evaluate the contribution of the term spread in inflation forecasting in different time periods, we measure the out‐of‐sample forecasting performance of all models using rolling window regressions. Considering various forecasting horizons, the empirical evidence suggests that the structural models do not outperform the autoregressive ones, regardless of the model's method. Thus we conclude that the term spread models are not more accurate than autoregressive models in inflation forecasting. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Methodology and Datamentioning

confidence: 97%

The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach

Gogas

Papadimitriou

Plakandaras

et al. 2016

Journal of Forecasting

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“…The Support Vector Regression (SVR) stems from the research field of machine learning and it is a direct extension of the Support Vector Machine algorithm. The ability of the SVR in better describing nonlinear and nonstationary phenomena than other econometric techniques has gained the interest of many researchers in economics and finance ( for more details see [24][25][26][27]). The technique was proposed by Cortes and Vapnik in [28] and it attempts to describe actual data observations based on a function where the errors terms do not play any role and are not taken into account as long as they do not violate a predefined threshold ε; only errors higher than ε are penalized.…”

Section: Support Vector Regressionmentioning

confidence: 99%

The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach

2018

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An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several assets: oil prices, exchange rates, national stock indices, and the price of gold. In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead. Our empirical findings suggest that geopolitical events in emerging countries are of little importance to the global economy, since their effect on the assets examined is mainly transitory and only of regional importance. In contrast, gold prices seem to be affected by fluctuation in geopolitical risk. This finding may be justified by the nature of investments in gold, in that they are typically used by economic agents to hedge risk.

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“…where w is the weight vector and b is the bias (Figure 1). The selection of the SVM methodology is motivated by the superior forecasting ability of the methodology, which has been reported in the relevant literature, in forecasting economic and financial variables (see among others [15,16]). Thus, the innovation of our paper stems from the application of a state-of-the-art machine learning methodology and the empirical recognition of a causal relationship between variables reported in the literature and oil prices.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Oil Market Efficiency under a Machine Learning Perspective

et al. 2018

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Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables that are often used in the relevant literature. Next, through a selection process, we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

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Forecasting the U.S. real house price index

Abstract: The 2006 forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.

Cited by 86 publications

References 50 publications

The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach

The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach

The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach

Oil Market Efficiency under a Machine Learning Perspective

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