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on Forecasting |
| By: | Dimitrios Papastamos; Antonis Alexandridis; Dimitris Karlis |
| Abstract: | In recent years big financial institutions are interested in creating and maintaining property valuation models. The main objective is to use reliable historical data in order to be able to forecast the price of a new property in a comprehensible manner and provide some indication for the uncertainty around this forecast. In this paper we develop an automatic valuation model for property valuation using a large database of historical prices from Greece. The Greek property market is an inefficient, non- homogeneous market, still at its infancy governed by lack of information. As a result modelling the Greek real estate market is a very challenging problem. The available data cover a big range of properties across time and include the financial crisis period in Greece which led to tremendous changes in the dynamics of the real estate market. We formulate and compare linear and non-linear models based on regression, hedonic equations and artificial neural networks. The forecasting ability of each method is evaluated out-of-sample. Special care is given on measuring the success of the forecasts but also to identify the property characteristics that lead to large forecasting errors. Finally, by examining the strengths and the performance of each method we apply a combined forecasting rule to improve performance. Our results indicate that the proposed methodology constitutes an accurate tool for property valuation in non- homogeneous, newly developed markets. |
| Keywords: | Artificial Neural Networks; Automated Valuation Models; Forecasting Accuracy; Residential Market; Valuations |
| JEL: | R3 |
| Date: | 2017–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2017_119&r=for |
| By: | Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Wasiu Adekunle (Centre for Econometric and Allied Research, University of Ibadan); Zachariah Emmanuel (Centre for Econometric and Allied Research, University of Ibadan Department of Economics, Federal University Wukari, Taraba State, Nigeria); Wasiu A. Alimi (Centre for Econometric and Allied Research, University of Ibadan) |
| Abstract: | In this paper, we offer new evidence on the predictability of exchange rate with commodity prices by accounting for the role of asymmetries and structural breaks. In particular, we evaluate whether such considerations matter for the forecast performance of the predictive model for exchange rate. We further account for any possible bias in estimation due to the presence of persistence, endogeneity and conditional heteroscedasticity effects in our predictors. Monthly data of five major tradable currency pairs in the world and disaggregated commodity price indices over the period of 1960 to 2017 are utilized. We find significant improvements in both the in-sample and out-of-sample forecast performance of the predictive model for exchange rate when asymmetries and structural breaks are accommodated. In addition, all the economic models considered with and without asymmetries and structural breaks offer superior forecast performance over the ARFIMA model. Our results are robust to alternative exchange rates and commodity price indices and different breaks, data samples and forecast horizons. |
| Keywords: | Exchange rate; Commodity prices; Forecast evaluation, Asymmetry, Structural break |
| JEL: | F31 F37 Q02 |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:cui:wpaper:0055&r=for |
| By: | Marcin Kolasa (SGH Warsaw School of Economics and Narodowy Bank Polski); Michał Rubaszek (SGH Warsaw School of Economics) |
| Abstract: | This paper evaluates the forecasting performance of several small open economy DSGE models relative to a closed economy benchmark using a long span of data for Australia, Canada and the United Kingdom. We find that opening the model economy usually does not improve, and even deteriorates the quality of point and density forecasts for key domestic variables. We show that this result can be to a large extent attributed to an increase in forecast error due to a more sophisticated structure of the extended setup which is not compensated by better model specification. This claim is based on a Monte Carlo experiment, in which an open economy model fails to consistently beat its closed economy benchmark even if the former is the true data generating process. |
| Keywords: | Forecasting, DSGE models, New Open Economy Macroeconomics, Bayesian estimation |
| JEL: | D58 E17 F41 F47 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:nbp:nbpmis:282&r=for |
| By: | Jonas Dovern (Alfred-Weber-Institute for Economics, Heidelberg University); Hans Manner (University of Graz, Austria) |
| Abstract: | Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to established approaches. We use the tests to evaluate GARCH-based multivariate density forecasts for a vector of stock market returns. |
| Keywords: | Density calibration; Goodness-of-fit test; Predictive density; Rosenblatt transformation |
| JEL: | C12 C32 C52 C53 |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:grz:wpaper:2018-09&r=for |
| By: | Artur Tarassow; Sven Schreiber |
| Abstract: | The FEP function package for the gretl program is a collection of functions for computing different types of forecast evaluation statistics as well as tests. For ease of use a common scripting interface framework is provided, which is flexible enough to accommodate future additions. Most of the functionality is also accessible through a graphical dialog window interface within gretl. This documentation explains the usage and capabilities as well as providing some econometric background where necessary. An illustration with expert forecasts of euro area growth is also provided. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:imk:wpaper:190-2018&r=for |
| By: | Sebastián M. Palacio (GiM, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona) |
| Abstract: | Public transport smart cards are widely used around the world. However, while they provide information about various aspects of passenger behavior, they have not been properly exploited to predict demand. Indeed, traditional methods in economics employ linear unbiased estimators that pay little attention to accuracy, which is the main problem faced by the sector’s regulators. This paper reports the application of various supervised machine learning (SML) techniques to smart card data in order to forecast demand, and it compares these outcomes with traditional linear model estimates. We conclude that the forecasts obtained from these algorithms are much more accurate. |
| URL: | http://d.repec.org/n?u=RePEc:xrp:wpaper:xreap2018-3&r=for |
| By: | Danish A. Alvi |
| Abstract: | The dissertation investigates the application of Probabilistic Graphical Models (PGMs) in forecasting the price of Crude Oil. This research is important because crude oil plays a very pivotal role in the global economy hence is a very critical macroeconomic indicator of the industrial growth. Given the vast amount of macroeconomic factors affecting the price of crude oil such as supply of oil from OPEC countries, demand of oil from OECD countries, geopolitical and geoeconomic changes among many other variables - probabilistic graphical models (PGMs) allow us to understand by learning the graphical structure. This dissertation proposes condensing data numerous Crude Oil factors into a graphical model in the attempt of creating a accurate forecast of the price of crude oil. The research project experiments with using different libraries in Python in order to construct models of the crude oil market. The experiments in this thesis investigate three main challenges commonly presented while trading oil in the financial markets. The first challenge it investigates is the process of learning the structure of the oil markets; thus allowing crude oil traders to understand the different physical market factors and macroeconomic indicators affecting crude oil markets and how they are \textit{causally} related. The second challenge it solves is the exploration and exploitation of the available data and the learnt structure in predicting the behaviour of the oil markets. The third challenge it investigates is how to validate the performance and reliability of the constructed model in order for it to be deployed in the financial markets. A design and implementation of a probabilistic framework for forecasting the price of crude oil is also presented as part of the research. |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1804.10869&r=for |
| By: | Franses, Ph.H.B.F. |
| Abstract: | This paper introduces the idea to adjust forecasts from a linear time series model where the adjustment relies on the assumption that this linear model is an approximation of for example a nonlinear time series model. This way to create forecasts can be convenient when inference for the nonlinear model is impossible, complicated or unreliable in small samples. The size of the forecast adjustment can be based on the estimation results for the linear model and on other data properties like the first few moments or autocorrelations. An illustration is given for an ARMA(1,1) model which is known to approximate a first order diagonal bilinear time series model. For this case, the forecast adjustment is easy to derive, which is convenient as the particular bilinear model is indeed cumbersome to analyze. An application to a range of inflation series for low income countries shows that such adjustment can lead to improved forecasts, although the gain is not large nor frequent |
| Keywords: | ARMA(1, 1), Inflation, First-order diagonal bilinear time series model, Methods, of Moments, Adjustment of forecasts |
| JEL: | C22 C53 |
| Date: | 2018–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:105879&r=for |