Forecasting house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.     

Forecasting house prices for the four census regions and the aggregate US economy in a data-rich environment

This article considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated by dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:01–2009:03, based on an in-sample of 1968:02–1994:12, relative to a random walk model, a small-scale VAR model comprising just the five real house price growth rates and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the five real house price growth rates. In addition to the forecast comparison exercise across small-, medium- and large-scale models, we also look at the ability of the ‘optimal’ model (i.e. the model that produces the minimum average mean squared forecast error) for a specific region in predicting ex ante real house prices (in levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or Bayesian) perform the best for the North East, Mid-West, West census regions and the aggregate US economy and equally well to a small-scale VAR for the South region. The ‘optimal’ factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.     

Modelling prices and the reputation of individual named wines

This article develops empirical models to assess the relation between the reputation of an individual named wine and its price. Unrestricted and polynomial distributed lag models are used to assess the impact of past expert quality ratings on the prices of Australian premium wines. Results point to the practical unimportance of current wine quality scores impacting prices and suggest that quality score lag effects up to six years may be important. The largest individual lagged impact of quality on price is estimated to occur at approximately two years, and prices are estimated to increase by more than 10% over six years for a one-point quality score increase. A procedure for identifying potential wine price bargains based on a comparison of price predictions from estimated wine reputation and current quality measures is illustrated. The implications of the findings for wine producers are also discussed.     

Forecasting electricity spot prices using time-series models with a double temporal segmentation

The French wholesale market is set to expand in the next few years under European pressure and national decisions. In this article, we assess the forecasting ability of several classes of time-series models for electricity wholesale spot prices at a day-ahead horizon in France. Electricity spot prices display a strong seasonal pattern, particularly in France, given the high share of electric heating in housing during winter time. To deal with this pattern, we implement a double temporal segmentation of the data. For each trading period and season, we use a large number of specifications based on market fundamentals: linear regressions, Markov-switching (MS) models and threshold models with a smooth transition. An extensive evaluation on French data shows that modelling each season independently leads to better results. Among nonlinear models, MS models designed to capture the sudden and fast-reverting spikes in the price dynamics yield more accurate forecasts. Finally, pooling forecasts give more reliable results.     

Predicting bid prices by using machine learning methods

It is well-known that empirical analysis suffers from multicollinearity and high dimensionality. In particular, this is much more severe in an empirical study of itemized bids in highway procurement auctions. To overcome this obstacle, this article employs the regularized linear regression for the estimation of a more precise interval for project winning bids. The approach is put to the test using empirical data of highway procurement auctions in Vermont. In our empirical analysis, we first choose a set of crucial tasks that determine a bidder’s bid amounts by using the random forest variable selection method. Given the selected tasks, project bid forecasting is conducted. We compare our proposed methodology with the least square linear model based on the bias and the standard root mean square error of the bid estimates. There is evidence supporting that the suggested approach provides superior forecasts for an interval of winning bids over the competing model. As far as we know, this article is the first attempt to provide reference bids of highway construction contracts.     

Optimal forecast evaluation: fertilizer prices

High fertilizer price volatility makes production planning and inventory management difficult, so accurate fertilizer price forecasts would be beneficial. This article evaluates commercial forecasts for urea and diammonium phosphate (DAP) prices based on forecast accuracy and optimal forecast properties. Most forecasts pass the tests, but forecasts for the US New Orleans urea and the US Gulf DAP markets, in particular, do not and thus show potential to be improved.     

Commodity prices and the AUD-Yen exchange rate: a real-time forecasting analysis

I study the impact of the GSCI commodity price indices on the Australian dollar-Japanese yen nominal exchange rate using a modified version of the classic monetary approach of exchange rate determination. I use a broad range of model-selection and model-averaging criteria. I find some evidence for a short-lived relationship as far as inclusions in the optimal forecasting models are concerned. In general, though, results of the Diebold-Mariano and Clark-West test show that results are not stable over the whole sample.     

Forecasting UK house prices: A time varying coefficient approach

Previous studies of UK house prices, developed from the demand and supply ofhousing or from the asset market approach have been poor in terms of robustness and ex-post forecasting ability. The UK housing market has suffered a number of structural changes, particularly since the early 1980s with substantial house price increases, financial market deregulation and the removal of mortgage market constraints through competition. Consequently, models which assume that the underlying data-generating process is stable and apply constant parameter techniques tend to suffer in terms of parameter instability. This article uses the Time Varying Coefficient (TVC) methodology where the underlying data-generating process in the UK housing market is treated as unstable. The estimation results of the TVC regression of UK house prices is compared with those obtained from three alternative constant parameter regressions. Comparisons of forecasting performance suggest the TVC regression out-performs forecasts from an Error Correction Mechanism (ECM), Vector Autoregressive (VAR) and an Autoregressive Time Series regression.     

Forecasting livestock prices with individual and composite methods

We investigate the incentives of private firms to adopt new technologies. Econometric investigation is performed on a pooled sample of individual US airline firms over the period 1971 to 1986 for which extensive information on available jet aircraft technology and fleet choice have been recorded. Given the incidence of successive commercial aircraft innovations and variation in production attributes across firms, we are able to consider a wider array of ‘time-dependent’ and ‘time-independent’ adoption influences than in previous firm-level studies. To the extent that our study provides useful general insights into adoption decisions by firms, the results have implications for US global competitiveness policy. One key finding is that firms subject to increased product market competition exhibit a higher propensity to adopt technological innovations.     

Fixed prices versus predetermined prices and the equilibrium probability of price adjustment

Elsewhere, papers comparing fixed prices with predetermined prices have assumed that the frequency of re-setting price contracts is equal in either case. This note demonstrates that in equilibrium, the frequency of re-setting price contracts is greater with fixed prices than predetermined prices.     

The role of rare earth prices for consumer prices: an ignored factor?

The article explores the role of a new determinant that accounts for driving consumer prices, i.e. rare earth prices. Rare earths are used for many advanced manufacturing and military technologies. Performing a number of panel methodological approaches, panel cointegration findings support the initial insight of the authors and illustrate a positive effect on consumer prices across all rare earths.     

Housing prices,stock prices and the US economy

This paper studies the relationship between housing prices, stock prices, interest rates and aggregate output in the US using monthly data from 1993 to 2014. Evidence from causality tests and a variance decomposition procedure suggest that stock prices have a much larger effect on aggregate output in the US economy than do either housing prices or interest rates. Instead, the wealth effect created by changes in stock prices has a relatively large impact on US aggregate output. Separate estimations and variance decompositions for the sample periods 1993–2001, 2002–2008 and 2009–2014 show that the impact of housing prices relative to stock prices has been waning over time.     

从虚高房价谈宏观调控政策

近十年来,我国主要大中城市的房地产价格快速上涨,房价虚高不下。文章分析了房价虚高,且国家宏观调控收效甚微的原因,提出了不断完善房地产价格调控的方向,以期促进房地产市场健康发展。     

Natural gas prices and stock prices: Evidence from EU-15 countries

This study investigates the long-run relationship between natural gas prices and stock prices by using the Johansen and Juselius cointegration test and error–correction based Granger causality models for the EU-15 countries. We employ quarterly data covering the period from 1990:1 to 2008:1. Empirical findings suggest that there is a unique long-term equilibrium relationship between natural gas prices, industrial production and stock prices in Austria, Denmark, Finland, Germany and Luxembourg. However, no relationship is found between these variables in the other ten EU-15 countries. Although we detect a significant long-run relationship between stock prices and natural gas prices, Granger causality test results imply an indirect Granger causal relationship between these two variables. In addition, we investigate the Granger causal relationship between stock returns, industrial production growth and natural gas price increase for Austria, Denmark, Finland, Germany and Luxembourg. As a result, increase in natural gas prices seem to impact industrial production growth at the first place. In turn, industrial production growth appears to affect stock returns.     

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian consumer inflation (Índice de Preços ao Consumidor Amplo; IPCA). We will compare forecasting models using disaggregated and aggregated data from IPCA over 12 months ahead. We used IPCA in a monthly basis, over the period between January 1996 and March 2012. Out-of-sample analysis will be made through the period of January 2008 to March 2012. The disaggregated models were estimated by Seasonal Autoregressive Integrated Moving Average (SARIMA) and will have different levels of disaggregation from IPCA as groups and items, as well as disaggregation with more economic sense used by Brazilian Central Bank as: (1) services, monitored prices, food and industrials and (2) durables, non-durables, semi-durables, services and monitored prices. Aggregated models will be estimated by time series techniques as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy among models will be made by the selection model procedure known as Model Confidence Set developed by Peter Hansen, Asger Lunde and James Nason. We were able to find evidence of forecast accuracy gains in models using more disaggregated rather than aggregate data.     

Space-time approach to commercial property prices valuation

There exists three ways of approaching real estate prices: the cost approach, the market data approach and the income capitalization approach. In this article, we propose an improvement of the market data approach that takes into account the spatial component. In particular, we propose a modified market data approach based on interpolation, being the structure of the spatial correlation between the prices of properties the main factor to obtain the weights. Interpolation methods have been widely used for estimating real estate prices, but they do not take into account the structure of their spatial dependence. Although this drawback is overcome by kriged estimation, in the case of the prices of commercial properties, they do not provide good estimates because the scarceness of the market information. This is why auxiliary information is needed and cokriging methods are used to obtain estimates that are more accurate. The aim of this article is the comparison of cokriged estimation of premises prices in two different temporal moments in the emblematic old part of Toledo city (Spain), using housing prices as an auxiliary random function due to their strong correlation with the main one. Cokriging, kriging and inverse distance weighting results are compared.     

Competition and production prices

My theorem, the so-called Okishio theorem, is a comparativestatics result. Therefore, it has no realistic meaning if capitalists'competition does not establish a new equilibrium following theintroduction of a new production method. Marx firmly believedthat a new equilibrium was established. In this paper, we investigatethe capitalist process without technical change. If we choosethe proper parameters and initial conditions, all profit ratesconverge to zero, Schumpeter's result.     

Simultaneous use of auctions and posted prices

I consider a model in which several identical objects are sold simultaneously via an auction and a posted price mechanism. The model explains several empirical regularities regarding bidding behavior in eBay auctions such as the finding that some bidders bid multiple times over the course of the auction, and that bidders tend to bid with greater frequency near the end of the auction than the beginning. I also show that sellers prefer to simultaneously use auctions and posted prices than to use either mechanism individually.