A Simple Linear Regression Approach to Modeling and Forecasting Mortality Rates

Observing that a sequence of negative logarithms of 1‐year survival probabilities displays a linear relationship with the sequence of corresponding terms with a time lag of a certain number of years, we propose a simple linear regression to model and forecast mortality rates. Our model assuming the linearity between two mortality sequences with a time lag each other does not need to formulate the time trends of mortality rates across ages for mortality prediction. Moreover, the parameters of our model for a given age depend on the mortality rates for that age only. Therefore, whether the span of the study ages with the age included is widened or shortened will not affect the results of mortality fitting and forecasting for that age. In the empirical testing, the regression results using the mortality data for the UK, USA and Japan show a satisfactory goodness of fit, which convinces us of the appropriateness of the linear assumption. Empirical illustrations further show that our model's performances of fitting and forecasting mortality rates are quite satisfactory compared with the existing well‐known mortality models. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting Online Auctions via Self‐Exciting Point Processes

Modeling online auction prices is a popular research topic among statisticians and marketing analysts. Recent research mainly focuses on two directions: one is the functional data analysis (FDA) approach, in which the price–time relationship is modeled by a smooth curve, and the other is the point process approach, which directly models the arrival process of bidders and bids. In this paper, a novel model for the bid arrival process using a self‐exciting point process (SEPP) is proposed and applied to forecast auction prices. The FDA and point process approaches are linked together by using functional data analysis technique to describe the intensity of the bid arrival point process. Using the SEPP to model the bid arrival process, many stylized facts in online auction data can be captured. We also develop a simulation‐based forecasting procedure using the estimated SEPP intensity and historical bidding increment. In particular, prediction interval for the terminal price of merchandise can be constructed. Applications to eBay auction data of Harry Potter books and Microsoft Xbox show that the SEPP model provides more accurate and more informative forecasting results than traditional methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis

We transform financial return series into its frequency and time domain via wavelet decomposition to separate short‐run noise from long‐run trends and assess the relevance of each frequency to value‐at‐risk (VaR) forecast. Furthermore, we analyze financial assets in calm and turmoil market times and show that daily 95% VaR forecasts are mainly driven by the volatility that is captured by the first scales comprising the short‐run information, whereas more timescales are needed to adequately forecast 99% VaR. As a result, individual timescales linked via copulas outperform classical parametric VaR approaches that incorporate all information available. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting the Effects of a Canada–US Currency Union on Output and Prices: A Counterfactual Analysis

This paper is a counterfactual analysis investigating the consequences of the formation of a currency union for Canada and the USA: whether outputs increase and prices decrease if these countries form a currency union. We use a two‐country cointegrated model to conduct the counterfactual analysis, where the conditional forecasts are generated based on the Gaussian assumption. To deal with structural breaks and model uncertainty, conditional forecasts are generated from different models/estimation windows and the model‐averaging technique is used to combine the forecasts. We also examine the robustness of our results to parameter uncertainty using the wild bootstrap method. The results show that forming the currency union would probably boost the Canadian economy, whereas it would not have significant effects on US output or Canadian and US price levels. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach

This paper compares the in‐sample fitting and the out‐of‐sample forecasting performances of four distinct Nelson–Siegel class models: Nelson–Siegel, Bliss, Svensson, and a five‐factor model we propose in order to enhance the fitting flexibility. The introduction of the fifth factor resulted in superior adjustment to the data. For the forecasting exercise the paper contrasts the performances of the term structure models in association with the following econometric methods: quantile autoregression evaluated at the median, VAR, AR, and a random walk. As a pattern, the quantile procedure delivered the best results for longer forecasting horizons. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting Simultaneously High‐Dimensional Time Series: A Robust Model‐Based Clustering Approach

This paper considers the problem of forecasting high‐dimensional time series. It employs a robust clustering approach to perform classification of the component series. Each series within a cluster is assumed to follow the same model and the data are then pooled for estimation. The classification is model‐based and robust to outlier contamination. The robustness is achieved by using the intrinsic mode functions of the Hilbert–Huang transform at lower frequencies. These functions are found to be robust to outlier contamination. The paper also compares out‐of‐sample forecast performance of the proposed method with several methods available in the literature. The other forecasting methods considered include vector autoregressive models with ∕ without LASSO, group LASSO, principal component regression, and partial least squares. The proposed method is found to perform well in out‐of‐sample forecasting of the monthly unemployment rates of 50 US states. Copyright © 2013 John Wiley & Sons, Ltd.     

The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach

This paper applies the GARCH‐MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short‐term and long‐term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low‐frequency macroeconomic information in the GARCH‐MIDAS model improves the prediction ability of the model, particularly for the long‐term variance component. Moreover, the GARCH‐MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright © 2013 John Wiley & Sons, Ltd.     

The Relation Between Overreaction in Forecasts and Uncertainty: A Nonlinear Approach

This paper examines overreaction of oil price forecasters. It takes into account impacts of uncertainty, measured by VSTOXX volatility; noisy signals, measured by oil price volatility; and oil price return on forecast changes. The panel smooth transition regression model is applied with different specifications of the transition function to account for nonlinear relations. Data on oil price expectations for different time horizons are taken from the European Central Bank Survey of Professional Forecasters. The results show that forecast changes are governed by overreaction. However, overreaction is markedly reduced when high levels of uncertainty prevail. On the other hand, noisy signals and positive oil price return tend to cause higher overreaction. Copyright © 2016 John Wiley & Sons, Ltd.     

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

We employ 47 different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out-of-sample predictions. The algorithms, which are specified in both single- and multi-equation frameworks, consist of traditional time series models, machine learning (ML) procedures, and deep learning neural networks. A method is adopted to compute iterated multistep forecasts from nonlinear ML specifications. While the rankings of forecast accuracy depend on the length of the forecast horizon, as well as on the choice of the dependent variable (log price or growth rate), a few generalizations can be made. For one- and two-quarter-ahead forecasts we find a large number of algorithms that outperform the random walk with drift benchmark. We also report several such outperformances at longer horizons of four and eight quarters, although these are not statistically significant at any conventional level. Six of the eight top forecasts (4 horizons × 2 dependent variables) are generated by the same algorithm, namely a linear support vector regressor (SVR). The other two highest ranked forecasts are produced as simple mean forecast combinations. Linear autoregressive moving average and vector autoregression models produce accurate olne-quarter-ahead predictions, while forecasts generated by deep learning nets rank well across medium and long forecast horizons.     

Measuring large‐scale market responses and forecasting aggregated sales: Regression for sparse high‐dimensional data

In this article, we propose a regression model for sparse high‐dimensional data from aggregated store‐level sales data. The modeling procedure includes two sub‐models of topic model and hierarchical factor regressions. These are applied in sequence to accommodate high dimensionality and sparseness and facilitate managerial interpretation. First, the topic model is applied to aggregated data to decompose the daily aggregated sales volume of a product into sub‐sales for several topics by allocating each unit sale (“word” in text analysis) in a day (“document”) into a topic based on joint‐purchase information. This stage reduces the dimensionality of data inside topics because the topic distribution is nonuniform and product sales are mostly allocated into smaller numbers of topics. Next, the market response regression model for the topic is estimated from information about items in the same topic. The hierarchical factor regression model we introduce, based on canonical correlation analysis for original high‐dimensional sample spaces, further reduces the dimensionality within topics. Feature selection is then performed on the basis of the credible interval of the parameters' posterior density. Empirical results show that (i) our model allows managerial implications from topic‐wise market responses according to the particular context, and (ii) it performs better than do conventional category regressions in both in‐sample and out‐of‐sample forecasts.     

Forecasting Inflation Across Euro Area Countries and Sectors: A Panel VAR Approach

In this paper, we adopt a panel vector autoregressive (PVAR) approach to estimating and forecasting inflation dynamics in four different sectors—industry, services, construction and agriculture—across the euro area and its four largest member states: France, Germany, Italy and Spain. By modelling inflation together with real activity, employment and wages at the sectoral level, we are able to disentangle the role of unit labour costs and profit margins as the fundamental determinants of price dynamics on the supply side. In out‐of‐sample forecast comparisons, the PVAR approach performs well against popular alternatives, especially at a short forecast horizon and relative to standard VAR forecasts based on aggregate economy‐wide data. Over longer forecast horizons, the accuracy of the PVAR model tends to decline relative to that of the univariate alternatives, while it remains high relative to the aggregate VAR forecasts. We show that these findings are driven by the event of the Great Recession. Our qualitative results carry over to a multi‐country extension of the PVAR approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach

We present and apply singular spectrum analysis (SSA), a relatively new, non‐parametric and data‐driven method for signal extraction (trends, seasonal and business cycle components) and forecasting of UK tourism income. Our results show that SSA slightly outperforms SARIMA and time‐varying‐parameter state space models in terms of root mean square error, mean absolute error and mean absolute percentage error forecasting criteria. Copyright © 2011 John Wiley & Sons, Ltd.     

A Method of Retail Mortgage Stress Testing: Based on Time‐Frame and Magnitude Analysis

In this study, a non‐stationary Markov chain model and a vector autoregressive moving average with exogenous variables coupled with a logistic function (VARMAX‐L) are used to analyze and predict the stability of a retail mortgage portfolio, based on the stress test framework. The method introduced in this paper can be used to forecast the transition probabilities in a retail mortgage over pre‐specified states, given a shock with a certain magnitude. Hence this method provides a dynamic picture of the portfolio transition process through which one can assess its behavior over time. While the paper concentrates on retail mortgages, the methodology of this study can be adapted also to analyze other credit products in banks. Copyright © 2015 John Wiley & Sons, Ltd.