Prediction in the one-way error component model with serial correlation

This paper derives the best linear unbiased predictor for a one-way error component model with serial correlation. A transformation derived by Baltagi and Li (1991) is used to show how the forecast can be easily computed from the GLS estimates and residuals. This result is useful for panel data applications which utilize the error component specification and exhibit serial correlation in the remainder disturbance term. Analytical expressions for this predictor are given when the remainder disturbances follow (1) an AR(1) process, (2) an AR(2) process, (3) a special AR(4) process for quarterly data, and (4) an MA(1) process.     

Prediction in the Random Effects Model with MA (q) Remainder Disturbances

This paper considers the problem of forecasting in a panel data model with random individual effects and MA (q) remainder disturbances. It utilizes a recursive transformation for the MA (q) process derived by Baltagi and Li (Econometric Theory 1994; 10 : 396–408) which yields a simple generalized least‐squares estimator for this model. This recursive transformation is used in conjunction with Goldberger's result (Journal of the American Statistical Association 1962; 57 : 369–375) to derive an analytic expression for the best linear unbiased predictor, for the ith cross‐sectional unit, s periods ahead. Copyright © 2011 John Wiley & Sons, Ltd.     

Prediction in a Generalized Spatial Panel Data Model with Serial Correlation

This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (Spatial panels: random components versus fixed effects. International Economic Review 2012; 53 : 1369–1412.), which encompasses many of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as Special Cases. Copyright © 2016 John Wiley & Sons, Ltd.     

Prediction from the One‐Way Error Components Model with AR(1) Disturbances

In this paper we extend the works of Baillie and Baltagi (1999, in Analysis of Panels and Limited Dependent Variables Models, Hsiao C et al. (eds). Cambridge University Press: Cambridge, UK; 255–267) and generalize certain results from the Baltagi and Li (1992, Journal of Forecasting 11 : 561–567) paper accounting for AR(1) errors in the disturbance term. In particular, we derive six predictors for the one‐way error components model, as well as their associated asymptotic mean squared error of multi‐step prediction in the presence of AR(1) errors in the disturbance term. In addition, we also provide both theoretical and simulation evidence as to the relative efficiency of our alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods and indicates that the ordinary optimal predictor performs well for various accuracy criteria. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimating and Predicting the General Random Effects Model

This paper extends the ‘remarkable property’ of Breusch (Journal of Econometrics 1987; 36 : 383–389) and Baltagi and Li (Journal of Econometrics 1992; 53 : 45–51) to the three‐way random components framework. Indeed, like its one‐way and two‐way counterparts, the three‐way random effects model maximum likelihood estimation can be obtained as an iterated generalized least squares procedure through an appropriate algorithm of monotonic sequences of some variance components ratios, θ_i (i = 2, 3, 4). More specifically, a search over θ_iwhile iterating on the regression coefficients estimates β and the other θ_jwill guard against the possibility of multiple local maxima of the likelihood function. In addition, the derivations of related prediction functions are obtained based on complete as well as incomplete panels. Finally, an application to international trade issues modeling is presented. Copyright © 2014 John Wiley & Sons, Ltd.     

Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors

In recent years, factor models have received increasing attention from both econometricians and practitioners in the forecasting of macroeconomic variables. In this context, Bai and Ng (Journal of Econometrics 2008; 146 : 304–317) find an improvement in selecting indicators according to the forecast variable prior to factor estimation (targeted predictors). In particular, they propose using the LARS‐EN algorithm to remove irrelevant predictors. In this paper, we adapt the Bai and Ng procedure to a setup in which data releases are delayed and staggered. In the pre‐selection step, we replace actual data with estimates obtained on the basis of past information, where the structure of the available information replicates the one a forecaster would face in real time. We estimate on the reduced dataset the dynamic factor model of Giannone et al. (Journal of Monetary Economics 2008; 55 : 665–676) and Doz et al. (Journal of Econometrics 2011; 164 : 188–205), which is particularly suitable for the very short‐term forecast of GDP. A pseudo real‐time evaluation on French data shows the potential of our approach. Copyright © 2013 John Wiley & Sons, Ltd.     

On the Predictive Content of Autoregression Residuals: A Semiparametric,Copula‐Based Approach to Time Series Prediction

This paper proposes an adjustment of linear autoregressive conditional mean forecasts that exploits the predictive content of uncorrelated model residuals. The adjustment is motivated by non‐Gaussian characteristics of model residuals, and implemented in a semiparametric fashion by means of conditional moments of simulated bivariate distributions. A pseudo ex ante forecasting comparison is conducted for a set of 494 macroeconomic time series recently collected by Dees et al. (Journal of Applied Econometrics 2007; 22: 1–38). In total, 10,374 time series realizations are contrasted against competing short‐, medium‐ and longer‐term purely autoregressive and adjusted predictors. With regard to all forecast horizons, the adjusted predictions consistently outperform conditionally Gaussian forecasts according to cross‐sectional mean group evaluation of absolute forecast errors and directional accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction in an Unbalanced Nested Error Components Panel Data Model

This paper derives the best linear unbiased predictor for an unbalanced nested error components panel data model. This predictor is useful in many econometric applications that are usually based on unbalanced panel data and have a nested (hierarchical) structure. Examples include predicting student performance in a class in a school, or house prices in a neighborhood in a county or a state. Using Monte Carlo simulations, we show that this predictor is better in root mean square error performance than the usual fixed‐ or random‐effects predictors ignoring the nested structure of the data. This is applied to forecasting the productivity of public capital in the private sector using nested panel data of 48 contiguous American states. Copyright © 2013 John Wiley & Sons, Ltd.     

A note on the predictive power of survey data in nowcasting euro area GDP

This paper investigates the trade‐off between timeliness and quality in nowcasting practices. This trade‐off arises when the frequency of the variable to be nowcast, such as gross domestic product (GDP), is quarterly, while that of the underlying panel data is monthly; and the latter contains both survey and macroeconomic data. These two categories of data have different properties regarding timeliness and quality: the survey data are timely available (but might possess less predictive power), while the macroeconomic data possess more predictive power (but are not timely available because of their publication lags). In our empirical analysis, we use a modified dynamic factor model which takes three refinements for the standard dynamic factor model of Stock and Watson (Journal of Business and Economic Statistics, 2002, 20, 147–162) into account, namely mixed frequency, preselections and cointegration among the economic variables. Our main finding from a historical nowcasting simulation based on euro area GDP is that the predictive power of the survey data depends on the economic circumstances; namely, that survey data are more useful in tranquil times, and less so in times of turmoil.     

Volatility impulse response analysis for DCC-GARCH models: The role of volatility transmission mechanisms

This study introduces volatility impulse response functions (VIRF) for dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models. In addition, the implications with respect to network analysis—using the connectedness approach of Diebold and Y lmaz (Journal of Econometrics, 2014, 182(1), 119–134)—is discussed. The main advantages of this framework are (i) that the time-varying dynamics do not underlie a rolling-window approach and (ii) that it allows us to test whether the propagation mechanism is time varying or not. An empirical analysis on the volatility transmission mechanism across foreign exchange rate returns is illustrated. The results indicate that the Swiss franc and the euro are net transmitters of shocks, whereas the British pound and the Japanese yen are net volatility receivers of shocks. Finally, the findings suggest a high degree of comovement across European currencies, which has important portfolio and risk management implications.     

The impact of parameter uncertainty in insurance pricing and reserve with the temperature‐related mortality model

Changes in mortality rates have an impact on the life insurance industry, the financial sector (as a significant proportion of the financial markets is driven by pension funds), governmental agencies, and decision makers and policymakers. Thus the pricing of financial, pension and insurance products that are contingent upon survival or death and which is related to the accuracy of central mortality rates is of key importance. Recently, a temperature‐related mortality (TRM) model was proposed by Seklecka et al. (Journal of Forecasting, 2017, 36(7), 824–841), and it has shown evidence of outperformance compared with the Lee and Carter (Journal of the American Statistical Association, 1992, 87, 659–671) model and several others of its extensions, when mortality‐experience data from the UK are used. There is a need for awareness, when fitting the TRM model, of model risk when assessing longevity‐related liabilities, especially when pricing long‐term annuities and pensions. In this paper, the impact of uncertainty on the various parameters involved in the model is examined. We demonstrate a number of ways to quantify model risk in the estimation of the temperature‐related parameters, the choice of the forecasting methodology, the structures of actuarial products chosen (e.g., annuity, endowment and life insurance), and the actuarial reserve. Finally, several tables and figures illustrate the main findings of this paper.     

Mortality effects of economic fluctuations in selected eurozone countries

Socioeconomic status is commonly conceptualized as the social standing or well‐being of an individual or society. Higher socioeconomic status has long been identified as a contributing factor for mortality improvement. This paper studies the impact of macroeconomic fluctuations (having gross domestic product (GDP) as a proxy) on mortality for the nine most populous eurozone countries. Based on the statistical analysis between the time‐dependent indicator of the Lee and Carter (Journal of the American Statistical Association, 1992, 87(419), 659–671) model and GDP, and adaptation of the good features of the O'Hare and Li (Insurance: Mathematics and Economics, 2012, 50, 12–25) model, a new mortality model including this additional economic‐related factor is proposed. Results for male and female from ages between 0 and 89, and similar for unisex data, are provided. This new model shows a better fitting and more plausible forecast among a significant number of eurozone countries. An in‐depth analysis of our findings is provided to give a better understanding of the relationship between mortality and GDP fluctuations.     

Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

In this study we evaluate the forecast performance of model‐averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold. First, the predictive likelihood does always better than the traditionally employed ‘marginal’ likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (RMSE) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995: Predictive model selection. Journal of the Royal Statistical Society B 57 : 247–262) with a hold‐out sample size of 25% to minimize the RMSE (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries, beating for almost all countries the AR(1) benchmark model. Copyright © 2011 John Wiley & Sons, Ltd.     

PARX model for football match predictions

We propose an innovative approach to model and predict the outcome of football matches based on the Poisson autoregression with exogenous covariates (PARX) model recently proposed by Agosto, Cavaliere, Kristensen, and Rahbek (Journal of Empirical Finance, 2016, 38(B), 640–663). We show that this methodology is particularly suited to model the goal distribution of a football team and provides a good forecast performance that can be exploited to develop a profitable betting strategy. This paper improves the strand of literature on Poisson‐based models, by proposing a specification able to capture the main characteristics of goal distribution. The betting strategy is based on the idea that the odds proposed by the market do not reflect the true probability of the match because they may also incorporate the betting volumes or strategic price settings in order to exploit betters' biases. The out‐of‐sample performance of the PARX model is better than the reference approach by Dixon and Coles (Applied Statistics, 1997, 46(2), 265–280). We also evaluate our approach in a simple betting strategy, which is applied to English football Premier League data for the 2013–2014, 2014–2015, and 2015–2016 seasons. The results show that the return from the betting strategy is larger than 30% in most of the cases considered and may even exceed 100% if we consider an alternative strategy based on a predetermined threshold, which makes it possible to exploit the inefficiency of the betting market.     

Mortality effects of temperature changes in the United Kingdom

Temperature changes are known to affect the social and environmental determinants of health in various ways. Consequently, excess deaths as a result of extreme weather conditions may increase over the coming decades because of climate change. In this paper, the relationship between trends in mortality and trends in temperature change (as a proxy) is investigated using annual data and for specified (warm and cold) periods during the year in the UK. A thoughtful statistical analysis is implemented and a new stochastic, central mortality rate model is proposed. The new model encompasses the good features of the Lee and Carter (Journal of the American Statistical Association, 1992, 87: 659–671) model and its recent extensions, and for the very first time includes an exogenous factor which is a temperature‐related factor. The new model is shown to provide a significantly better‐fitting performance and more interpretable forecasts. An illustrative example of pricing a life insurance product is provided and discussed.     

Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model

A recent study by Rapach, Strauss, and Zhou (Journal of Finance, 2013, 68(4), 1633–1662) shows that US stock returns can provide predictive content for international stock returns. We extend their work from a volatility perspective. We propose a model, namely a heterogeneous volatility spillover–generalized autoregressive conditional heteroskedasticity model, to investigate volatility spillover. The model specification is parsimonious and can be used to analyze the time variation property of the spillover effect. Our in‐sample evidence shows the existence of strong volatility spillover from the US to five major stock markets and indicates that the spillover was stronger during business cycle recessions in the USA. Out‐of‐sample results show that accounting for spillover information from the USA can significantly improve the forecasting accuracy of international stock price volatility.     

Modeling eBay price using stochastic differential equations

Online auctions have become increasingly popular in recent years. There is a growing body of research on this topic, whereas modeling online auction price curves constitutes one of the most interesting problems. Most research treats price curves as deterministic functions, which ignores the random effects of external and internal factors. To account for the randomness, a more realistic model using stochastic differential equations is proposed in this paper. The online auction price is modeled by a stochastic differential equation in which the deterministic part is equivalent to the second‐order differential equation model proposed in Wang et al. (Journal of the American Statistical Association, 2008, 103, 1100–1118). The model also includes a component representing the measurement errors. Explicit expressions for the likelihood function are also obtained, from which statistical inference can be conducted. Forecast accuracy of the proposed model is compared with the ODE (ordinary differential equation) approach. Simulation results show that the proposed model performs better.     

Prediction from the regression model with two‐way error components

In this paper we extend the Baillie and Baltagi ( 1999 ) paper (Prediction from the regression model with one‐way error components. In Analysis of Panels and Limited Dependent Variables Models, Hsiao C, Lahiri K, Lee LF, Pesaran H (eds). Cambridge University Press, Cambridge, UK). In particular, we derive six predictors for the two‐way error components model, as well as their associated asymptotic mean squared error (AMSE) of multi‐step prediction. In addition, we also provide both theoretical and simulation evidence as to the relative efficiency of our six alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods which indicate that the ordinary optimal predictors perform well for various accuracy criteria. Copyright © 2010 John Wiley & Sons, Ltd.     

Scenario planning: An investigation of the construct and its measurement

Scenario‐planning academicians and practitioners have been observing for more than three decades the importance of this method in dealing with environmental uncertainty. However, there has been no valid scale that may help organizational leaders to act in practice. Our review of prior studies identifies some problems related to conceptualization, reliability, and validity of this construct. We address these concerns by developing and validating a measure of scenario planning based on Churchill's paradigm (Journal of Marketing Research, 1979, 16, 64–73). Our data analysis follows from a sample of 133 managers operating in the healthcare field in France. To validate our scale, we used three approaches: first, an exploratory factor analysis; second, an examination of psychometric proprieties of all dimensions; and third, a confirmatory factor analysis. The results of this study indicate that scenario planning is a multidimensional construct composed of three dimensions: information acquisition, knowledge dissemination, and scenario development and strategic choices.