Bushfire rate-of-spread forecasting: Deterministic and statistical approaches to fire modelling

The three basic modelling approaches used to explain forest fire behaviour are theoretically, laboratory or empirically based. Results of all three approaches are reviewed, but it is noted that only the laboratory- and empirically based models have led to forecasting techniques that are in widespread use. These are the Rothermel model and the McArthur meters, respectively. Field tests designed to test the performance of these operational models were carried out in tropical grasslands. A preliminary analysis indicated that the Rothermel model overpredicted spread rates while the McArthur model underpredicted. To improve the forecast of bushfire rate of spread available to operational firefighting crews it is suggested that a time-variable parameter (TYP) recursive least squares algorithm can be used to assign weights to the respective models, with the weights recursively updated as information on fire-front location becomes available. Results of this methodology when applied to US Grasslands fire experiment data indicate that the quality of the input combined with a priori knowledge of the performance of the candidate models plays an important role in the performance of the TVP algorithm. With high-quality input data, the Rothermel model on its own outperformed the TVP algorithm, but with slightly inferior data both approaches were comparable. Though the use of all available data in a multiple linear regression produces a lower sum of squared errors than the recursive, time-variable weighting approach, or that of any single model, the uncertainties of data input and consequent changes in weighting coefficients during operational conditions suggest the use of the TVP algorithm approach.     

Related‐variables selection in temporal disaggregation

Two related‐variables selection methods for temporal disaggregation are proposed. In the first method, the hypothesis tests for a common feature (cointegration or serial correlation) are first performed. If there is a common feature between observed aggregated series and related variables, the conventional Chow–Lin procedure is applied. In the second method, alternative Chow–Lin disaggregating models with and without related variables are first estimated and the corresponding values of the Bayesian information criterion (BIC) are stored. It is determined on the basis of the selected model whether related variables should be included in the Chow–Lin model. The efficacy of these methods is examined via simulations and empirical applications. Copyright © 2008 John Wiley & Sons, Ltd.     

Forecast Evaluation of Nonlinear Models: The Case of Long‐Span Real Exchange Rates

This paper deals with the nonlinear modeling and forecasting of the dollar–sterling and franc–sterling real exchange rates using long spans of data. Our contribution is threefold. First, we provide significant evidence of smooth transition dynamics in the series by employing a battery of recently developed in‐sample statistical tests. Second, we investigate the small‐sample properties of several evaluation measures for comparing recursive forecasts when one of the competing models is nonlinear. Finally, we run a forecasting race for the post‐Bretton Woods era between the nonlinear real exchange rate model, the random walk, and the linear autoregressive model. The nonlinear model outperforms all rival models in the dollar–sterling case but cannot beat the linear autoregressive in the franc–sterling. Copyright © 2011 John Wiley & Sons, Ltd.     

Multiple Hypothesis Testing of Market Risk Forecasting Models

Extending previous risk model backtesting literature, we construct multiple hypothesis testing (MHT) with the stationary bootstrap. We conduct multiple tests which control for the generalized confidence level and employ the bootstrap MHT to design multiple comparison testing. We consider absolute and relative predictive ability to test a range of competing risk models, focusing on value‐at‐risk and expected shortfall (ExS). In devising the test for the absolute predictive ability, we take the route of recent literature and construct balanced simultaneous confidence sets that control for the generalized family‐wise error rate, which is the joint probability of rejecting true hypotheses. We implement a step‐down method which increases the power of the MHT in isolating false discoveries. In testing for the ExS model predictive ability, we design a new simple test to draw inference about recursive model forecasting capability. In the second suite of statistical testing, we develop a novel device for measuring the relative predictive ability in the bootstrap MHT framework. The device, which we coin multiple comparison mapping, provides a statistically robust instrument designed to answer the question: ‘Which model is the best model?’ Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of prospective Hawkes and recursive point process models for Ebola in DRC

Point process models, such as Hawkes and recursive models, have recently been shown to offer improved accuracy over more traditional compartmental models for the purposes of modeling and forecasting the spread of disease epidemics. To explicitly test the performance of these two models in a real-world and ongoing epidemic, we compared the fit of Hawkes and recursive models to outbreak data on Ebola virus disease (EVD) in the Democratic Republic of the Congo in 2018–2020. The models were estimated, and the forecasts were produced, time-stamped, and stored in real time, so that their prospective value can be assessed and to guard against potential overfitting. The fit of the two models was similar, with both models resulting in much smaller errors in the beginning and waning phases of the epidemic and with slightly smaller error sizes on average for the Hawkes model compared with the recursive model. Our results suggest that both Hawkes and recursive point process models can be used in near real time during the course of an epidemic to help predict future cases and inform management and mitigation strategies.     

Predicting bankruptcy using recursive partitioning and a realistically proportioned data set

Auditors must assess their clients' ability to function as a going concern for at least the year following the financial statement date. The audit profession has been severely criticized for failure to ‘blow the whistle’ in numerous highly visible bankruptcies that occurred shortly after unmodified audit opinions were issued. Financial distress indicators examined in this study are one mechanism for making such assessments. This study measures and compares the predictive accuracy of an easily implemented two‐variable bankruptcy model originally developed using recursive partitioning on an equally proportioned data set of 202 firms. In this study, we test the predictive accuracy of this model, as well as previously developed logit and neural network models, using a realistically proportioned set of 14,212 firms' financial data covering the period 1981–1990. The previously developed recursive partitioning model had an overall accuracy for all firms ranging from 95 to 97% which outperformed both the logit model at 93 to 94% and the neural network model at 86 to 91%. The recursive partitioning model predicted the bankrupt firms with 33–58% accuracy. A sensitivity analysis of recursive partitioning cutting points indicated that a newly specified model could achieve an all firm and a bankrupt firm predictive accuracy of approximately 85%. Auditors will be interested in the Type I and Type II error tradeoffs revealed in a detailed sensitivity table for this easily implemented model. Copyright © 2000 John Wiley & Sons, Ltd.     

Robustness of alternative non‐linearity tests for SETAR models

In recent years there has been a growing interest in exploiting potential forecast gains from the non‐linear structure of self‐exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR‐type non‐linearities in an observed time series. It is important to study the power and robustness properties of these tests since erroneous test results might lead to misspecified prediction problems. In this paper we investigate the robustness properties of several commonly used non‐linearity tests. Both the robustness with respect to outlying observations and the robustness with respect to model specification are considered. The power comparison of these testing procedures is carried out using Monte Carlo simulation. The results indicate that all of the existing tests are not robust to outliers and model misspecification. Finally, an empirical application applies the statistical tests to stock market returns of the four little dragons (Hong Kong, South Korea, Singapore and Taiwan) in East Asia. The non‐linearity tests fail to provide consistent conclusions most of the time. The results in this article stress the need for a more robust test for SETAR‐type non‐linearity in time series analysis and forecasting. Copyright © 2004 John Wiley & Sons, Ltd.     

Non‐linear,non‐parametric,non‐fundamental exchange rate forecasting

This paper employs a non‐parametric method to forecast high‐frequency Canadian/US dollar exchange rate. The introduction of a microstructure variable, order flow, substantially improves the predictive power of both linear and non‐linear models. The non‐linear models outperform random walk and linear models based on a number of recursive out‐of‐sample forecasts. Two main criteria that are applied to evaluate model performance are root mean squared error (RMSE) and the ability to predict the direction of exchange rate moves. The artificial neural network (ANN) model is consistently better in RMSE to random walk and linear models for the various out‐of‐sample set sizes. Moreover, ANN performs better than other models in terms of percentage of correctly predicted exchange rate changes. The empirical results suggest that optimal ANN architecture is superior to random walk and any linear competing model for high‐frequency exchange rate forecasting. Copyright © 2006 John Wiley & Sons, Ltd.     

Modelling exchange rate dynamics new perspectives from the frequency domain

In this paper the dynamics of foreign exchange rates is sought to be studied via new frequency domain techniques. Stationarity properties of the rates are analysed via a unit root test as well as a test based on the evolutionary spectrum. Linearity and Gaussianity are analysed via bispectral tests and compared with the more frequently employed time domain tests, such as the McLeod-Li and Tsay tests. Finally, an evaluation of the out-of-sample forecasting properties for eight methods—Random Walk, ARMA, Bilinear, State dependent model, dynamic linear model, ARCH, GARCH, and Garch-in-mean—is made. The methods used here seem to shed a great deal of light on hitherto neglected aspects of exchange rate dynamics.     

Monthly Employment Indicators of the Euro Area and Larger Member States: Real‐Time Analysis of Indirect Estimates

The paper presents a comparative real‐time analysis of alternative indirect estimates relative to monthly euro area employment. In the experiment quarterly employment is temporally disaggregated using monthly unemployment as related series. The strategies under comparison make use of the contribution of sectoral data of the euro area and its six larger member states. The comparison is carried out among univariate temporal disaggregations of the Chow and Lin type and multivariate structural time series models of small and medium size. Specifications in logarithms are also systematically assessed. All multivariate set‐ups, up to 49 series modelled simultaneously, are estimated via the EM algorithm. Main conclusions are that mean revision errors of disaggregated estimates are overall small, a gain is obtained when the model strategy takes into account the information by both sector and member state and that larger multivariate set‐ups perform very well, with several advantages with respect to simpler models.Copyright © 2014 John Wiley & Sons, Ltd.     

Pre‐recession pattern of six economic indicators in the USA

This paper applies a tightly parameterized pattern recognition algorithm, previously applied to earthquake prediction, to the problem of predicting recessions. Monthly data from 1962 to 1996 on six leading and coincident economic indicators for the USA are used. In the full sample, the model performs better than benchmark linear and non‐linear models with the same number of parameters. Subsample and recursive analysis indicates that the algorithm is stable and produces reasonably accurate forecasts even when estimated using a small number of recessions. Copyright © 2000 John Wiley & Sons, Ltd.     

Testing for (common) stochastic trends in the presence of structural breaks

This paper considers the problem of testing for the presence of stochastic trends in multivariate time series with structural breaks. The breakpoints are assumed to be known. The testing framework is the multivariate locally best invariant test and the common trend test of Nyblom and Harvey (2000). The asymptotic distributions of the test statistics are derived under a specification of the deterministic component which allows for structural breaks. Asymptotic critical values are provided for the case of a single breakpoint. A modified statistic is then proposed, the asymptotic distribution of which is independent of the breakpoint location and belongs to the Cramér‐von Mises family. This modification is particularly advantageous in the case of multiple breakpoints. It is also shown that the asymptotic distributions of the test statistics are unchanged when seasonal dummy variables and/or weakly dependent exogenous regressors are included. Finally, as an example, the tests are applied to UK macroeconomic data and to data on road casualties in Great Britain. Copyright © 2002 John Wiley & Sons, Ltd.     

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.     

Can panel data really improve the predictability of the monetary exchange rate model?

A common explanation for the inability of the monetary model to beat the random walk in forecasting future exchange rates is that conventional time series tests may have low power, and that panel data should generate more powerful tests. This paper provides an extensive evaluation of this power argument to the use of panel data in the forecasting context. In particular, by using simulations it is shown that although pooling of the individual prediction tests can lead to substantial power gains, pooling only the parameters of the forecasting equation, as has been suggested in the previous literature, does not seem to generate more powerful tests. The simulation results are illustrated through an empirical application. Copyright © 2007 John Wiley & Sons, Ltd.     

Testing for Granger (non‐)causality in a time‐varying coefficient VAR model

In this paper we propose Granger (non‐)causality tests based on a VAR model allowing for time‐varying coefficients. The functional form of the time‐varying coefficients is a logistic smooth transition autoregressive (LSTAR) model using time as the transition variable. The model allows for testing Granger non‐causality when the VAR is subject to a smooth break in the coefficients of the Granger causal variables. The proposed test then is applied to the money–output relationship using quarterly US data for the period 1952:2–2002:4. We find that causality from money to output becomes stronger after 1978:4 and the model is shown to have a good out‐of‐sample forecasting performance for output relative to a linear VAR model. Copyright © 2008 John Wiley & Sons, Ltd.     

Realized Volatility Forecast of Stock Index Under Structural Breaks

We investigate the realized volatility forecast of stock indices under the structural breaks. We utilize a pure multiple mean break model to identify the possibility of structural breaks in the daily realized volatility series by employing the intraday high‐frequency data of the Shanghai Stock Exchange Composite Index and the five sectoral stock indices in Chinese stock markets for the period 4 January 2000 to 30 December 2011. We then conduct both in‐sample tests and out‐of‐sample forecasts to examine the effects of structural breaks on the performance of ARFIMAX‐FIGARCH models for the realized volatility forecast by utilizing a variety of estimation window sizes designed to accommodate potential structural breaks. The results of the in‐sample tests show that there are multiple breaks in all realized volatility series. The results of the out‐of‐sample point forecasts indicate that the combination forecasts with time‐varying weights across individual forecast models estimated with different estimation windows perform well. In particular, nonlinear combination forecasts with the weights chosen based on a non‐parametric kernel regression and linear combination forecasts with the weights chosen based on the non‐negative restricted least squares and Schwarz information criterion appear to be the most accurate methods in point forecasting for realized volatility under structural breaks. We also conduct an interval forecast of the realized volatility for the combination approaches, and find that the interval forecast for nonlinear combination approaches with the weights chosen according to a non‐parametric kernel regression performs best among the competing models. Copyright © 2014 John Wiley & Sons, Ltd.     

Unemployment variation over the business cycles: a comparison of forecasting models

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non‐linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non‐linearity in the unemployment series. Only recently have there been some developments in applying non‐linear models to estimate and forecast unemployment rates. A major concern of non‐linear modelling is the model specification problem; it is very hard to test all possible non‐linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non‐linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back‐propagation model and a generalized regression neural network model to estimate and forecast post‐war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out‐of‐sample forecast results obtained by the ANN models with those obtained by several linear and non‐linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.     

A state-dependent linear recurrent formula with application to time series with structural breaks

An underlying assumption in Multivariate Singular Spectrum Analysis (MSSA) is that the time series are governed by a linear recurrent continuation. However, in the presence of a structural break, multiple series can be transferred from one homogeneous state to another over a comparatively short time breaking this assumption. As a consequence, forecasting performance can degrade significantly. In this paper, we propose a state-dependent model to incorporate the movement of states in the linear recurrent formula called a State-Dependent Multivariate SSA (SD-MSSA) model. The proposed model is examined for its reliability in the presence of a structural break by conducting an empirical analysis covering both synthetic and real data. Comparison with standard MSSA, BVAR, VAR and VECM models shows the proposed model outperforms all three models significantly.     

某古塔结构振动台试验研究

以模型试验的相似关系为基础,设计并制作一模型相似比为1/10的古塔模型结构,并对该模型进行了地震模拟振动台试验,测试其受震前后的动力特性变化,了解其刚度变化情况.同时测试了该模型结构在3种选定地震波作用下的加速度反应、位移反应;确定了相应的动力放大系数、最大加速度包络图、最大位移包络图;考察了相应的裂缝出现与发展情况以及薄弱部位的变形情况;最后根据试验结果,评判模型结构的地震反应机理和规律,评价结构的总体抗震性能,为古塔原型结构抗震保护提供了设计和构造建议.     

A note on in‐sample and out‐of‐sample tests for Granger causality

This paper studies in‐sample and out‐of‐sample tests for Granger causality using Monte Carlo simulation. The results show that the out‐of‐sample tests may be more powerful than the in‐sample tests when discrete structural breaks appear in time series data. Further, an empirical example investigating Taiwan's investment–saving relationship shows that Taiwan's domestic savings may be helpful in predicting domestic investments. It further illustrates that a possible Granger causal relationship is detected by out‐of‐sample tests while the in‐sample test fails to reject the null of non‐causality. Copyright © 2005 John Wiley & Sons, Ltd.