Intermittency and obsolescence: A Croston method with linear decay

Only two forecasting methods have been designed specifically for intermittent demand with possible demand obsolescence: Teunter–Syntetos–Babai (TSB) and Hyperbolic-Exponential Smoothing (HES). When an item becomes obsolete the TSB forecasts decay exponentially while those of HES decay hyperbolically. Both types of decay continue to predict nonzero demand indefinitely, and it would be preferable for forecasts to become zero after a finite time. We describe a third method, called Exponential Smoothing with Linear Decay, that decays linearly to zero in a finite time, is asymptotically the best method for handling obsolescence, and performs well in experiments on real and synthetic data.     

fETSmcs: Feature-based ETS model component selection

The well-developed ETS (ExponenTial Smoothing, or Error, Trend, Seasonality) method incorporates a family of exponential smoothing models in state space representation and is widely used for automatic forecasting. The existing ETS method uses information criteria for model selection by choosing an optimal model with the smallest information criterion among all models fitted to a given time series. The ETS method under such a model selection scheme suffers from computational complexity when applied to large-scale time series data. To tackle this issue, we propose an efficient approach to ETS model selection by training classifiers on simulated data to predict appropriate model component forms for a given time series. We provide a simulation study to show the model selection ability of the proposed approach on simulated data. We evaluate our approach on the widely used M4 forecasting competition dataset in terms of both point forecasts and prediction intervals. To demonstrate the practical value of our method, we showcase the performance improvements from our approach on a monthly hospital dataset.     

A note on forecasting international tourism demand in Spain

This paper considers the extent to which price and income proxy variables help in forecasting tourist demand in Spain. Contrary to some recent studies, we found that the inputs' contribution in terms of fitting and forecasting is nil when compared with alternative univariate models. Whether these findings are the results of the restrictions embedded in building the proxy inputs or in a poor specification of the dynamics of these models remains to be seen. We also contend that when dealing with medium, long-term forecasting comparisons, the use of the traditional aggregate accuracy measures like RMSE and MAPE help very little in discriminating among competing models. In these situations, predicted annual growth rates may be a better alternative.     

基于神经网络集成的失业预警方法

提出采用神经网络集成技术对中国失业预警系统进行建模,以克服当前失业预警系统建模中存在的小样本、高维度、非线性、噪音数据等难题。采用BP神经网络回归模型对失业率进行预测;基于两种集成技术Bagging与AdaBoost对多个神经网络进行集成,以获得比单个预测模型更好的精度与稳定性;最后基于广东省的社会经济调查数据进行了实证分析,实验结果表明:在对失业率的预测上,Bagging集成方法的预测效果优于Adaboost集成方法,也优于单个最好的神经网络模型。     

Restricted forecasting with VAR models: An analysis of a test for joint compatibility between restrictions and forecasts

A restricted forecasting compatibility test for Vector Autoregressive Error Correction models is analyzed in this work. It is shown that a variance–covariance matrix associated with the restrictions can be used to cancel out model dynamics and interactions between restrictions. This allows us to interpret the joint compatibility test as a composition of the corresponding single restriction compatibility tests. These tests are useful for appreciating the contribution of each and every restriction to the joint compatibility between the whole set of restrictions and the unrestricted forecasts. An estimated process adjustment for the test is derived and the resulting feasible joint compatibility test turns out to have better performance than the original one. An empirical illustration of the usefulness of the proposed test makes use of Mexican macroeconomic data and the targets proposed by the Mexican Government for the year 2003.     

Using past contribution patterns to forecast fundraising outcomes in crowdfunding

The crowdfunding mechanism has proven to be a practical way of raising funds, especially with the widespread use of the Internet. However, one limitation of current crowdfunding platforms is that it is hard for creators and backers to anticipate the success of a campaign. This paper tackles this limitation. We take a two-pronged approach to building our forecasting model. First, we explore the nature and heterogeneity of contribution dynamics in crowdfunding campaigns and compare them across two natural groups (successful and unsuccessful campaigns). We then use insights generated from our exploratory analysis and draw upon the general laws of motion for stochastic processes in order to introduce a new dynamic model for predicting crowdfunding outcomes. Our model incorporates the history and dynamics of both the focal crowdfunding campaign and other campaigns for predicting outcomes. We compare our model to other parametric and semi-parametric benchmark models, and show substantial improvements.     

Nonlinearities and Fractional Integration in the US Unemployment Rate*

This paper proposes a model of the US unemployment rate which accounts for both its asymmetry and its long memory. Our approach introduces fractional integration and nonlinearities simultaneously into the same framework, using a Lagrange multiplier procedure with a standard null‐limit distribution. The empirical results suggest that the US unemployment rate can be specified in terms of a fractionally integrated process, which interacts with some nonlinear functions of labour‐demand variables such as real oil prices and real interest rates. We also find evidence of a long‐memory component. Our results are consistent with a hysteresis model with path dependency rather than a non‐accelerating inflation rate of unemployment (NAIRU) model with an underlying unemployment equilibrium rate, thereby giving support to more activist stabilization policies. However, any suitable model should also include business cycle asymmetries, with implications for both forecasting and policy‐making.     

Optimal error and GMDH predictors: A comparison with some statistical techniques

This paper presents some results obtained in time series forecasting using two nonstandard approaches and compares them with those obtained by usual statistical techniques. In particular, a new method based on recent results of the General Theory of Optimal Algorithm is considered. This method may be useful when no reliable statistical hypotheses can be made or when a limited number of observations is available. Moreover, a nonlinear modelling technique based on Group Method of Data Handling (GMDH) is also considered to derive forecasts. The well-known Wolf Sunspot Numbers and Annual Canadian Lynx Trappings series are analyzed; the Optimal Error Predictor is also applied to a recently published demographic series on Australian Births. The reported results show that the Optimal Error and GMDH predictors provide accurate one step ahead forecasts with respect to those obtained by some linear and nonlinear statistical models. Furthermore, the Optimal Error Predictor shows very good performances in multistep forecasting.     

On the evasion of employment protection legislation

This paper analyzes how the option to evade employment protection legislation impacts on unemployment. Using a stylized model, it is established that the level of unemployment is non-monotonous in the degree of strictness with which employment protection legislation is enforced. Considering just cause and social criteria requirements for three regulatory regimes representative of a large number of industrialized countries, we find that different regimes generate different dismissal decisions only if the regimes are strictly enforced. In contrast, unemployment rates may differ across regimes even in the case of weak enforcement. Additionally, we find that it may be worse for the economy to weakly enforce harmful regulations than to strictly enforce them.     

Business cycle asymmetry and the stock market

We present and estimate models of an asymmetric relationship between CRSP stock index returns and the U.S. unemployment rate. Based on the Akaike Information Criterion, conventional linear time series models are improved by allowing asymmetric responses. Our results show that negative stock returns are quickly followed by sharp increases in unemployment, while more gradual unemployment declines follow positive stock returns. According to our forecasting model, the unemployment rate rises by 1.12 percentage points during the 12 months after a 10 percent stock decline. Because macroeconomic forecasters have been unable to reliably predict downturns, these findings may provide a useful contribution.     

The application of a Markov Chain in educational planning

Mathematical models can assist educators in the preparation of their educational plans and their potential in this regard is being increasingly realized. As a result, models have found application at all levels at which planning is conducted.This paper examines the potential of one model—the Markov Chain—which is capable of predicting enrolments for an education system.The model is applied to the New South Wales State Government education system between 1947 and 1961 and the projected enrolments compared to the actual enrolments in those years.Some success is achieved but it appears as if the data rather than the model are responsible for this. The limitations of the Markov Chain approach are discussed and present research and directions listed.     

Loss given default models incorporating macroeconomic variables for credit cards

Based on UK data for major retail credit cards, we build several models of Loss Given Default based on account level data, including Tobit, a decision tree model, a Beta and fractional logit transformation. We find that Ordinary Least Squares models with macroeconomic variables perform best for forecasting Loss Given Default at the account and portfolio levels on independent hold-out data sets. The inclusion of macroeconomic conditions in the model is important, since it provides a means to model Loss Given Default in downturn conditions, as required by Basel II, and enables stress testing. We find that bank interest rates and the unemployment level significantly affect LGD.     

Overnight stock returns and realized volatility

The information flow in modern financial markets is continuous, but major stock exchanges are open for trading for only a limited number of hours. No consensus has yet emerged on how to deal with overnight returns when calculating and forecasting realized volatility in markets where trading does not take place 24 hours a day. Based on a recently introduced formal testing procedure, we find that for the S&P 500 index, a realized volatility estimator that optimally incorporates overnight information is more accurate in-sample. In contrast, estimators that do not incorporate overnight information are more accurate for individual stocks. We also show that accounting for overnight returns may affect the conclusions drawn in an out-of-sample horserace of forecasting models. Finally, there is considerably less variation in the selection of the best out-of-sample forecasting model when only the most accurate in-sample RV estimators are considered.     

Policy intervention and forecasting: An application to minimum wages

This paper examines the forecasting implications of incorporating policy effects into the structure of unconditional time series models. The forecasting model is applied to the Puerto Rican experience with minimum wages from 1953 to 1982. The empirical results suggest that significant disemployment and unemployment followed the imposition of economy-wide minimum wages in 1974. The growth of employment suffered and the aggregate unemployment rate reached an unprecedented level. Multivariate time-series models for the employment-population ratio and the unemployment rate capture these effects well. They also forecast more accurately than unvariate and intervention models over the ex post period, 1983–1984. It is argued that models that combine subject matter-specific structure within a dynamic time-series framework greatly help to satisfy demands for theoretical consistency and forecast accuracy. Multivariate time-series models play an important complementary role in the structural modelling of economic policy analysis. This is particularly so when limitations of either data or theory preclude complete specification of structural equations.     

How Much Unemployment in the Soviet Union?

The Soviet Government is usually at pains to stress how communism abolishes unemployment. Joseph Porket, a Czech emigré economist, formerly at the Prague School of Economics and now working in Britain, looks below the surface of official propaganda to discover large-scale structural unemployment as well as more transient forms of joblessness.     

基于灰色系统理论的江苏省入境旅游研究

入境旅游是旅游业的重要组成部分,本文运用灰色系统理论中的灰色关联度分析方法对江苏省旅游外汇收入与主要客源国入境旅游人次数之间的关联性进行分析,并建立了GM(1,1)灰色预测模型对江苏省典型创汇国未来的入境旅游客流规模进行预测,为江苏省入境旅游提供决策依据和方法参考。     

Forecasting monthly riverflow time series

Mean monthly flows from thirty rivers in North and South America are used to test the short-term forecasting ability of seasonal ARIMA, deseasonalized ARMA, and periodic autoregressive models. The series were split into two sections and models were calibrated to the first portion of the data. The models were then used to generate one-step-ahead forecasts for the second portion of the data. The forecast performance is compared using various measures of accuracy. The results suggest that a periodic autoregressive model, identified by using the partial autocorrelation function, provided the most accurate forecasts