Consumer Bankruptcies and the Bankruptcy Reform Act: A Time-Series Intervention Analysis, 1960–1997

The Bankruptcy Reform Act of 1978, effective on October 1, 1979, significantly altered the basic rules by which consumers file for bankruptcy. Between 1979 and 1997, the number of nonbusiness bankruptcies filed annually rose from 200,000 to 1.35 million, and the rate of bankruptcies per 100,000 adults increased from 129 to 715. A controversial aspect of bankruptcy is how much of this increase can be attributed to the 1978 act. Early empirical studies provide estimates ranging from a low of 6% to a high of 75% for the immediate post-act period. However, two recent studies using longer data series report that none of the increase was due to the act. Previous studies suffer from several econometric problems, including inadequate attention to stochastic properties and stationarity of the data series, as well as data errors due to reporting changes. This paper uses an ARIMA intervention analysis to estimate the impact of the 1978 act. Using adjusted quarterly data for 1960:3 to 1995:4, the data first are examined for unit roots. The tests reject the presence of seasonal unit roots but confirm the presence of a nonseasonal unit root. The empirical analysis therefore is based on logged first differences of bankruptcy filings and filing rates per capita. An ARIMA model is estimated using the preintervention data for 1960:3 to 1979:3. This model is re-estimated for 1960:3 to 1995:4 with the intervention terms included. The intervention model estimates indicate that the 1978 act increased consumer bankruptcies by 36% in the post-act period relative to the pre-act period, or about 72,400 additional bankruptcies per year. Overall, the net impact of the 1978 act was modest compared to the substantial rise in bankruptcies that has occurred since 1979.     

应用ARIMA模型预测我国能源消费量

本文采用ARIMA模型型对1974年～2003年我国能源消费量的年度数据进行分析,结果显示ARIMA模型不但适合于我国能源消费量的非平稳时间序列的特点,并且可以用于未来值的预测.     

光伏建筑一体化行业板块指数的预测模型构建 ——基于ARIMA模型的实证分析

在30、60双碳目标之下,光伏建筑一体化行业的发展是绿色建筑、碳中和的重要实现路径,故有效地判断该行业板块未来的变动趋势,有助于更好地把握该行业投资盈利点。基于2019年4月1日至2021年5月28日沪深股市中 37 家光伏建筑一体化相关企业的股票数据,建立ARIMA模型对光伏建筑一体化的板块指数发展趋势做出预测。研究发现,ARIMA(20,1,3)模型通过了ADF 检验和 L-Jung-Box 检验,并预测出2021年5月至2021年7月该板块指数将呈现“锯齿”形总体不断向上增长。最后,根据模型预测结果和市场背景,提出针对性的建议,为市场投资者和企业管理者提供相应的技术参考和启示。     

FACTOR MODELS AND TIME‐VARYING PARAMETER FRAMEWORK FOR FORECASTING EXCHANGE RATES AND INFLATION: A SURVEY

A survey of models used for forecasting exchange rates and inflation reveals that the factor‐based and time‐varying parameter or state space models generate superior forecasts relative to all other models. This survey also finds that models based on Taylor rule and portfolio balance theory have moderate predictive power for forecasting exchange rates. The evidence on the use of Bayesian Model Averaging approach in forecasting exchange rates reveals limited predictive power, but strong support for forecasting inflation. Overall, the evidence overwhelmingly points to the context of the forecasts, relevance of the historical data, data transformation, choice of the benchmark, selected time horizons, sample period and forecast evaluation methods as the crucial elements in selecting forecasting models for exchange rate and inflation.     

基于ARIMA模型的北京市CPI预测分析

模型是在分析时间序列问题时较为常用的一种方法,该模型尤其在研究时间序列的短期预测方面表现较好。文章选取了北京市2005年1月至2017年6月的(居民消费价格指数)月度数据为样本,借助于Eviews8.0软件,构建了(0,1,12)模型,经过检验,该模型充分提取了数据中的有用信息,拟合的效果也较为良好,并利用该模型对北京市2017年下半年趋势进行了预测。     

新型农村社会养老保险财政支持能力测度及引申

新型农村社会养老保险离开政府财政的支持,便成为无源之水、无本之木.充裕的财政收入是有效规避新农保制度震荡和实现其长期稳定发展的关键.基于ARIMA模型动态预测2012-2020年我国财政收入走势,结果表明,未来十年中国财政收入仍保持增长态势,但增长速度放缓.整体上看,新农保的财政补贴压力适中,基本维持在中国财政收入的0.8％左右,政府财政具备支持新农保可持续发展的能力.应调整新农保养老金补贴与财政收入增长比重、降低行政费用、压缩基建支出,从而改善各级政府财政支付能力不均的现实.     

Forecasting of Hong Kong airport's passenger throughput

Hong Kong International Airport (HKIA) is one of the main gateways to Mainland China and the major aviation hub in Asia. An accurate airport traffic demand forecast allows for short and long-term planning and decision making regarding airport facilities and flight networks. This paper employs the Box–Jenkins Seasonal ARIMA (SARIMA) model and the ARIMAX model to forecast airport passenger traffic for Hong Kong, and projecting its future growth trend to 2015. Both models predict a steady growth in future airport passenger traffic at Hong Kong. In addition, scenario analysis suggests that Hong Kong airport's future passenger traffic will continue to grow in different magnitudes.     

Agustín Maravall: An interview with the International Journal of Forecasting

Agustín Maravall Herrero (Madrid, 1944) is one of the world’s authorities in seasonal adjustment and automatic forecasting of economic time series. He studied Agricultural Engineering and completed a doctorate at the Universidad Politécnica de Madrid. With a Ford-Fulbright fellowship he moved to the University of Wisconsin-Madison to obtain a Ph.D. in Economics in 1975. He worked at the Research Division of the Federal Reserve Board of Governors (the Fed) in Washington D.C. and in 1979 returned to Madrid as a Senior Economist in the Research Department of the Banco de España (BE). In the period 1989-96, he was a full professor in the Department of Economics of the European University Institute (EUI) in Florence. He returned to the BE as Chief Economist and Head of the Time Series Analysis Unit and retired in December 2014.Maravall has done outstanding research in time series and has been a pioneer in developing methodology and writing computer programs for automatic estimation and model selection, seasonal adjustment, and forecasting of time series. His programs “Time Series Regression with ARIMA noise, Missing observations and Outliers” (TRAMO) and “Signal Extraction in ARIMA Time Series” (SEATS), jointly developed with Victor Gómez, have had a large influence in applied forecasting, including adjusting series for seasonality and possibly other undesirable effects, such as outliers, or missing observations, and have been used in many economic institutions around the world. He has been very active in promoting the automatic analysis of time series, teaching short courses in many countries. Also, he has stimulated research in this field being on the editorial board of the Journal of Business and Economic Statistics and the Journal of Econometrics. He has been a Special Advisor to the European Central Bank (ECB) and Eurostat in time series analysis. His research contributions have been recognized as Fellow of the Journal of Econometrics, 1995; Fellow of the American Statistical Association, 2000; Julius Shiskin Award for Economic Statistics, 2004, and the highest prizes for Economic Research in Spain: The Rey Jaime I Prize in Economics, 2005 and the Rey Juan Carlos Prize in Economics, 2014.     

基于ARIMA模型的P公司产品需求预测研究

电子产品市场需求的动态变化给制造企业的生产计划带来了很大的不确定性。以P公司的历史销售订单数据为时间序列,以ARIMA模型为基础,利用EVIEWS分析工具对电子产品的季度需求进行预测。实例结果表明,基于ARIMA建立的需求预测模型具有预测精度高,操作简便等优点。     

The tourism forecasting competition

We evaluate the performances of various methods for forecasting tourism data. The data used include 366 monthly series, 427 quarterly series and 518 annual series, all supplied to us by either tourism bodies or academics who had used them in previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate on tourism data only; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as the point forecast accuracy; (iv) we observe the effect of temporal aggregation on the forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts, and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.