A comparison of the accuracy of the Box-Jenkins method with that of automated forecasting methods

The focus of the research described in this paper is on presenting an automated forecasting system that encompasses an objective ARIMA method with the Holt-Winters procedure in a weighted averaging scheme. The system is applied to M-Competition data and the results are compared to the subjective Box-Jenkins forecasts as well as to results from two other automated methods, CAPRI and SIFT. The comparison reveals that especially for one-step ahead forecasting, the automated system competes favorably with both automated methods and the individualized Box-Jenkins analysis.     

Stochastic methods in population forecasting

"This paper presents a stochastic version of the demographic cohort-component method of forecasting future population. In this model the sizes of future age-sex groups are non-linear functions of random future vital rates. An approximation to their joint distribution can be obtained using linear approximations or simulation. A stochastic formulation points to the need for new empirical work on both the autocorrelations and the cross-correlations of the vital rates. Problems of forecasting declining mortality and fluctuating fertility are contrasted. A volatility measure for fertility is presented. The model can be used to calculate approximate prediction intervals for births using data from deterministic cohort-component forecasts. The paper compares the use of expert opinion in mortality forecasting with simple extrapolation techniques to see how useful each approach has been in the past. Data from the United States suggest that expert opinion may have caused systematic bias in the forecasts."     

Developing Official Stochastic Population Forecasts at the US Census Bureau

The U.S. Census Bureau is approaching a critical decision regarding a major facet of its methodology for forecasting the United States population. In the past, it has relied on alternative scenarios, low, medium, and high, to reflect varying interpretations of current trends in fertility, mortality, and international migration to forecast population. This approach has limitations that have been noted in the recent literature on population forecasting. Census Bureau researchers are embarking on an attempt to incorporate probabilistic reasoning to forecast prediction intervals around point forecasts to future dates. The current literature offers a choice of approaches to this problem. We are opting to employ formal time series modeling of parameters of fertility, mortality, and international migration, with stochastic renewal processes. The endeavor is complicated by the administrative necessity to produce a large amount of racial and Hispanic origin detail in the population, as well as the ubiquitous cross-categories of age and sex. As official population forecasts must meet user demand, we are faced with the added challenge of presenting and supporting the resulting product in a way that is comprehensible to users, many of whom have little or no comprehension of the technical forecasting literature, and are accustomed to simple, deterministic answers. We may well find a need to modify our strategy, depending on the realities that may emerge from the limitations of data, the administrative requirements of the product, and the diverse needs of our user community.     

Testing the predictive accuracy of COVID-19 forecasts

We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention for the epidemic in the United States. We find three main results. First, at the short horizon (1 week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3 and 4 week ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.     

A regional forecasting model for construction activity

This paper presents a new, comprehensive and detailed model of construction activity. The model is intended primarily for forecasting applications. The model generates forecasts of new construction starts for each of the 50 states of the United States. Forecasts are made for 29 types of structures. The paper presents evidence that the structure of the determinants of construction activity varies across regions within the United States. Thus, prior models of construction, based only on national time-series data, may be subject to aggregation bias. Evaluation of the model's forecasts indicates that the model outperforms simpler forecasting methods.     

Leading indicators of inflation in market economies

Inflation rates are cyclical in major market-oriented economies. Recently Geoffrey H. Moore and Stanley Kaish applied the well-known leading indicator approach to the development of a leading index of inflation cycles for the United States. Their index was based on measures of tightness in the labor market, and a measure of tightness in total credit markets, along with a measure of changes in industrial commodity prices. They found that this composite index reflects changes in inflation rate cycles reasonably well, and that it was more reliable than any of the three components taken alone. The present study broadens their study by attempting to duplicate the leading inflation index for forecasting changes in inflation rates in Canada, the United Kingdom, West Germany, France, Italy, and Japan. In general we find that the leading index is useful in anticipating changes in inflation rates in all these countries with the exception of France and Italy. As such we find that the forecasting properties of this index are often as promising in other countries as they have been in the U.S. Where they are not we conclude that there is a need for further research.     

Using Medicare data for short-run projections of the elderly population.

As the elderly population of the United States grows in absolute number and as a proportion of total population, accurate projections of that population become increasingly important for sound policy decisions. Cohort component techniques are typically used for state and local projections of the elderly population, but are often outdated or even nonexistent for many local areas. This paper suggests an alternative approach, based on Medicare data and simple projection techniques. Projections for several base periods and projection horizons are made for all states and for counties in Florida and are compared with actual Medicare enrollment. On the basis of these comparisons it appears that Medicare data and simple projection techniques can produce very useful short-run projections of the elderly population for states and local areas.     

THE DYNAMICS OF REGIONAL POPULATION AND EMPLOYMENT GROWTH

The authors investigate what Muth labels as the "chicken and egg" or what others label it as "jobs follow people" versus "people follow jobs" debate by studying the dynamic relationships of changes in population and employment in the snowbelt, the sunbelt, and the nine census regions of the United States. The vector autoregressive (VAR) modeling method is used for the annual time series data (1955–1988) on population and employment. The Granger causality tests show that, in the snowbelt region, population tends to precede employment, while in the sunbelt region, the opposite is true. The impulse response functions generated for the forecasting decade suggest that "jobs follow people" in the snowbelt and "people follow jobs" in the sunbelt. Similar findings in general hold for the census regions belonging to the snowbelt and sunbelt. These findings raise doubts about the proposition of others that the growth process is either demand or supply-driven in all phases of regional growth.     

Intrametropolitan planning for community-based residential care: a goals programming approach.

Small community-based residential facilities have increasingly supplanted large-scale institutions as treatment settings for mentally and physically disabled, indigent, elderly and ex-offender populations in the United States. Because the intrametropolitan assignment of these service-dependent populations and their community care facilities has not been purposively planned, most facilities and clients have clustered in inner cities, resulting in the formation of service-dependent population ghettos. This paper outlines a goals programming approach to the client/facility assignment problem. The model provides a basis for an intrametropolitan distribution of residential service facilities that balances equity and efficiency goals and that protects both client and community rights. A regional fair-share plan that incorporates negotiation and arbitration techniques is offered as an institutional mechanism for implementing the goals programming framework.     

Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty

Macroeconomic data are subject to data revisions. Yet, the usual way of generating real-time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real-time vintages. Second, the BVAR model is jointly estimated with a model of data revisions such that forecasts are conditioned on estimates of the ‘true’ values. We find that this second method generally improves upon conventional practice for density forecasting, especially for the United States.     

美国突发动物疫情应急管理对中国的启示

我国已将突发动物疫情纳入应急管理范畴,也形成了基本的应急管理机制,但还不够完善,在实践中仍存在问题。文章介绍了美国在动物疫情应急管理、动物防疫、动物疫情预警预报以及兽医管理体系的做法,认为我国可借鉴美国的成功经验。     

On the future of macroeconomic forecasting

This note offers several assertions - unsupported forecasts about the future of macroeconomic forecasting based on recent experience in the United States. The remarks fall into three areas: (1) model building, (2) the role of ‘judgment’ in forecasting, and (3) forecast evaluation.     

Combination schemes for turning point predictions

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions generated by autoregressive (AR) and Markov-switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach for both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and the Euro area business cycles.     

The changing geography of major causes of death among middle age white Americans, 1939-1981.

Urban areas, especially the Northeast, are assumed to have the highest death rates from chronic diseases in the United States. Based on analysis of age-adjusted death rates of the white population 35-64 from 1939-1941 through 1979-1981, it is shown that chronic disease rates in the urban Northeast and Midwest have declined compared to the rest of the United States. High rates of chronic as well as traumatic causes of death now characterize the South. Hypotheses are offered to explain these changes, including changes in lifestyle, differences in state government policies, the changing geography of industry and ethnic populations, and the spread of medical care.     

Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

Regional forecasting of the demand for fossil fuels by electric utilities in the United States

The possibility of interfuel substitution in the generation of electrical energy exists. Given this fact, a demand model for various fossil fuels by electric utilities in the United States is developed. Using regional data, the results suggest that the responsiveness of the demand for coal, residual fuel oil, distillate fuel oil, and natural gas by electric utilities to relative price changes is significant. In a forecasting setting, the demand model performs remarkably well when actual and forecast values for 1979 are compared.     

Panel data analysis of U.S. coal productivity

We analyze labor productivity in coal mining in the United States using indices of productivity change associated with the concepts of panel data modeling. This approach is valuable when there is extensive heterogeneity in production units, as with coal mines. We find substantial returns to scale for coal mining in all geographical regions, and find that smooth technical progress is exhibited by estimates of the fixed effects for coal mining. We carry out a variety of diagnostic analyses of our basic model and primary modeling assumptions, using recently proposed methods for addressing ‘errors-in-variables’ and ‘weak instrument bias’ problems in linear and nonlinear models.     

Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective

Forecasting election results has been a highly attractive activity among political and social scientists. Different forecasting methods have been proposed, but those based on public opinion polls are the most common. However, there are challenges to using opinion polls, especially because they neglect undecided voters. Due to the significant number of undecided participants and their impact on voting outcomes, we analyze the potential behavior of undecided voters by considering opinion polls and sentiment based on voter expectation from the perspective of the bandwagon effect and the spiral of silence. We establish a hierarchical Bayesian forecasting model to predict voting results, and apply it to the 2016 United States presidential election and the 2016 Brexit referendum. The results of our model suggest that voting outcomes are more predictable when fully utilizing the impact of undecided voters. The results indicate that integrating aggregated polls into the hierarchical Bayesian framework is a strong predictor for forecasting outcomes, and they provide evidence for the influence of sentiment based on voter expectation in forecasting election results.     

美国经济所面临的挑战（英文）

美国经济在全球经济中占有非常重要的地位,因此美国经济的未来走势直接关系到全球经济的发展.本文分析美国在经济发展中所具有的优势地位以及在未来经济发展中所要面对的挑战与风险。美国未来经济面临着巨额的经常项目逆差、财政收支赤字、能源价格高涨以及房地产泡沫破灭的威胁,使美国必须采取措施进行调整。这将导致本世纪初美国未来经济发展速度减慢。     

Forecasting the US housing market

The recent housing market boom and bust in the United States illustrates that real estate returns are characterized by short-term positive serial correlation and long-term mean reversion to fundamental values. We develop an econometric model that includes these two components, but with weights that vary dynamically through time depending on recent forecasting performances. The smooth transition weighting mechanism can assign more weight to positive serial correlation in boom times, and more weight to reversal to fundamental values during downturns. We estimate the model with US national house price index data. In-sample, the switching mechanism significantly improves the fit of the model. In an out-of-sample forecasting assessment the model performs better than competing benchmark models.