Population forecasting theory,methods and assessments of accuracy. The magnitude of error due to different vital processes in population forecasts

The accuracy of population forecasts depends in part upon the method chosen for forecasting the vital rates of fertility, mortality, and migration. Methods for handling the stochastic propagation of error calculations in demographic forecasting are hard to do precisely. This paper discusses this obstacle in stochastic cohort-component population forecasts. The uncertainty of forecasts is due to uncertain estimates of the jump-off population and to errors in the forecasts of the vital rates. Empirically based of each source are presented and propagated through a simplified analytical model of population growth that allows assessment of the role of each component in the total error. Numerical estimates based on the errors of an actual vector ARIMA forecast of the US female population. These results broadly agree with those of the analytical model. This work especially uncertainty in the fertility forecasts to be so much higher than that in the other sources that the latter can be ignored in the propagation of error calculations for those cohorts that are born after the jump-off year of the forecast. A methodology is therefore presented which far simplifies the propagation of error calculations. It is noted, however, that the uncertainty of the jump-off population, migration, and mortality in the propagation of error for those alive at the jump-off time of the forecast must still be considered.     

Stochastic demographic forecasting

"This paper describes a particular approach to stochastic population forecasting, which is implemented for the U.S.A. through 2065. Statistical time series methods are combined with demographic models to produce plausible long run forecasts of vital rates, with probability distributions. The resulting mortality forecasts imply gains in future life expectancy that are roughly twice as large as those forecast by the Office of the Social Security Actuary....Resulting stochastic forecasts of the elderly population, elderly dependency ratios, and payroll tax rates for health, education and pensions are presented."     

Response to updated mortality forecasts in life cycle saving and labor supply

Historical evidence shows that demographic forecasts, including mortality forecasts, have often been grossly in error. One consequence of this is that forecasts are updated frequently. How should individuals or institutions react to updates, given that these are likewise expected to be uncertain? We discuss this problem in the context of a life cycle saving and labor supply problem, in which a cohort of workers decides how much to work and how much to save for mutual pensions. Mortality is stochastic and point forecasts are updated regularly. A Markovian approximation for the predictive distribution of mortality is derived. This renders the model computationally tractable, and allows us to compare a theoretically optimal rational expectations solution to a strategy in which the cohort merely updates the life cycle plan to match each updated mortality forecast. The implications of the analyses for overlapping generations modeling of pension systems are pointed out.     

LOOKING TOWARDS 2030: A NEW WORLD COMING INTO FOCUS

This article examines global demographic trends to 2030, with a particular focus on projections for the world's major economies. All else held constant, ageing populations imply slower rates of economic development for Western Societies. However, a number of policy options are available to counteract the impact of demographic change.     

Evaluating Point and Density Forecasts of DSGE Models

This paper investigates the accuracy of forecasts from four dynamic stochastic general equilibrium (DSGE) models for inflation, output growth and the federal funds rate using a real‐time dataset synchronized with the Fed's Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation are the model forecasts dominated by the Greenbook projections. A comparison with forecasts from Bayesian vector autoregressions shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections

The Eurosystem staff forecasts are conditional on the financial markets, the global economy and fiscal policy outlook, and include expert judgement. We develop a multi-country BVAR for the four largest countries of the euro area and we show that it provides accurate conditional forecasts of policy relevant variables such as, for example, consumer prices and GDP. The forecasting accuracy and the ability to mimic the path of the Eurosystem projections suggest that the model is a valid benchmark to assess the consistency of the projections with the conditional assumptions. As such, the BVAR can be used to identify possible sources of judgement, based on the gaps between the Eurosystem projections and the historical regularities captured by the model.     

Stochastic population forecasts and their uses

"The properties and uses of stochastic forecasts are discussed here. For linear stochastic projections, we show how the computation of forecast moments and the statistical distribution of forecasts depend on the multiplicative and autoregressive structure of the dynamics. Both scalar and vector projection methods are discussed, and their similarities are explored. Next we discuss the uses of stochastic forecasts, arguing that it is important to relate forecasts to the specific decision-making criteria of particular forecast users. The example of [the U.S. system of] Social Security is used to show how a dynamic programming approach may be used to explore alternative decisions in a probabilistic context."     

Evaluation of the best M4 competition methods for small area population forecasting

The ‘M4’ forecasting competition results were featured recently in a special issue of the International Journal of Forecasting and included projections for demographic time series. We sought to investigate whether the best M4 methods could improve the accuracy of small area population forecasts, which generally suffer from much higher forecast errors than regions with larger populations. The aim of this study was to apply the top ten M4 forecasting methods to produce 5- and 10-year forecasts of small area total populations using historical datasets from Australia and New Zealand. Forecasts were compared against the actual population numbers and forecasts from two simple benchmark models. The M4 methods were found to perform relatively well compared to our benchmarks. In the light of these findings, we discuss possible future directions for small area population forecasting research.     

Conditional Probabilistic Population Projections: An Application to Climate Change

Future changes in population size, composition, and spatial distribution are key factors in the analysis of climate change, and their future evolution is highly uncertain. In climate change analyses, population uncertainty has traditionally been accounted for by using alternative scenarios spanning a range of outcomes. This paper illustrates how conditional probabilistic projections offer a means of combining probabilistic approaches with the scenario-based approach typically employed in the development of greenhouse gas emissions projections. The illustration combines a set of emissions scenarios developed by the Intergovernmental Panel on Climate Change (IPCC) with existing probabilistic population projections from IIASA. Results demonstrate that conditional probabilistic projections have the potential to account more fully for uncertainty in emissions within conditional storylines about future development patterns, to provide a context for judging the consistency of individual scenarios with a given storyline, and to provide insight into relative likelihoods across storylines, at least from a demographic perspective. They may also serve as a step toward more comprehensive quantification of uncertainty in emissions projections.     

Long term electricity consumption forecast in Brazil: A fuzzy logic approach

The energy companies are always facing the challenge of producing more accurate load forecasts. A fuzzy logic methodology is proposed in order to extract rules from the input variables and provide Brazil's long-term annual electricity demand forecasts. In recent literature, the formulation of these types of models has been limited to treating the explanatory variables in the univariate form, or involving only the GDP. This study proposes an extension of this model, starting with population and the GDP additional value. The proposed model is compared with the official projections. The obtained results are quite promising.     

Detailed population projections for small areas: the Massachusetts experience.

There is an urgent and growing need for population projections that are not only detailed by geographic unit, but are also detailed by demographic characteristics. A high level of disaggregation allows the planner considerable flexibility to aggregate the projected data in a variety of geographic and demographic configurations. This study reports on the methodology and results of the Massachusetts experience in developing detailed population projections. Combining conventional with some not-so-conventional techniques, population projections are developed for 351 geographic units, as well as 108 demographic categories. The projections are currently in use in a large variety of public and private planning activities.     

The effects of age structure on economic growth: An application of probabilistic forecasting to India

During recent years there has been an increasing awareness of the explanatory power of population age structure variables in economic growth regressions. We estimate a new cross-country regression model of the effects of age structure change on economic growth. We use the new model and recent probabilistic demographic forecasts for India to derive the uncertainty of predicted economic growth rates caused by the uncertainty in demographic developments.     

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."     

Demographic volatility and economic growth: convention and beyond

Following the success of endogenous growth theory, recent empirical examinations of the demography–economic growth construct established that components of demographic change can provide meaningful and clear insights into the direction and impact of demographic variation in economic growth. While theoretical justification and empirical support to the claim cannot be denied, confusions seem to have arisen whether an empirical growth construct will only be limited to demographic dynamics or the model can entertain other non-demographic variables. In a leading research, Kelley and Schmidt (1995, 2001) provided seemingly ambiguous evidence that addition of non-demographic variables can add explanatory power to the growth regression. While subsequent empirical growth models have largely followed the convention as in KS, some important considerations like the stochastic effects of demographic system on economic growth seem to be missing. This paper attempts to address the concerns by suggesting a long-memory demographic system and embedding stochastic demographic characteristics in a standard Solow–Swan model, which also forms the basis of convergence pattern. We empirically show that significant stochastic shocks exist in the demographic components which could have contributed to the growth volatility across nations. We suggest that empirical growth models should account for stochastic demographic characteristics to enable economic policy makers with correct information about the current and future state of evolution of the demography–economic system.     

The effects of foreign-currency translation accounting on security analysts' forecasts

This paper examines certain initial effects on security analysts' forecasts of earnings per share that resulted from the December 1981 change in the Financial Accounting Standards Board rules for foreign-currency accounting and reporting from Statement 8 to Statement 52. The results show that, relative to a non-Statement 52 control sample, analysts were more uncertain about future earnings when Statement 52 was initially adopted by the FASB or implemented by companies, and that analysis revised their forecasts more frequently during those periods. The revisions of analysts were positively correlated with the magnitude of the impact of the new standard on company earnings.     

Policy change and learning in the RBC model

What is the impact of surprise and anticipated policy changes when agents form expectations using adaptive learning rather than rational expectations? We examine this issue using the standard stochastic real business cycle model with lump-sum taxes. Agents combine knowledge about future policy with econometric forecasts of future wages and interest rates. Dynamics under learning can have large impact effects and a gradual hump-shaped response, and tend to be prominently characterized by oscillations not present under rational expectations. These fluctuations reflect periods of excessive optimism or pessimism, followed by subsequent corrections.     

Some observations on forecasting and policy

This paper offers some thoughts on the use of macroeconomic and financial forecasts in monetary and fiscal policy. It stresses the role of nowcasting in constructing good forecasts: most of the value added in macreoeconomic forecasts comes from getting a good approximation to the jumping-off point. Some specific applications are discussed: long-range debt/GDP projections and forecasting recessions using asset prices. I also discuss the construction and use of density forecasts.     

Evaluating the conditionality of judgmental forecasts

We propose a framework for evaluating the conditionality of forecasts. The crux of our framework is the observation that a forecast is conditional if revisions to the conditioning factor are incorporated faithfully into the remainder of the forecast. We consider whether the Greenbook, Blue Chip survey and Survey of Professional Forecasters exhibit systematic biases in the manner in which they incorporate interest rate projections into the forecasts of other macroeconomic variables. We do not find strong evidence of systematic biases in the three economic forecasts that we consider, as the interest rate projections in these forecasts appear to be incorporated efficiently into the forecasts of other economic variables.     

Fast and frugal forecasting

Simple statistical forecasting rules, which are usually simplifications of classical models, have been shown to make better predictions than more complex rules, especially when the future values of a criterion are highly uncertain. In this article, we provide evidence that some of the fast and frugal heuristics that people use intuitively are able to make forecasts that are as good as or better than those of knowledge-intensive procedures. We draw from research on the adaptive toolbox and ecological rationality to demonstrate the power of using intuitive heuristics for forecasting in various domains including sport, business, and crime.