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State and federal revenues fell well short of projections in 2002. While revenues normally turn down in a recession, those revenue shortfalls were much greater than would have been expected given how mild the 2001 recession turned out to be. This paper examines some of the reasons for the large forecast variances observed in recent years using specific examples from forecasts made for the state of Minnesota. Key factors identified include inaccurate forecast for U.S. economic growth; inadequate, untimely and inaccurate data; imperfect models; and unrecognized changes in the structure of the economy. These factors came together and reinforced each other, ultimately producing a larger reduction in state revenues than could have been anticipated in advance.

Over a decade ago, Bretschneider and Gorr proposed two directions for future research in government forecasting: one was to extend the research on developing and evaluating alternative forecasting methods and the other, to look at forecasting as a human activity and examine how organizational factors affect forecasting. What has happened since then? To see partially what has been done and what remains to be done, this paper provides a review of the literature on government revenue forecasting with a primary focus on the state level and identifies areas for future research in government revenue forecasting.

The purpose of the paper is to report on a novel approach to assessing long‐term policy and technology impacts. This approach combines a qualitative forecast with a tri‐level quantitative projection to provide a broadly socio‐economic analysis. It is aimed at forecasting problems, such as impact assessment for future policy formulation in the light of socio‐economic, technological and market developments.

The paper is based on a variety of research methods including scenario planning, and techniques taken from analysis of stochastic processes to identify and correlate behaviour, combined with the concepts meso‐economics, in order to produce more robust tri‐level quantitative estimations, driven by qualitative analysis.

The paper finds that it is possible to join micro‐economic behaviour to macro‐economic, using meso‐economics to attack what was previously seen as a difficult gap between the two. It also finds that quantitative forecasting, based on socio‐economic behaviour using the qualitative assessment from a scenario – i.e. from stories about the future – can form a basis for quantitative forecasting. Different scenarios may be linked to corresponding quantitative economic estimations using key indicator parameters at each economic level, those which are the most relevant to the scenarios, and so exploit statistical techniques across the three levels in a balanced fashion.

This paper summarises a novel approach, taking a fresh look at forecasting economic impacts assessments by shaping the quantitative form with a qualitative tool, while introducing the linking powers of meso‐economics. General economic theories in widespread use today seem to be weak when dealing with the non‐deterministic nature of real markets and economies and especially in linking micro‐economic parameters to macro‐economic. The approach attempts to resolve this dilemma. An example is presented of its use in a recent study.

The purpose of this study is to investigate the impacts of interval measured data, rather than traditional point data, on economic variability studies.

The study uses interval measured data to forecast the variability of future stock market changes. The variability (interval) forecasts are then compared with point data‐based confidence interval forecasts.

Using interval measured data in stock market variability forecasting can significantly increase forecasting accuracy, compared with using traditional point data.

An interval forecast for stock prices essentially consists of predicted levels and a predicted variability which can reduce perceived uncertainty or risk embedded in future investments, and therefore, may influence required returns and capital asset prices.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

In recent work, we have developed a theory of economic forecasting for empirical econometric models when there are structural breaks. This research shows that well-specified models may forecast poorly, whereas it is possible to design forecasting devices more immune to the effects of breaks. In this chapter, we summarise key aspects of that theory, describe the models and data, then provide an empirical illustration of some of these developments when the goal is to generate sequences of inflation forecasts over a long historical period, starting with the model of annual inflation in the UK over 1875–1991 in Hendry (2001a).

Purpose – The purpose of this chapter is twofold: to forecast gross domestic product (GDP) using nonparametric method, known as multivariate k-nearest neighbors method, and to provide asymptotic properties for this method.

Methodology/approach – We consider monthly and quarterly macroeconomic variables, and to match the quarterly GDP, we estimate the missing monthly economic variables using multivariate k-nearest neighbors method and parametric vector autoregressive (VAR) modeling. Then linking these monthly macroeconomic variables through the use of bridge equations, we can produce nowcasting and forecasting of GDP.

Findings – Using multivariate k-nearest neighbors method, we provide a forecast of the euro area monthly economic indicator and quarterly GDP, which is better than that obtained with a competitive linear VAR modeling. We also provide the asymptotic normality of this k-nearest neighbors regression estimator for dependent time series, as a confidence interval for point forecast in time series.

Originality/value of chapter – We provide a new theoretical result for nonparametric method and propose a novel methodology for forecasting using macroeconomic data.

A striking feature of inflation in developing countries is the high variability from one period to another. This feature would seem to make forecasting of the inflation rate a difficult task. This article applies three different forecasting techniques to predict the monthly rate of inflation in India over the period June, 1971 to May, 1980: The three methods used include the more conventional regression method and the newer time‐series and combined regression‐time‐series methods. A subsidiary objective is to examine the empirical applicability of the monetarist model of inflation in a developing economy. It is found that the combined regression‐time‐series model and the regression model are good predictors of the monthly rate of inflation in India. The results also indicate that the monetarist model performs well in predicting the monthly inflation rate in a regression framework.

The purpose of this paper is to understand the behavior of the automotive industry which is very critical to avoid major economic disruptions in the economy. To understand this industry, one needs to understand its historical performance in relation to many economic factors that may affect the industry.

Data about automobile sales (in dollars and in units) and many economic and demographic variables are collected from a variety of sources. Automobile sales are the dependent variable. However, the variable of automobile sales is divided into foreign and domestic car makers. The data are regressed using Statistical Package for the Social Sciences (SPSS) stepwise regression to obtain highly correlated variables.

The results indicate a strong relationship between the economic variables and foreign car sales, but the relationship between the economic variables and domestic car sales is weak. The domestic cars sales relationship to the other economic variables should be explored further to determine possible causes for the weak correlation. One of the possible reasons could be that domestic car makers use many incentives to influence sales, but data on incentives by model by year are not available. The addition of this variable as a factor may improve correlation.

The results in this study could help the automobile companies better understand their business, and the auto companies could use the results for possible strategic decisions. In addition, legislatures in the impacted states could use the results to prepare for fluctuations in the industry that would result in profound effects on the states in question.

This type of analysis is not standard, and the use of multiple economic variables correlated with domestic and foreign car sales is unique. The study provides a basis for further research.