Validation and forecasting accuracy in models of climate change

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster’s perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.     

The performance of alternative forecasting methods for SETAR models

We compare a number of methods that have been proposed in the literature for obtaining h-step ahead minimum mean square error forecasts for self-exciting threshold autoregressive (SETAR) models. These forecasts are compared to those from an AR model. The comparison of forecasting methods is made using Monte Carlo simulation. The Monte-Carlo method of calculating SETAR forecasts is generally at least as good as that of the other methods we consider. An exception is when the disturbances in the SETAR model come from a highly asymmetric distribution, when a Bootstrap method is to be preferred.An empirical application calculates multi-period forecasts from a SETAR model of US gross national product using a number of the forecasting methods. We find that whether there are improvements in forecast performance relative to a linear AR model depends on the historical epoch we select, and whether forecasts are evaluated conditional on the regime the process was in at the time the forecast was made.     

The econometric forecasting model of Czechoslovakia

Conclusion The result of the construction, estimation, testing and forecasting uses of the VVS-2 model testify to the expediency of using comprehensive econometric models in socialist economy. The VVS-2 model expresses at a satisfying rate of accuracy the quantitative relations between the fundamental indicators of Czechoslovak economy and has produced satisfactory results also in calculating short-term forecasts. Some new methods and computer programs that have been developed and employed for model estimation may be considered a contribution towards the development of applied econometrics in socialist countries.At the Computing Research Centre, United Nations D.P. in Bratislava, research work has continued on the development and improvement of further comprehensive econometric models. Within this framework the VVS-2 model will be permanently reestimated and possibly also extended and re-specificated according to the practical needs of macroeconomic analysis and planning.Authors are indebted to Professor Anton Klas, director of the Computing Research Centre, United Nations D.P., for creating very favourable conditions for the research work.     

The pricing accuracy of alternative equity valuation models: Scandinavian evidence

In many decision contexts, there is a need for benchmark equity valuations, based on simplified modeling and publicly available information. Prior research on U.S. data however shows that the accuracy of such valuation models can be low and sensitive to the choice of model specifications and value driver predictions. In this paper, we test the applicability and pricing accuracy of three fundamental valuation (dividend discount, residual income, and abnormal earnings growth) models, all based on forecasts of company dividends, earnings, and/or equity book values. Extending prior research, we apply these models to Scandinavian firms with accounting data from the period 2005–2014, explicitly testing two approaches for the prediction of the value drivers—exogenously forecasted numbers versus projected historical numbers. Given access to the forecasted value drivers, the dividend discount model comes out as the most accurate valuation model. In particular, this holds in a comparison between the most parsimonious model specifications. The residual income valuation model generates the best pricing accuracy given the prediction of value drivers based on historical financial numbers. Notably, we observe pricing errors that in general are lower than what has been reported in prior U.S.‐based research for the dividend discount and the residual income valuation models. The pricing accuracy of the abnormal earnings growth models is surprisingly weak in the Scandinavian setting. However, these models improve somewhat after a couple of complexity adjustments, in particular with value driver predictions based on the projected history setting.     

Specification and estimation of spatial econometric models: A discussion of alternative strategies for spatial economic modelling

The semantical insufficiency of (spatial) economic theories necessitates the making of additional assumptions — thereby introducing substantial specification uncertainty — in order to arrive at a fully specified econometric model. The traditional or current approach to econometric modelling treats specification uncertainty inadequately. This proposition is illustrated by two well-known examples from the spatial economic literature. Two alternative specification strategies for spatial economic modelling — designed to improve the current spatial econometric modelling approach — are proposed. One of these strategies is used for a specification analysis of agricultural output in Eire.     

Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures

Airline traffic forecasting is important to airlines and regulatory authorities. This paper examines a number of approaches to forecasting short- to medium-term air traffic flows. It contributes as a rare replication, testing a variety of alternative modelling approaches. The econometric models employed include autoregressive distributed lag (ADL) models, time-varying parameter (TVP) models and an automatic method for econometric model specification. A vector autoregressive (VAR) model and various univariate alternatives are also included to deliver unconditional forecast comparisons. Various approaches for taking into account interactions between contemporaneous air traffic flows are examined, including pooled ADL models and the enhanced models with the addition of a “world trade” variable. Based on the analysis of a number of forecasting error measures, it is concluded that pooled ADL models that include the “world trade” variable outperform the alternatives, and in particular univariate methods; and, second, that automatic modelling procedures are enhanced through judgmental intervention. In contrast to earlier results, the TVP models do not improve accuracy. Depending on the preferred error measure, the difference in accuracy may be substantial.     

Diffusion performance of major steel-making countries: alternative econometric tests

The purpose of this study is to compare the speed of diffusion in major steel-making countries. This is a cross-system analysis, involving industrial market countries (Western Europe, the United States, Canada, and Japan), quasi-market economies (selected newly industrializing countries, India), and the central planning states (the Soviet Union and Eastern Europe). The study reveals that at least in this, significant case, the latter countries are clearly inferior, i.e. slower.The article seeks the most accurate measure of speed of diffusion of one radical steel innovation, the oxygen process. The speed is estimated by regressing a logistic function not applied to the steel industry to date. Parameters of a logistic function are estimated first with linear least squares methods and then with nonlinear (or iterative) least squares, to establish which offers more accurate estimation than the widely used linear approach. It is shown that the iterative method produces a better statistical fit.Associate Professor; the author would like to acknowledge computational assistance by Steve Langolis, Stanford University, and help in updating the author's data on the world steel production provided by Mark Shafter, London School of Economics.     

Unpredictability in economic analysis,econometric modeling and forecasting

Unpredictability arises from intrinsic stochastic variation, unexpected instances of outliers, and unanticipated extrinsic shifts of distributions. We analyze their properties, relationships, and different effects on the three arenas in the title, which suggests considering three associated information sets. The implications of unanticipated shifts for forecasting, economic analyses of efficient markets, conditional expectations, and inter-temporal derivations are described. The potential success of general-to-specific model selection in tackling location shifts by impulse-indicator saturation is contrasted with the major difficulties confronting forecasting.     

Indirect inference in structural econometric models

This paper considers parametric inference in a wide range of structural econometric models. It illustrates how the indirect inference principle can be used in the inference of these models. Specifically, we show that an ordinary least squares (OLS) estimation can be used as an auxiliary model, which leads to a method that is similar in spirit to a two-stage least squares (2SLS) estimator. Monte Carlo studies and an empirical analysis of timber sale auctions held in Oregon illustrate the usefulness and feasibility of our approach.     

A labor-oriented quarterly econometric forecasting model of the youngstown-Warren SMSA

Our model contains forty-two stochastic equations and thirty-two identities designed to explain variation in manhours of work, employment, weekly wages and the wage bill by sector of activity for the Youngstown-Warren SMSA. Methodology in the development of these equations is discussed and the regression results shown. The paper evaluates the simulation results of the model by showing the root mean squares error and percent root mean squares error for the major aggregates using both dynamic and historic system simulations.     

Conditions under which econometric models will outperform naive models

Brodie and de Kluyver have reported empirical results in which simple naive models have produced forecasts as accurate as those derived from econometric models. I apply some recent theoretical results to show that these findings are plausible. I also suggest some general conditions under which econometric models will outperform naive models.     

The specification of multi-market disequilibrium econometric models

The specifications of multi-market disequilibrium econometric models are clouded with different notions of effective demand. This paper points out that the specification of such models for econometric analysis can be achieved from the basic concept of fixed-price equilibrium and without the use of the concepts of effective demand. The specifications of Ito and Gourieroux, Laffont and Monfort are justified within this framework. With proper stochastic elements introduced in the system, the derived likelihood function from our approach does not involve multiple integrals and is computationally tractable for models with many disequilibrium markets.     

Macroeconomic forecasting in Austria: An analysis of accuracy

In the present paper, we attempt a critical evaluation of macroeconomic forecasting in Austria. For this purpose, we calculate conventional magnitude measures of accuracy as well as probabilities of correctly predicting directional change for the forecasts made by two Austrian institutions (WIFO and IHS) and by the OECD. ARIMA models and Holt-Winters exponential smoothing serve as benchmarks for comparison.     

Testing unstable econometric models for stability: An empirical study

Econometric models may be tested for stability using the asymptotic distribution of the dominant characteristic root of the system as derived by Theil and Boot (1962) or by Oberhofer and Kmenta (1973). This paper points out that this distribution is known for stable models only which implies that the null hypothesis must always be stability and the power of the test is in question. A Monte Carlo study is performed to investigate the power of the test and the distribution of the test statistic for unstable and stable models in the small sample case.