An assessment of the economic value of non-linear foreign exchange rate forecasts

Recent research suggests that non-linear methods cannot improve the point forecasts of high-frequency exchange rates. These studies have been using standard forecasting criteria such as smallest mean squared error (MSE) and smallest mean absolute error (MAE). It is, however, premature to conclude from this evidence that non-linear forecasts of high-frequency financial returns are economically or statistically insignificant. We prove a proposition which implies that the standard forecasting criteria are not necessarily particularly suited for assessment of the economic value of predictions of non-linear processes where the predicted value and the prediction error may not be independently distributed. Adopting a simple non-linear forecasting procedure to 15 daily exchange rate series we find that although, when compared to simple random walk forecasts, all the non-linear forecasts give a higher MSE and MAE, when applied in a simple trading strategy these forecasts result in a higher mean return. It is also shown that the ranking of portfolio payoffs based on forecasts from a random walk, and linear and non-linear models, is closely related to a non-parametric test of market timing.     

Energy demand forecasts with investment constraints

The energy sector in India claims 30% of the available investments. Moreover, oil import bills have the largest share among the total import bills. Thus, macro economic development and energy sector are highly interdependent. Where energy demand is forecasted without these linkages one cannot be sure if investments and imports required for energy sector will be available. The SImulation of MAcroeconomic scenarios (SIMA) model generates macroeconomically consistent energy scenarios from two interlinked submodels i.e. economic and energy submodels. The energy sector is a part of the non-agricultural sector but it is linked to both the agricultural and the non-agricultural sectors. These three sectors compete with each other for the available capital. In a two-step procedure, various energy economy relations are econometrically estimated and then these are solved simultaneously by feeding in the exogenous parameters (population, oil prices, etc.). The scenarios created correspond to 1991–2010. They are the Dynamics As Usual and the High Oil Price scenarios with capital required for phasing in the electricity sector. Energy-related emission levels for pollutants such as CO₂, NO_x and SO₂ emissions are also calculated for each scenario.     

Nature in the laboratory — nature as a laboratory. Considerations about the ethics of release experiments

Field tests with genetically modified organisms go beyond the boundaries of the politically and morally neutralized space that normally surrounds scientific experiments. They enter public areas. As a social process of shaping nature they are political in a fundamental sense. Consequences of this observation concern the legitimacy of decisions and the legitimacy of deciding procedures. The political rights of citizens and their human rights can only be respected if these procedures are democratic. Without a more serious exploration of the specific circumstances of release tests — for example, the precise ecological context, the consequences for the future development of the affected ecosystem, the social consequences, and the possible institutional ways of establishing gene technology in agriculture — we do not really know what we are doing when we release transgenic organisms. Moral judgements today can therefore only be prima facie, not free from shortcomings. As responsible judges we must confess that we are still morally blind.     

Social networks and private spaces in economic forecasting

The outputs of economic forecasting—predictions for national economic indicators such as GDP, unemployment rates and inflation—are all highly visible. The production of these forecasts is a much more private affair, however, typically being thought of as the work of individual forecasters or forecast teams using their economic model to produce a forecast that is then made public. This conception over-emphasises the individual and the technical whilst silencing the broader social context through which economic forecasters develop the expertise that is essential for the credibility of their predictions. In particular, economic forecasts are given meaning and fine-tuned through the social and institutional networks that give forecasters access to the expertise of a heterogeneous mix of academics, policy-makers and business people. Within these broader groups, individual forecasters often create private forecast ‘clubs’, where subscribers have privileged access to the expertise of the economist, but where the forecasters also have privileged access to their clients’ own expert knowledge. In examining these aspects of the forecasters’ work I show that the visible and audible activities of modelling and forecasting are made possible and plausible by virtue of the modeller’s invisible interaction with a wider network.     

Measurements,disturbances and the quantum three box paradox

A quantum pre- and post-selection paradox involves making measurements at two separate times on a quantum system, and making inferences about the state of the system at an intermediate time, conditional upon the observed outcomes. The inferences lead to predictions about the results of measurements performed at the intermediate time, which have been well confirmed experimentally, but which nevertheless seem paradoxical when inferences about different intermediate measurements are combined. The three box paradox is the paradigm example of such an effect, where a ball is placed in one of three boxes and is shuffled between the boxes in between two measurements of its location. By conditionalising on the outcomes of those measurements, it is inferred that between the two measurements the ball would have been found with certainty in Box 1 and with certainty in Box 2, if either box been opened on their own. Despite experimental confirmation of the predictions, and much discussion, it has remained unclear what exactly is supposed to be paradoxical or what specifically is supposed to be quantum, about these effects. In this paper I identify precisely the conditions under which the quantum three box paradox occurs, and show that these conditions are the same as arise in the derivation of the Leggett–Garg Inequality, which is supposed to demonstrate the incompatibility of quantum theory with macroscopic realism. I will argue that, as in Leggett–Garg Inequality violations, the source of the effect actually lies in the disturbance introduced by the intermediate measurement, and that the quantum nature of the effect is that no classical model of measurement disturbance can reproduce the paradox.     

Ex-post and ex-ante prediction of unobserved economic time series: a case study

In several countries, some macro-economic variables are not observed frequently (e.g. quarterly) and economic authorities need estimates of these high-frequency figures to make econometric analyses or to follow closely the country's economic growth. Two problems are involved in this context. The first is to make these estimates after observing low-frequency values and some related indicators, and the second is to obtain predictions using just the observed indicators, i.e. before observing a new low-frequency figure. This paper gives a new optimal solution to the first problem, and solves the second using a recursive optimal approach. In the second situation, additionally, statistical tests are developed for detecting structural changes at current periods in the macro-economic variable involved. © 1998 John Wiley & Sons, Ltd.     

Testing hypotheses in macroevolution

Experimental manipulation of microevolution (changes in frequency of heritable traits in populations) has shed much light on evolutionary processes. But many evolutionary processes occur on scales that are not amenable to experimental manipulation. Indeed, one of the reasons that macroevolution (changes in biodiversity over time, space and lineages) has sometimes been a controversial topic is that processes underlying the generation of biological diversity generally operate at scales that are not open to direct observation or manipulation. Macroevolutionary hypotheses can be tested by using them to generate predictions then asking whether observations from the biological world match those predictions. Each study that identifies significant correlations between evolutionary events, processes or outcomes can generate new predictions that can be further tested with different datasets, allowing a cumulative process that may narrow down on plausible explanations, or lead to rejection of other explanations as inconsistent or unsupported. A similar approach can be taken even for unique events, for example by comparing patterns in different regions, lineages, or time periods. I will illustrate the promise and pitfalls of these approaches using a range of examples, and discuss the problems of inferring causality from significant evolutionary associations.     

A linear benchmark for forecasting GDP growth and inflation?

Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory‐based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd.     

Unit roots versus other types of time heterogeneity,parameter time dependence and superexogeneity

This paper stresses the restrictive nature of the standard unit root/cointegration assumptions and examines a more general type of time heterogeneity, which might characterize a number of economic variables, and which results in parameter time dependence and misleading statistical inference. We show that in such cases ‘operational’ models cannot be obtained, and the estimation of time‐varying parameter models becomes necessary. For instance, economic processes subject to endemic change can only be adequately modelled in a state space form. This is a very important point, because unstable models will break down when used for forecasting purposes. We also discuss a new test for the null of cointegration developed by Quintos and Phillips (1993), which is based on parameter constancy in cointegrating regressions. Finally, we point out that, if it is possible to condition on a subset of superexogenous variables, parameter instability can be handled by estimating a restricted system. Copyright © 2002 John Wiley & Sons, Ltd.     

Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts

By linking measures of forecast accuracy as well as testing procedures with regard to forecast rationality this paper investigates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis-à-vis the US dollar and, hence, for four different currency regimes. The rationality of the exchange rate predictions is initially assessed utilizing tests for unbiasedness and efficiency which indicate that the investigated forecasts are irrational in the sense that the predictions are biased. As one major contribution of this paper, it is subsequently shown that these results are not consistent with an alternative, less restrictive, measure of rationality. Investigating the order of integration of the time series as well as cointegrating relationships, this empirical evidence supports the conclusion that the majority of forecasts are in fact rational. Regarding forerunning properties of the predictions, the results are rather mediocre, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one additional important and novel evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

A stepwise model of polyreactivity of the T cell antigen-receptor (TCR): its impact on the self–nonself discrimination and on related observations (receptor editing,anergy, dual receptor cells)

The existence of antigen-receptors, BCR, and T cell antigen-receptors, that are “polyreactive”, necessitates a rethinking of its effect on two problems faced by the “adaptive” immune system: the self (S)–nonself (NS) discrimination and the determination of effector class. Here, we will concentrate on the impact of polyreactivity on the S–NS discrimination. The anti-S cells interacting with S (i.e., responding to Signal 1) are on the pathway to inactivation. Before irreversibility sets in, these cells can be activated by a second signal (Signal 2) from an effector T-helper (eTh). As these polyreactive anti-S cells express anti-NS specificities, they can be activated by recognition of NS-epitopes in the host’s normal immunogenic load with the potential to result in autoimmunity. This problem is delineated using a discrete structural model, the corollaries of which are: (1) a two-step pathway for the purging of anti-S cells (i.e., the S–NS discrimination), and (2) defensible contexts within which to view the phenomena of receptor editing, anergy, and dual receptor cells.     

Moment tests for density forecast evaluation in the presence of parameter estimation uncertainty

Density forecast (DF) possesses appealing properties when it is correctly specified for the true conditional distribution. Although a number of parametric specification tests have been introduced for the DF evaluation (DFE) in the parameter‐free context, econometric DF models are typically parameter‐dependent. In this paper, we first use a generalized probability integral transformation‐based moment test to unify these existing tests, and then apply the Newey–Tauchen method (the West–McCracken method) to correct this unified test as a generalized full‐sample (out‐of‐sample) test in the parameter‐dependent context. Unlike the corrected tests, the uncorrected tests could be substantially undersized (oversized) when they are directly applied to the full‐sample (out‐of‐sample) DFE in the presence of parameter estimation uncertainty. We also use a simulation to show the usefulness of the corrected tests in rectifying the size distortion problem, and apply the corrected tests to an empirical study of stock index returns. Copyright © 2010 John Wiley & Sons, Ltd.     

Estimation and prediction under structural instability: The case of the U.S. pulp and paper market

The objectives of this paper are: first, to show empirically the relevance of using adaptive estimation techniques over more traditional estimation approaches when economic systems are believed to be structurally unstable over time; and secondly, to compare in an empirical framework two adaptive estimation techniques: Kalman filtering and the Carbone–Longini filter. For that purpose, an econometric model for the U.S. pulp and paper market is examined under the assumption of structural instability and, hence, constitutes the basis for comparing forecasting performances and estimation accuracy achieved by each technique. A version of Kalman filtering, modified in line with the basic idea of ‘tracking’ characterizing the Carbone–Longini filter, is also presented and applied. The analysis of the results shows that it may be worth using adapative estimation methods to estimate structurally unstable models, even if there is no prior knowledge about the patterns of variation of the parameters. Also, it shows the Carbone–Longini filter and Kalman filtering as being complementary estimation techniques. An estimation/forecasting methodology involving a sequential application mode of these two techniques is suggested.     

Forecasting euro area variables with German pre‐EMU data

It is investigated whether euro area variables can be forecast better based on synthetic time series for the pre‐euro period or by using just data from Germany for the pre‐euro period. Our forecast comparison is based on quarterly data for the period 1970Q1–2003Q4 for 10 macroeconomic variables. The years 2000–2003 are used as forecasting period. A range of different univariate forecasting methods is applied. Some of them are based on linear autoregressive models and we also use some nonlinear or time‐varying coefficient models. It turns out that most variables which have a similar level for Germany and the euro area such as prices can be better predicted based on German data, while aggregated European data are preferable for forecasting variables which need considerable adjustments in their levels when joining German and European Monetary Union (EMU) data. These results suggest that for variables which have a similar level for Germany and the euro area it may be reasonable to consider the German pre‐EMU data for studying economic problems in the euro area. Copyright © 2008 John Wiley & Sons, Ltd.     

美国中小企业生存环境的八大要素

中外经济发展的实践表明，中小企业的大量存在是经济发展的内在要求和必然结果，是经济体系中形成合理的价格体系、维护市场竞争活力、确保经济运行稳定、保障充分就业的前提和条件。自20世纪70年代英国博尔顿报告以来，为中小企业尽可能地营造一个良好的生存环境，已经成为世界各国重要的政策取向，美国走在世界前列。我国中小企业的发展走过了很长一段弯路，究其原因在于没有建设好促进其生存的环境。本文分析了美国中小企业生存环境的要点，对于我国制定和调整中小企业政策、促进其健康发展具有借鉴意义。     

Subjective probability and quantum certainty

In the Bayesian approach to quantum mechanics, probabilities—and thus quantum states—represent an agent's degrees of belief, rather than corresponding to objective properties of physical systems. In this paper we investigate the concept of certainty in quantum mechanics. Particularly, we show how the probability-1 predictions derived from pure quantum states highlight a fundamental difference between our Bayesian approach, on the one hand, and Copenhagen and similar interpretations on the other. We first review the main arguments for the general claim that probabilities always represent degrees of belief. We then argue that a quantum state prepared by some physical device always depends on an agent's prior beliefs, implying that the probability-1 predictions derived from that state also depend on the agent's prior beliefs. Quantum certainty is therefore always some agent's certainty. Conversely, if facts about an experimental setup could imply agent-independent certainty for a measurement outcome, as in many Copenhagen-like interpretations, that outcome would effectively correspond to a preexisting system property. The idea that measurement outcomes occurring with certainty correspond to preexisting system properties is, however, in conflict with locality. We emphasize this by giving a version of an argument of Stairs [(1983). Quantum logic, realism, and value-definiteness. Philosophy of Science, 50, 578], which applies the Kochen–Specker theorem to an entangled bipartite system.     

Are Analysts' Loss Functions Asymmetric?

Despite displaying a statistically significant optimism bias, analysts' earnings forecasts are an important input to investors’ valuation models. Understanding the possible reasons for any bias is important if information is to be extracted from earnings forecasts and used optimally by investors. Extant research into the shape of analysts' loss functions explains optimism bias as resulting from analysts minimizing the mean absolute forecast error under symmetric, linear loss functions. When the distribution of earnings outcomes is skewed, optimalforecasts can appear biased. In contrast, research into analysts' economic incentives suggests that positive and negative earnings forecast errors made by analysts are not penalized or rewarded symmetrically, suggesting that asymmetric loss functions are an appropriate characterization. To reconcile these findings, we exploit results from economic theory relating to the Linex loss function to discriminate between the symmetric linear loss and the asymmetric loss explanations of analyst forecast bias. Under asymmetric loss functions optimal forecasts will appear biased even if earnings outcomes are symmetric. Our empirical results support the asymmetric loss function explanation. Further analysis also reveals that forecast bias varies systematically across firm characteristics that capture systematic variation in the earnings forecast error distribution. Copyright © 2011 John Wiley & Sons, Ltd.