Formalizing judgemental adjustment of model-based forecasts

In business and in macroeconomics it is common practice to use econo-metric models to generate forecasts. These models can take any degree ofsophistication. Sometimes it is felt by an expert that the model-based fore-cast needs adjustment. This paper makes a plea for a formal approach to suchan adjustment, more precise, for the creation of detailed logbooks which con-tain information on why and how model-based forecasts have been adjusted.The reasons for doing so are that such logbooks allow for (i) the preservationof expert knowledge, (ii) for the possible future modi¯cation of econometricmodels in case adjustment is almost always needed, and (iii) for the evaluationof adjusted forecasts. In this paper I put forward an explicit mathematicalexpression for a judgementally adjusted model-based forecast. The key pa-rameters in the expression should enter the logbook. In a limited simulationexperiment I illustrate an additional use of this expression, that is, lookingwith hindsight if adjustment would have led to better results. The resultsof the simulation suggest that always adjusting forecasts leads to very poorresults. Also, it is documented that small adjustments are better that largeadjustments, even in case large adjustments are felt necessary.forecasting;judgemental adjustment

Model selection for forecast combination

In this paper it is advocated to select a model only if it significantly contributes to the accuracy of a combined forecast. Using hold-out-data forecasts of individual models and of the combined forecast, a useful test for equal forecast accuracy can be designed. An illustration for real-time forecasts for GDP in the Netherlands shows its ease of use.model selection;forecast combination

On the Bass diffusion theory, empirical models and out-of-sample forecasting

The Bass (1969) diffusion theory often guides the construction of forecasting models for new product diffusion. To match the model with data, one needs to put forward a statistical model. This paper compares four empirical versions of the model, where two of these explicitly incorporate autoregressive dynamics. Next, it is shown that some of the regression models imply multi-step ahead forecasts that are biased. Therefore, one better relies on the simulation methods, which are put forward in this paper. An empirical analysis of twelve series (Van den Bulte and Lilien 1997) indicates that one-step ahead forecasts substantially improve by including autoregressive terms and that simulated two-step ahead forecasts are quite accurate.forecasting;diffusion

Do we make better forecasts these days? A survey amongst academics

This paper presents the results of a survey held amongst all editorial board members of six journals. These journals in part focus on the development of models and methods for forecasting. The key question was whether one believes that the forecasting discipline has made progress in the last three decades. Amongst various results, the most important one is that modest progress has been made, although the profession seems far from satisfied. This progress appears to be mainly due to the increase in computing power and the fact that we are better able to incorporate important data features in our models. Additionally, progress could have been faster if we somehow were to include the opinions of experts. These last two findings define two important topics on the research agenda.forecasting;models and methods for forecasting

Forecasting in marketing

With the advent of advanced data collection techniques, there is an increased interest in using econometric models to support decisions in marketing. Due to the sometimes specific nature of variables in marketing, the discipline uses econometric models that are rarely, if ever, used elsewhere. This chapter deals with techniques to derive forecasts from these models. Due to the intrinsic non-linear nature of these models, these techniques draw heavliy on simulation techniques.marketing;forecasting;unobserved heterogeneity;Koyck model;attraction model;Bass model

Forecasting Sales

This chapter deals with forecasting sales (in units or money), where an explicit distinction is made between sales of durable goods (computers, cars, books) and sales of utilitarian products (SKU level in supermarkets). Invariably, sales forecasting amounts to a combination of statistical modeling and an expert’s touch. Models for durable goods sales are usually based on (variants of) the Bass model, while SKU sales forecasts are typically based on simple extrapolation methods. Forecast evaluation is not standard due to the interaction of model and expert.diffusion;SKU-level sales;durable goods;human judgment;sales forecasting

Forecasting 1 to h steps ahead using partial least squares.

This paper proposes a methodology to jointly generate optimal forecastsfrom an autoregression of order p for 1 to h steps ahead. The relevant model isa Partial Least Squares Autoregression, which is positioned in between a singleAR(p) model for all forecast horizons and different AR models for differenthorizons. Representation, estimation and forecasting using the new model arediscussed. An illustration for US industrial production shows the merits ofthe methodology.forecasting;autoregression;partial least squares

A sequential approach to testing seasonal unit roots in high frequency data

In this paper we introduce a sequential seasonal unit root testing approach which explicitly addresses its application to high frequency data. The main idea is to see which unit roots at higher frequency data can also be found in temporally aggregated data. We illustrate our procedure to the analysis of monthly data, and we find, upon analysing the aggregated quarterly data, that a smaller amount of test statistics can sometimes be considered. Monte Carlo simulation and empirical illustrations emphasize the practical relevance of our method.high frequency data;unit root testing

Financial innumeracy: Consumers cannot deal with interest rates

Consumers often have to make decisions involving computations with interest rates. It is well known from the literature that computations with percentages and thus with interest rates amount to a difficult task. We survey a large group of consumers, and we find that questions on interest rates are answered correctly in about 20% of the cases, which in our setting amounts to a random choice. Additional to the available literature, we also document that consumers are too optimistic in the sense that they believe loans are paid off sooner than is true, which provides empirical evidence of self-serving bias. We further find that optimism can be reduced by increasing the monthly payments. The results are robust to corrections for general numeracy.interest rates;D14;D91;financial innumeracy;numeracy;percentages

Common large innovations across nonlinear time series

We propose a multivariate nonlinear econometric time series model, which can beused to examine if there is common nonlinearity across economic variables. Themodel is a multivariate censored latent effects autoregression. The key featureof this model is that nonlinearity appears as separate innovation-likevariables. Common nonlinearity can then be easily defined as the presence ofcommon innovations. We discuss representation, inference, estimation anddiagnostics. We illustrate the model for US and Canadian unemployment and findthat US innovation variables have an effect on Canadian unemployment, and notthe other way around, and also that there is no common nonlinearity across theunemployment variables.Nonlinearity;Censored latent effects autoregression;Common features