Macroeconomic forecasts under the prism of error-correction models

When the error correction term exhibits persistence, its change may convey useful information about short-run economic dynamics which, if not taken sufficiently into account by a forecasting model, may be associated with predictable forecast errors. Such errors are documented in the DRI forecasts for the U.S. consumption, GNP and imports. The strong results, together with the very general assumptions behind the conceptual framework, suggest that similar predictable errors may be pervasive in the forecasts of other large-scale econometric models.     

Conditions under which index models are useful: Reply to bio-index commentaries

This paper summarizes the key conditions under which the index method is valuable for forecasting and describes the procedures one should use when developing index models. The paper also addresses the specific concern of selecting inferior candidates when using the bio-index as a nomination helper. Political decision-makers should not use the bio-index as a stand-alone method but should combine forecasts from a variety of different methods that draw upon different information.     

The importance of demographics in economic analysis: the unusual suspects.

"The proper use of demographic information can add significantly to the ability to analyze and forecast economic activity. With housing, short-run forecasting is difficult and long-term forecasting impossible without considering changing demographic factors. Combining detailed demographic analysis with sound structural econometric modeling of the cyclical factors underlying the demand for and the supply of housing has resulted in significantly more accurate analyses and forecasts of the [U.S.] housing market."     

Forecasting volatility

The forecasting ability of the most popular volatility forecasting models is examined and an alternative model developed. Existing models are compared in terms of four attributes: (1) the relative weighting of recent versus older observations, (2) the estimation criterion, (3) the trade‐off in terms of out‐of‐sample forecasting error between simple and complex models, and (4) the emphasis placed on large shocks. As in previous studies, we find that financial markets have longer memories than reflected in GARCH(1,1) model estimates, but find this has little impact on outofsample forecasting ability. While more complex models which allow a more flexible weighting pattern than the exponential model forecast better on an in‐sample basis, due to the additional estimation error introduced by additional parameters, they forecast poorly out‐of‐sample. With the exception of GARCH models, we find that models based on absolute return deviations generally forecast volatility better than otherwise equivalent models based on squared return deviations. Among the most popular time series models, we find that GARCH(1,1) generally yields better forecasts than the historical standard deviation and exponentially weighted moving average models, though between GARCH and EGARCH there is no clear favorite. However, in terms of forecast accuracy, all are dominated by a new, simple, nonlinear least squares model, based on historical absolute return deviations, that we develop and test here. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:465–490, 2005     

Forecasting the World Economy in the Short Term

(1433) Audrone Jakaitiene and Stephane Dees Forecasting the world economy is a difficult task given the complex interrelationships within and across countries. This paper proposes a number of approaches to forecast short‐term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of forecast accuracy and, second, at checking whether methods forecasting directly aggregate variables (direct approaches) outperform methods based on the aggregation of country‐specific forecasts (bottom‐up approaches). Overall, all methods perform better than a simple benchmark for short horizons (up to 3 months ahead). Among the forecasting approaches used, factor models appear to perform the best. Moreover, direct approaches outperform bottom‐up ones for real variables, but not for prices. Finally, when country‐specific forecasts are adjusted to match direct forecasts at the aggregate levels (top‐down approaches), the forecast accuracy is neither improved nor deteriorated (i.e. top‐down and bottom‐up approaches are broadly equivalent in terms of country‐specific forecast accuracy).     

Internet use in Ukraine's Orange Revolution

The Ukrainian presidential elections of 2004 witnessed a massive uprising of the Ukrainian electorate against the incumbent government's tactics of vote rigging and ballot fraud. In this paper, ten Ukrainian websites focusing on the election are critically evaluated and compared by analyzing their website activity data. Despite lacking access to mass media, supporters of democratic change compensated through skillful use of the internet to recruit volunteers, raise funds, organize campaigns, report breaking news, and garner the sympathy of the global democratic community. Comparisons with broader web usage are made, and social and business implications are suggested.     

Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment

Inaccurate forecasts of demand during promotions diminish the already meager profit margins of retailers. No forecasting method described in the literature can accurately account for the combination of seasonal sales variations and promotion-induced sales peaks over forecasting horizons of several weeks or months. We address this research gap by developing a forecasting method for seasonal, frequently promoted products that generates accurate predictions, can handle a large number of sales series, and requires minimal training data. In our method's first stage, we forecast the seasonal sales cycle by fitting a harmonic regression model to a decomposed training set, which excludes promotional and holiday sales, and then extrapolate that model to a testing set. In the second stage, we integrate the resulting seasonal forecast into a multiplicative demand function that accounts for consumer stockpiling and captures promotional and holiday sales uplifts. The final model is then fitted using ridge regression. We use sales data from a grocery retailing chain to compare the forecasting accuracy of our method with popular seasonal and promotion demand forecasting models at multiple aggregation levels for both short and long forecasting horizons. The significantly more accurate forecasts generated by our model attest to the merit of the approach developed here.     

Advertising competition in presidential elections

Presidential candidates purchase advertising based on each state’s potential to tip the election. The structure of the Electoral College concentrates spending in battleground states, such that a majority of voters are ignored. We estimate an equilibrium model of multimarket advertising competition between candidates that allows for endogenously determined budgets. In a Direct Vote counterfactual, we find advertising would be spread more evenly across states, but total spending levels can either decrease or increase depending on the contestability of the popular vote. Spending would increase by 13 % in the extremely narrow 2000 election, but would decrease by 54 % in 2004. These results suggest that the Electoral College greatly increases advertising spending in typical elections.     

Using Neural Nets to Forecast the Unemployment Rate

The paper identifies leading indicators of the unemployment rate. Forecasts of the unemployment rate are obtained with an econometric model, and with an artificial neural network. Both model-based forecasts outperform forecasts from the Survey of Professional Forecasters. This is important because the unemployment rate forecast from the Survey of Professional Forecasters has outperformed other forecasts based on time-series models to the point that some observers view it as a proxy for a full-information forecast. JEL Classification C450,E240     

A Comparison of Consensus and BVAR Macroeconomic Forecasts

This paper provides a one-month-ahead, macroeconomic, Bayesian Vector Autoregressive (BVAR) forecasting approach that offers several advantages over conventional short-term forecasting procedures. In particular, it produces more accurate forecasts than the Bloomberg consensus forecasts, on average, for 20 major macroeconomic variables. In addition to a quantitative comparison of BVAR and Bloomberg consensus forecast, the paper focuses on five important areas of macroeconomic forecasting: the role of short-term macroeconomic forecasting, the importance of a robust forecasting approach, the importance of timing of data releases, forecast evaluation criteria, and the importance of changing model specifications as conditions warrant.     

Präsidentschaftswahl zeigt Frankreichs Spaltung

French presidential elections 2022 have significantly strengthened anti-EU populists. Traditional mainstream centrists are not among the big three election winners, while far-right Marine Le Pen deftly addressed the plight of the disengaged. The country’s rural-urban divide masks significant differences in voting motives; Paris is a stark outlier. Unemployment rates, low skills, and car dependency fuel Le Pen’s support, as well as xenophobia. The province has fought back against neglect by electing populists. It is doubtful whether place-based policies will solve the structural cause of the populist tendency by the next presidential election in five years.

     

Top‐Down Versus Bottom‐Up Demand Forecasts: The Value of Shared Point‐of‐Sale Data in the Retail Supply Chain

Accurate demand forecasts are critical to maintaining customer service levels and minimizing total costs, yet increasingly difficult to achieve. Using weekly point‐of‐sale (POS) and order data for 10 ready‐to‐eat cereal stock‐keeping units from 18 regional U.S. grocery distribution centers, this research empirically investigates two demand forecasting issues: (1) the accuracy of top‐down versus bottom‐up demand forecasts; and (2) whether shared POS data improve demand forecast accuracy. The results reveal a previously unexplored relationship between demand forecast methodology and the use of shared POS data. We find that the superiority of the top‐down or bottom‐up forecasting as the more accurate demand forecast method depends on whether shared POS data are used.     

Volatility forecasts embedded in the prices of crude-oil options

This paper evaluates the ability of alternative option-implied volatility measures to forecast crude-oil return volatility. We find that a corridor implied volatility measure that aggregates information from a narrow range of option contracts consistently outperforms forecasts obtained by the popular Black–Scholes and model-free volatility expectations, as well as those generated by a realized volatility model. This measure ranks favorably in regression-based tests, delivers the lowest forecast errors under different loss functions, and generates economically significant gains in volatility timing exercises. Our results also show that the Chicago Board Options Exchange's “oil-VIX” index performs poorly, as it routinely produces the least accurate forecasts.     

CREATING ORDER FORECASTS: POINT‐OF‐SALE OR ORDER HISTORY?

Point‐of‐sale (POS) data, shared by retailers, is often touted as the solution to suppliers' ongoing challenge of accurate order forecasting. However, we find neither empirical evidence of increased order forecast accuracy from the literature, nor consistent use of POS data in suppliers' order forecasting processes. Using a sample containing weekly POS and order data for 10 ready‐to‐eat (RTE) cereal stock‐keeping‐units (SKU's), 7 yogurt SKU's, and 7 canned soup SKU's from 18 retailer distribution centers (DC's) throughout the U.S, our research compares historical POS and order data as order forecasting inputs and finds that POS data does not always outperform order data in terms of order forecast accuracy. While we did find that POS data is a better forecast input in a majority of the forecasts and that on average POS data produces a lower order forecast error, we find that there remain a large number of forecasts where order data is a better predictor than is POS data. Hence, we operationalize this comparison in terms of the frequency and magnitude of order forecast improvement based on POS data. We then hypothesize affecting factors and empirically test these relationships.     

Managerial intervention in forecasting. An empirical investigation of forecast manipulation

There has been a continuing debate in the forecasting literature concerning the relative advantages of quantitative versus qualitative approaches to forecasting. However, although a considerable number of studies have contrasted the merits of the two approaches, relatively few efforts have investigated the application of human judgement on forecasts generated by quantitative forecasting models. This study is an empirical investigation of the effects of human intervention on forecast accuracy. It examines the consequences of managerial manipulation of sales forecasts initially produced by a smoothing model. A total of 281 products are investigated using multiple measures of forecast accuracy and the effects of subjective revision are discussed in terms of size, directionality, and distribution of errors. The results indicate that human intervention can lead to an overall improvement in forecast performance as reflected in a reduction in the variability and absolute size of forecasting errors. The results also show that more forecasts are improved by manipulation than are degraded. Finally, there is some indication that subjective revision may result in an increase in overall forecasting bias. These findings appear to conflict with previous evidence, however, the empirical setting of the present study is specific to shortterm sales forecasting which is not strictly comparable with the settings of earlier efforts.     

Incivility in political advertisements: a look at the 2012 US presidential election

The 2012 presidential election reached new heights in dollars spent and the rancorous nature of advertisements emanating from candidates and other interested parties. While ‘going negative’ has become a well-known tactic in political campaigns, several observers believe that the level of acrimony crossed the line between civil discourse essential to democratic societies and uncivil haranguing that has little to do with election issues. To explore the nuances of this topic, we open with a discussion of limited cross-disciplinary research on incivility in political discourse so that its essential nature is exposed, differentiating it from more common uses of negativity. Our empirical work examined about 350 television advertisements that were shown between the presidential debates of 2012 and the November elections. Levels of civility and incivility are noted and implications for the future of political advertising are provided.     

The HWWA econometric model for economic forecasts

Econometric models are often used to forecast economic developments. Does the use of computer-based econometric procedures help sharpen our view of the future or are we blinkered by the supposed ‘precision’ of such models? What problems arise when building national economic models and when these models are used for forecasting purposes? These issues are examined with reference to the HWWA model.     

The forecast quality of CBOE implied volatility indexes

We examine the forecast quality of Chicago Board Options Exchange (CBOE) implied volatility indexes based on the Nasdaq 100 and Standard and Poor's 100 and 500 stock indexes. We find that the forecast quality of CBOE implied volatilities for the S&P 100 (VXO) and S&P 500 (VIX) has improved since 1995. Implied volatilities for the Nasdaq 100 (VXN) appear to provide even higher quality forecasts of future volatility. We further find that attenuation biases induced by the econometric problem of errors in variables appear to have largely disappeared from CBOE volatility index data since 1995. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:339–373, 2005     

Examining political connections to study institutional change: Evidence from two unexpected election outcomes in South Korea

We argue that the analysis of multiple political connections in an event study framework can improve the study of institutional change. Based on a unique data set of multiple political relationships of 4,936 South Korean board of director members, we show that the large business conglomerates, the chaebol, did not benefit from the unexpected conservative election victories in the 2012 South Korean parliamentary and presidential elections. Personal connections to the presidential candidates and to the opposition party were relevant for the stock returns of small firms. Our findings suggest that Korea's political economy has evolved into a hybrid regime in which the political power of large multinational corporations is limited, but political connections still matter for smaller firms. The corruption scandal that led to the impeachment of President Park in 2017 and the long‐term development of market capitalisation appear to be congruent with the results of our study.     

Information theoretics-based technoeconomic forecasting: application to telecommunication service industry

Addressed in this paper is an information-theoretics inspired econometric approach to evolve a forecast model on the growth profiles of telecommunication services. It includes cohesively, both the profile of user-economics as well as the technological framework of service providers; and the forecasting suite is built on the basis of information-theoretics considerations. It refers to a modified Fisher-Kaysen method that accounts for “free-market” principles and uses entropy (stochastic) details of differential changes in the short-run (state) variables of the growth function. Further, the principle of proportional fairness is appropriately invoked and the heuristics of users’ willingness-to-pay for the network resources allocated to them is presumed. A few simulation examples using real-world data are furnished to validate the forecast algorithm developed. The computed results on forecasting presented depict a “cone-of-forecast” in the ex ante regime of the examples considered. Relevance of this method to modern aspects of managerial approach and market penetration vis-à-vis forecast trends is indicated. Shortcomings of the method are identified.