scholarly journals The Success of Business Forecasting: Comparisons across Industries, Countries and Time

2020 ◽  
Vol 13 (10) ◽  
pp. 108
Author(s):  
Tobias F. Rötheli
Keyword(s):  
Forecast Accuracy ◽  
Data Set ◽  
Electric Machinery ◽  
Forecasting Performance ◽  
Business Forecasting ◽  
The Stability ◽  
Changes Over Time ◽  
German Data ◽  
Historical Periods ◽  
Over Time

This study assesses the accuracy of forecasts by industry branches. Such an investigation provides a perspective on the relative benefits of forecasting in different industries. Accuracy of forecasting is assessed by econometrically investigating expectations data on firms’ production drawn from surveys covering manufacturing. Such data is available for only few countries and few historical periods. We study U.S. data covering the 1980s and German data over the period from 1991 to 2018. We first present rankings of industries according to forecast accuracy for both countries. Then the historical gap between the two countries’ data set is put to use to assess the stability and the dynamics in the relevance of forecasting in different branches of industry. We identify several industries that – across time and place – are among the most (e.g., electric machinery) and least accurate forecasters (e.g., the food industry). By contrast in some industries forecasting performance appear to undergo noticeable changes over time: the reported evidence suggests that forecasting has lost some of its potential in the printing and textile industries while gaining over time in the nonelectric machinery and in the metals industry. The findings can help management to make decisions regarding the allocation of resources to forecasting.

scholarly journals Stability of risk attitude, agricultural policies and production shocks: evidence from Italy

2020 ◽  
Author(s):  
Martina Bozzola ◽  
Robert Finger
Keyword(s):  
European Union ◽  
Panel Data ◽  
Agricultural Policy ◽  
Risk Preference ◽  
Risk Attitude ◽  
The European Union ◽  
Agricultural Policies ◽  
The Stability ◽  
Changes Over Time ◽  
Over Time

Abstract This article investigates the stability of farmers’ risk attitude over time. To this end, we estimate responses to changes in agricultural policies and production shocks. We use a unique panel data of over 36,000 Italian farms specialised in cereals, during the period 1989–2009. We find evidence of risk preference changes over time in response to changes in the European Union Common Agricultural Policy and possibly after a drought-induced production shock.

Tree growth inference and prediction when the point of measurement changes: modelling around buttresses in tropical forests

2009 ◽  
Vol 25 (1) ◽  
pp. 1-12 ◽  
Author(s):  
C. Jessica E. Metcalf ◽  
James S. Clark ◽  
Deborah A. Clark
Keyword(s):  
Tree Growth ◽  
Biomass Accumulation ◽  
Relative Magnitude ◽  
Barro Colorado Island ◽  
Full Data ◽  
Data Set ◽  
Measurement Height ◽  
Changes Over Time ◽  
Climate Responses ◽  
Over Time

Abstract:Estimation of tree growth is generally based on repeated diameter measurements. A buttress at the height of measurement will lead to overestimates of tree diameter. Because buttresses grow up the trunk through time, it has become common practice to increase the height of measurement, to ensure that measurements remain above the buttress. However, tapering of the trunk means that increasing measurement height will bias estimates of diameter downward by up to 10% per m of height. This bias could affect inference concerning species differences and climate effects on tree demography and on biomass accumulation. Here we introduce a hierarchical state space method that allows formal integration of data on diameter taken at different heights and can include individual variation, temporal effects or other covariates. We illustrate our approach using species from Barro Colorado Island, Panama, and La Selva, Costa Rica. Results include trends that are consistent with some of those previously reported for climate responses and changes over time, but differ in relative magnitude. By including the full data-set and accounting for bias and variation among individuals and over time, our approach allows for quantification of climate responses and the uncertainty associated with measurements and the underlying growth process.

scholarly journals Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions

2020 ◽  
Vol 34 (02) ◽  
pp. 2236-2243 ◽  
Author(s):  
Weiran Shen ◽  
Binghui Peng ◽  
Hanpeng Liu ◽  
Michael Zhang ◽  
Ruohan Qian ◽  
...  
Keyword(s):  
Mechanism Design ◽  
Search Engine ◽  
Dynamic Pricing ◽  
Social Systems ◽  
Sponsored Search ◽  
Data Set ◽  
Sponsored Search Auctions ◽  
Reserve Prices ◽  
Changes Over Time ◽  
Over Time

In many social systems in which individuals and organizations interact with each other, there can be no easy laws to govern the rules of the environment, and agents' payoffs are often influenced by other agents' actions. We examine such a social system in the setting of sponsored search auctions and tackle the search engine's dynamic pricing problem by combining the tools from both mechanism design and the AI domain. In this setting, the environment not only changes over time, but also behaves strategically. Over repeated interactions with bidders, the search engine can dynamically change the reserve prices and determine the optimal strategy that maximizes the profit. We first train a buyer behavior model, with a real bidding data set from a major search engine, that predicts bids given information disclosed by the search engine and the bidders' performance data from previous rounds. We then formulate the dynamic pricing problem as an MDP and apply a reinforcement-based algorithm that optimizes reserve prices over time. Experiments demonstrate that our model outperforms static optimization strategies including the ones that are currently in use as well as several other dynamic ones.

scholarly journals An investigation of biases in web search engine query suggestions

2019 ◽  
Vol 44 (2) ◽  
pp. 365-381 ◽  
Author(s):  
Malte Bonart ◽  
Anastasiia Samokhina ◽  
Gernot Heisenberg ◽  
Philipp Schaer
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Query Suggestion ◽  
Data Set ◽  
Content Type ◽  
Web Search Engine ◽  
The Stability ◽  
Query Suggestions ◽  
Over Time

Purpose Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017. Design/methodology/approach This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time. Findings By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often. Originality/value This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.

De plaats van het voorzetselvoorwerp

2014 ◽  
Vol 19 (2) ◽  
pp. 221-244 ◽  
Author(s):  
Jack Hoeksema
Keyword(s):  
Word Order ◽  
Data Set ◽  
Corpus Study ◽  
Prepositional Phrases ◽  
Subordinate Clauses ◽  
Word Order Variation ◽  
Order Variation ◽  
Changes Over Time ◽  
Regular Position ◽  
Over Time

Abstract This paper presents the results of a corpus study of Dutch complement PPs. On the basis of a collection of 3400 occurrences in negative sentences, the four major word order patterns (regular position, scrambling order, topicalization and extraposition) are studied, both in main and subordinate clauses, and linked to the properties of the prepositional phrases, in particular weight and definiteness. Greater weight corresponds to higher likelihood of extraposition, and definiteness to higher likelihood of scrambling and topicalization. This corresponds well with earlier studies of word order variation in Dutch, but had not been established for the class of complement PPs. Among definite phrases, PPs with so-called R-pronouns, such as hieraan ‘here-on’ and daarvan ‘thereof’ showed especially high preferences for topicalization and scrambling. Negative sentences were selected for this study to avoid cases where regular order and scrambling order could not be distinguished due to lack of adverbial elements in the middle field. The data set is temporally stratified. This made it possible to study changes over time, and the most robust finding was a continuous retreat of the scrambling order throughout the period 1700-2014.

Averaging Across Asset Allocation Models

2015 ◽  
Vol 235 (1) ◽  
pp. 61-81 ◽  
Author(s):  
Peter Schanbacher
Keyword(s):  
Empirical Study ◽  
Asset Allocation ◽  
Risk Function ◽  
Combined Model ◽  
Data Set ◽  
Individual Model ◽  
Risk Functions ◽  
Changes Over Time ◽  
Simple Average ◽  
Over Time

Summary Combination of asset allocation models is rewarding if (i) the applied risk function is concave and (ii) there is no dominating model. We show that most common risk functions are either concave or at least concave in common applications. In a comprehensive empirical study using standard asset allocation models we find that there is no constantly dominating model. The ranking of the models depends on the data set, the risk function and even changes over time. We find that a simple average of all asset allocation models can outperform each individual model. Our contribution is twofold. We present a theory why the combined model is expected to dominate most individual models. In a comprehensive empirical study we show that model combinations perform exceptionally well in asset allocation.

scholarly journals Stability of the Cross Linking Component of the Biosensor Receptor Layer after Addition of Gold Nanoparticles

2021 ◽  
Vol 25 (1) ◽  
pp. 49-52
Author(s):  
Aleksandra Kłos-Witkowska ◽  
Vasyl Martsenyuk
Keyword(s):  
Gold Nanoparticles ◽  
Conformational Changes ◽  
Cross Linking ◽  
Relative Percentage ◽  
Pure Protein ◽  
The Cross ◽  
The Stability ◽  
Changes Over Time ◽  
Over Time ◽  
Receptor Layer

In this study, the stability of the receptor layer component of a biosensor after addition of gold nanoparticles was investigated. Accelerated conformational changes under the influence of Au were demonstrated. The relative percentage changes over time between the pure protein and the Au doped protein were calculated. It was shown that these changes are greater with time and exceed 20 % in the last days of the experiment.

scholarly journals Confidence Calibration in a Multi-year Geopolitical Forecasting Competition

2016 ◽  
Author(s):  
Don A Moore
Keyword(s):  
Forecast Accuracy ◽  
Data Set ◽  
Team Collaboration ◽  
Unique Data ◽  
Confidence Calibration ◽  
And Training ◽  
Over Time ◽  
Good Calibration

This research examines the development of confidence and accuracy over time in the context of forecasting. Although overconfidence has been studied in many contexts, little research examines its progression over long periods of time or in consequential policy domains. This study employs a unique data set from a geopolitical forecasting tournament spanning three years in which thousands of forecasters predicted the outcomes of hundreds of events. We sought to apply insights from research to structure the questions, interactions, and elicitations to improve forecasts. Indeed, forecasters’ confidence roughly matched their accuracy. As information came in, accuracy increased. Confidence increased at approximately the same rate as accuracy, and good calibration persisted. Nevertheless, there was evidence of a small amount of overconfidence (3%), especially on the most confident forecasts. Training helped reduce overconfidence and team collaboration improved forecast accuracy. Together, teams and training reduced overconfidence to 1%. Our results provide reason for tempered optimism regarding confidence calibration and its development over time in consequential field contexts.

Confidence calibration in a multi-year geopolitical forecasting competition (CAC)

2017 ◽  
Author(s):  
Don A Moore ◽  
Elizabeth R. Tenney
Keyword(s):  
Forecast Accuracy ◽  
Data Set ◽  
Team Collaboration ◽  
Unique Data ◽  
Confidence Calibration ◽  
And Training ◽  
Over Time ◽  
Good Calibration

This research examines the development of confidence and accuracy over time in the context of forecasting. Although overconfidence has been studied in many contexts, little research examines its progression over long periods of time or in consequential policy domains. This study employs a unique data set from a geopolitical forecasting tournament spanning three years in which thousands of forecasters predicted the outcomes of hundreds of events. We sought to apply insights from research to structure the questions, interactions, and elicitations to improve forecasts. Indeed, forecasters’ confidence roughly matched their accuracy. As information came in, accuracy increased. Confidence increased at approximately the same rate as accuracy, and good calibration persisted. Nevertheless, there was evidence of a small amount of overconfidence (3%), especially on the most confident forecasts. Training helped reduce overconfidence and team collaboration improved forecast accuracy. Together, teams and training reduced overconfidence to 1%. Our results provide reason for tempered optimism regarding confidence calibration and its development over time in consequential field contexts.

A Timely Intervention: Tracking the Changing Meanings of Political Concepts with Word Vectors

2019 ◽  
Vol 28 (1) ◽  
pp. 87-111 ◽  
Author(s):  
Emma Rodman
Keyword(s):  
Best Practices ◽  
Newspaper Coverage ◽  
Great Promise ◽  
Bootstrap Resampling ◽  
Cultural Meanings ◽  
Data Set ◽  
Research Questions ◽  
Implementation Method ◽  
Changes Over Time ◽  
Over Time

Word vectorization is an emerging text-as-data method that shows great promise for automating the analysis of semantics—here, the cultural meanings of words—in large volumes of text. Yet successes with this method have largely been confined to massive corpora where the meanings of words are presumed to be fixed. In political science applications, however, many corpora are comparatively small and many interesting questions hinge on the recognition that meaning changes over time. Together, these two facts raise vexing methodological challenges. Can word vectors trace the changing cultural meanings of words in typical small corpora use cases? I test four time-sensitive implementations of word vectors (word2vec) against a gold standard developed from a modest data set of 161 years of newspaper coverage. I find that one implementation method clearly outperforms the others in matching human assessments of how public dialogues around equality in America have changed over time. In addition, I suggest best practices for using word2vec to study small corpora for time series questions, including bootstrap resampling of documents and pretraining of vectors. I close by showing that word2vec allows granular analysis of the changing meaning of words, an advance over other common text-as-data methods for semantic research questions.

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