On the external validity of experimental inflation forecasts: A comparison with five categories of field expectations

Cornand, Camille; Hubert, Philippe

doi:10.1016/j.jedc.2019.103746

Cited by 25 publications

(2 citation statements)

References 54 publications

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“…Our results are complementary to Cornand and Hubert (2020) and Bao et al (2017), suggesting that the individual belief heterogeneity is quite persistent over time. Contrary to popularly held views, agents with incorrect expectations may not disappear from the market.…”

Section: Discussionsupporting

confidence: 43%

Investor beliefs in the midst of a market crash and the COVID 19 pandemic: Survey and experimental evidence.

Guerrero¹

2021

Decision

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In March of 2020, the stock market fell 34%, due to the COVID-19 pandemic. How do market crashes affect the way investors form beliefs regarding future market returns? To investigate, we surveyed two mTurk panels during the March 2020 meltdown. The first survey was completed after the S&P 500 fell 9.5% on March 12, and the second survey was distributed the next day on March 13 after S&P 500 rose by 9.3%. In the midst of extreme market volatility and widespread media reporting of investor panic, most participants reported that beliefs were formed thoughtfully and in line with traditional financial advice. We next report the results of a controlled laboratory experiment where participants provided incentivized forecasts and allocated money between a risky and a riskless asset for 56 periods, with one group experiencing a severe market crash. Comparing participant forecasts against six different classes of belief models, we find the best fitting models are a naïve short memory Recency model, and a short memory Cumulative Prospect Theory model, along with an Adaptive Expectations model. The experimental results are consistent with the idea that recent returns encourage extrapolative beliefs. In contrast, the mTurk survey results convey that investors claim to be more thoughtful than short-term extrapolative models suggest. We conclude by suggesting that investors can both be thoughtful and reactionary depending upon the context of the investing environment.

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Section: Discussionsupporting

confidence: 43%

Investor beliefs in the midst of a market crash and the COVID 19 pandemic: Survey and experimental evidence.

Guerrero¹

2021

Decision

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“…The author's approach to the analysis of regional indicators, proposed in the article, develops the ideas previously disclosed in the works of domestic and foreign researchers, in particular Statsenko [1], Barinova [2], Klimanov et al [3], Rahmah [4], Szitasiova et al [5], Rickman [6], Yeah [7], Billon and Marco [8], Fornaro and Wolf [9], Raven and Walrave [10], Michaels [11], Biau and Devroye [12], Arifovic and Petersen [13], Christopeit and Massmann [14], Cornand and Hubert [15], Beechey et al [16], Granitsa [17] and others.…”

Section: Introductionmentioning

confidence: 84%

Cluster analysis of regional indicators using DBSCAN algorithm

Granitsa¹,

Khujayev²

2022

Human Resource Management Within the Framework of Realisation of National Development Goals and Strategic Objectives

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Regional economies are playing an increasingly important role in the development of the national economic complex of the country. The disproportionate development of economies at the mesolevel is associated with a number of risks affecting various markets and industries, which in turn necessitates the development of effective methods for identifying regional clusters and the search for effective methods for assessing the interconnections of regional economic determinants. To conduct the study, the authors accumulated data on 25 indicators reflecting the investment, resource, production and financial performance components of the socio-economic development of Russian regions. Applying machine learning algorithms such as XG Boost, Gradient Boosting, CART, we identified the most significant factor for assessing regional sustainability and established the regional development indicators associated with it by calculating the non-linear correlation coefficient Phi_K. The use of the DBSCAN algorithm allowed us to identify two regional clusters, while per capita consumption, the level of demographic load and urbanization were significant factors for the clustering of regions. The significance of the criteria for combining regions into clusters using the DBSCAN method was established using the construction of a classification tree.

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New DTW Windows Type for Forward- and Backward-Lookingness Examination. Application for Inflation Expectation

Rutkowska

Szyszko

2021

Comput Econ

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This study provides an application of dynamic time warping algorithm with a new window constraint to assess consumer expectations’ information content regarding current and future inflation. Our study’s contribution is the novel application of DTW for testing expectations’ forward-lookingness. Additionally, we modify the algorithm to adjust it for a specific question on the information content of our data. The DTW overcomes constraints of the standard tool that examines forward-lookingness: DTW does not impose assumptions on time series properties. In empirical study we cover seven European counties and compare the DTW outcomes with the results of previous studies in these economies using a standard methodology. The research period covers 2001 to mid-2018. Application of DTW provides information on the degree of expectations’ forward-lookingness. The result, after standardization, are similar to the standard parameters of hybrid specification of expectations. Moreover, the rankings of most forward-looking consumers are replicated. Our results confirm the economic intuition, and they do not contradict previous studies.

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On the external validity of experimental inflation forecasts: A comparison with five categories of field expectations

Cited by 25 publications

References 54 publications

Investor beliefs in the midst of a market crash and the COVID 19 pandemic: Survey and experimental evidence.

Investor beliefs in the midst of a market crash and the COVID 19 pandemic: Survey and experimental evidence.

Cluster analysis of regional indicators using DBSCAN algorithm

New DTW Windows Type for Forward- and Backward-Lookingness Examination. Application for Inflation Expectation

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