{"title":"宏观经济变量预测准确性的不确定性下限和上限","authors":"Victor Olkhov","doi":"arxiv-2408.04644","DOIUrl":null,"url":null,"abstract":"We consider the randomness of values and volumes of market deals as a major\nfactor that describes lower bounds of uncertainty and upper limits on the\naccuracy of the forecasts of macroeconomic variables, prices, and returns. We\nintroduce random macroeconomic variables, whose average values coincide with\nusual macroeconomic variables, and describe their uncertainty by coefficients\nof variation that depend on the volatilities, correlations, and coefficients of\nvariation of random values or volumes of trades. The same approach describes\nbounds of uncertainty and limits on the accuracy of forecasts for growth rates,\ninflation, interest rates, etc. Limits on the accuracy of forecasts of\nmacroeconomic variables depend on the certainty of predictions of their\nprobabilities. The number of predicted statistical moments determines the\nveracity of macroeconomic probability. To quantify macroeconomic 2nd\nstatistical moments, one needs additional econometric methodologies, data, and\ncalculations of variables determined as sums of squares of values or volumes of\nmarket trades. Forecasting of macroeconomic 2nd statistical moments requires\n2nd order economic theories. All of that is absent and for many years to come,\nthe accuracy of forecasts of the probabilities of random macroeconomic\nvariables, prices, and returns will be limited by the Gaussian approximations,\nwhich are determined by the first two statistical moments.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"23 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lower bounds of uncertainty and upper limits on the accuracy of forecasts of macroeconomic variables\",\"authors\":\"Victor Olkhov\",\"doi\":\"arxiv-2408.04644\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the randomness of values and volumes of market deals as a major\\nfactor that describes lower bounds of uncertainty and upper limits on the\\naccuracy of the forecasts of macroeconomic variables, prices, and returns. We\\nintroduce random macroeconomic variables, whose average values coincide with\\nusual macroeconomic variables, and describe their uncertainty by coefficients\\nof variation that depend on the volatilities, correlations, and coefficients of\\nvariation of random values or volumes of trades. The same approach describes\\nbounds of uncertainty and limits on the accuracy of forecasts for growth rates,\\ninflation, interest rates, etc. Limits on the accuracy of forecasts of\\nmacroeconomic variables depend on the certainty of predictions of their\\nprobabilities. The number of predicted statistical moments determines the\\nveracity of macroeconomic probability. To quantify macroeconomic 2nd\\nstatistical moments, one needs additional econometric methodologies, data, and\\ncalculations of variables determined as sums of squares of values or volumes of\\nmarket trades. Forecasting of macroeconomic 2nd statistical moments requires\\n2nd order economic theories. All of that is absent and for many years to come,\\nthe accuracy of forecasts of the probabilities of random macroeconomic\\nvariables, prices, and returns will be limited by the Gaussian approximations,\\nwhich are determined by the first two statistical moments.\",\"PeriodicalId\":501273,\"journal\":{\"name\":\"arXiv - ECON - General Economics\",\"volume\":\"23 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - ECON - General Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.04644\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.04644","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lower bounds of uncertainty and upper limits on the accuracy of forecasts of macroeconomic variables
We consider the randomness of values and volumes of market deals as a major
factor that describes lower bounds of uncertainty and upper limits on the
accuracy of the forecasts of macroeconomic variables, prices, and returns. We
introduce random macroeconomic variables, whose average values coincide with
usual macroeconomic variables, and describe their uncertainty by coefficients
of variation that depend on the volatilities, correlations, and coefficients of
variation of random values or volumes of trades. The same approach describes
bounds of uncertainty and limits on the accuracy of forecasts for growth rates,
inflation, interest rates, etc. Limits on the accuracy of forecasts of
macroeconomic variables depend on the certainty of predictions of their
probabilities. The number of predicted statistical moments determines the
veracity of macroeconomic probability. To quantify macroeconomic 2nd
statistical moments, one needs additional econometric methodologies, data, and
calculations of variables determined as sums of squares of values or volumes of
market trades. Forecasting of macroeconomic 2nd statistical moments requires
2nd order economic theories. All of that is absent and for many years to come,
the accuracy of forecasts of the probabilities of random macroeconomic
variables, prices, and returns will be limited by the Gaussian approximations,
which are determined by the first two statistical moments.