 Relegating - The GDP Structural Modelling Strategy, The Dynamics in Time-Series Data: Short-Run Shocks, Disequilibrium Shocks and Innovative Shocks to NuisanceMPRA Paper from University Library of Munich, Germany Abstract: We argue that many confusions relating to the system of methods used in a particular area of study economics and econometrics, if we a considering in time-series forecasting might be considered as arising out of ambivalence or inconclusiveness about the error terms. Relationships between macroeconomic time series are fallacious and, inevitably, the early sentimental frocks-and-romance brigade econometricians concluded that any estimated regression equation, in statistics, an equation constructed to model the relationship between dependent and independent variables would only fit with errors. Beyond dispute, Slutsky concluded that these errors could be interpreted as shocks that constitute wherefore, force behind business cycles. On the other hand, Frisch adjudecated to dissect the errors further into two categories: stimuli, which are analogous to shocks, and irregularity. However, the theory is constraint of providing a statistical framework. Furthermore, Haavelmo interpreted the error term in equations as giving, rather the statistical groundwork for econometric models and making sense that they match up to a priori dispersal assumptions specified in structural models of the stochastic dynamic general equilibrium type, later in 80ths known as simultaneous-equations models intro SDGE approach (proposed in 1982 by Kydland & Prescott.) Because in those days economies required an interpretation in a framework with rather static theoretical models , forming part of the structure of a building simultaneous-equations relegated the dynamics in time-series data frequently to frustration. Relauch of errors interpretation as the shocks in theoretical models came from model-consistent expectations, in that agents inside the model are assumed to "know the model" and on average take the model's predictions as valid. Forecasting of any non-stationary time-series as in our paper are intended to develop vector autoregression modeling giving freshness to the decade in which economic science and econometrics will be put to the test and obstacles. The so-called Sargent, Hansen, and Tallarini’s risk-sensitive permanent income model, and one and two-country stochastic growth models, decomposes the dynamics of the modeled variable into three parts: short-run shocks, disequilibrium shocks, and innovative residuals, with only the first two of these sustaining an economic interpretation. Keywords: economic growth and aggregate productivity; the gross domestic product; innovation and communications; cross-country output convergence; prediction and forecasting methods; time series analysis and modelling; ARIMA modelling; Box–Jenkins method. (search for similar items in EconPapers) Published in International Scientific Conference "Economic and Social Implications of the COVID-19 Pandemic: Analysis, Forecasts and Consequences Mitigation Strategies". 2020.I(2020): pp. 51-53 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:112857 Access Statistics for this paper More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC. |
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