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Business cycle - Essay Example

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Calibrated business model is a concept applied to the selection of constraint values due to macroeconomic confirmation and compares the model’s calculations concerning the variances and co-variances of different series with those in the statistics (Romer 217-220). The…
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HW4b Calibrated Business Model Calibrated business model is a concept applied to the selection of constraint values due to macroeconomic confirmation and compares the model’s calculations concerning the variances and co-variances of different series with those in the statistics (Romer 217-220). The methodology entails the estimation of casual or unrelated variances, which are quantified to confirm the existence of a historical data. This implies that the model uses real-business concepts against empirical values to ascertain the various sequences in the data. Calibration imposes models on macroeconomic disciplines for planning purposes and the detection of errors. For instance, the choice of the parameter values depends on the macroeconomic evidences available for comparison purposes (Romer 217-220). It is also apparent that the calibration model can result in the statistical rejection of adoption of a concept in business operations. This is because most models are always difficult to interpret and a model that fits the data properly, within different dimensions, may be statistically rejected if one aspect is omitted (Summers 129-148). A model may still be ignored if the data is consistent with a wide variety of options.
The models are calibrated to ensure that they undergo testing via the formal econometric methods. This is normally done through the identification of available evidence against the variances of other data in the series. For instance, the comparison of labor against capital and output can adopt the calibrated model (Romer 217-220). This means that government intervention and technological components do not apply in the final determination of outputs. The calibration is different from other models like the Solow theory that assumes the prevalence of technology in productivity. However, an alternative model for calibration is the proper assessment of fully specified models in which the researchers determine models using macroeconomic evidences (Romer 217-220). This focuses on the main building aspects or through the evaluation of the model’s consistency with other statistics.
According to Summers (p. 129-148), calibration model enables economists to apply different concepts in the interpretation of their business performance. The historical data comparison helps in speculation purposes in which a firm can change its methods to suit the trend. This is done through the relation between variables and independent factors present in the industry (Summers 129-148). As a result, the real-business cycle model relies on assumptions that differentiate it from other methods, such as no government involvement. The reality is that government is a vital player in economic decisions, but economists do not want to depend on their policies.
Conclusion
The application and testing of theories, and calibrated business cycle model is among the practical concepts adopted by economists. It involves the comparison of variance and co-variance data with the historical sequences in the statistics. Calibration imposes models on macroeconomic disciplines for planning purposes and the detection of errors. The performance of calibrated business-cycle models is through the identification of available evidence against the variances of other data in the series. This is an indication that the approach is favorable for various applications, such as the provision of quality analysis of the performance of different variances. As a result, calibration business-cycle model uses real-business concepts against empirical values to ascertain the various sequences in the data.
Works Cited
Romer, David. Advanced Macroeconomics. New York: McGraw-Hill Higher Education, 2010.
Print.
Summers, Lawrence H. "The Scientific Illusion in Empirical Macroeconomics." Scandinavian
Journal of Economics 93.2 (1991): 129-148. Read More
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