Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining

With the rapid development of information technology and globalization of economy, financial data are being generated and collected at an unprecedented rate. Consequently, there has been a dire need of automated methods for effective and proficient utilization of a substantial amount of financial data to help in investment planning and decision-making. Data mining methods have been employed to discover hidden patterns and estimate future tendencies in financial markets. In this article, an improved macroeconomic growth prediction algorithm based on data mining and fuzzy correlation analysis is presented. This study analyzes the sequence of economic characteristics, reorganizes the spatial structure of economic characteristics, and integrates the statistical information of economic data. Using the optimized Apriori algorithm, the association rules between macroeconomic data are generated. Distinct features are extracted according to association rules using the joint distribution characteristic quantity of macroeconomic time series. Moreover, the Doppler parameter of macroeconomic time series growth prediction is calculated, and the residual analysis method of the regression model is used to predict the growth of macroeconomic data. Experimental results show that the proposed algorithm has better adaptability, less computation time, and higher prediction accuracy of economic data mining.

Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis

On September 15th 2020, Arctic sea ice extent (SIE) ranked second-to-lowest in history and keeps trending downward. The understanding of how feedback loops amplify the effects of external CO2 forcing is still limited. We propose the VARCTIC, which is a Vector Autoregression (VAR) designed to capture and extrapolate Arctic feedback loops. VARs are dynamic simultaneous systems of equations, routinely estimated to predict and understand the interactions of multiple macroeconomic time series. The VARCTIC is a parsimonious compromise between full-blown climate models and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our completely unconditional forecast has SIE hitting 0 in September by the 2060’s. Impulse response functions reveal that anthropogenic CO2 emission shocks have an unusually durable effect on SIE – a property shared by no other shock. We find Albedo- and Thickness-based feedbacks to be the main amplification channels through which CO2 anomalies impact SIE in the short/medium run. Furthermore, conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of CO2 emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050’s. Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.

Business-Cycle Anatomy

We propose a new strategy for dissecting the macroeconomic time series, provide a template for the business-cycle propagation mechanism that best describes the data, and use its properties to appraise models of both the parsimonious and the medium-scale variety. Our findings support the existence of a main business-cycle driver but rule out the following candidates for this role: technology or other shocks that map to TFP movements; news about future productivity; and inflationary demand shocks of the textbook type. Models aimed at accommodating demand-driven cycles without a strict reliance on nominal rigidity appear promising. (JEL C22, E10, E32)

Modeling and Extrapolating Arctic Feedback Loops using Macroeconometric Techniques

<p>The minimum extent of arctic sea ice (SIE) in 2019 ranked second-to-lowest in history and is trending downward. Hence, there is an immediate need for flexible statistical modeling approaches that both explain endogenously the trend of SIE and permits its extrapolation to generate a long-run forecast. To that end, we propose the VARCTIC, which is a Vector Autoregression (VAR) specifically designed to capture and extrapolate feedback loops that characterize the Arctic system.  VARs are dynamic simultaneous systems of equations routinely estimated in economics to predict and understand the interactions of multiple macroeconomic time series. The VARCTIC is a compromise between fully structural/deterministic modeling and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our "business as usual" completely unconditional forecast has September SIE hitting 0 around the middle of the century. By studying the impulse response functions of Bayesian VARs including different sets of variables, we single out CO2 shocks as main drivers of the long-run evolution of SIE. Additionally, we document that the corresponding responses of Sea Ice Albedo and Thickness largely amplify the long-run impact of CO2 on SIE.  Finally, we conduct conditional forecasts analysis of remedies like reducing CO2 emissions or the implementation of Albedo-enhancing Geo-Engineering technologies.</p>

Macroeconomic uncertainty and forecasting macroeconomic aggregates

Can information on macroeconomic uncertainty improve the forecast accuracy for key macroeconomic time series for the US? Since previous studies have demonstrated that the link between the real economy and uncertainty is subject to nonlinearities, I assess the predictive power of macroeconomic uncertainty in both linear and nonlinear Bayesian VARs. For the latter, I use a threshold VAR that allows for regime-dependent dynamics conditional on the level of the uncertainty measure. I find that the predictive power of macroeconomic uncertainty in the linear VAR is negligible. In contrast, using information on macroeconomic uncertainty in a threshold VAR can significantly improve the accuracy of short-term point and density forecasts, especially in the presence of high uncertainty.