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Simulating distributional impacts of macro-dynamics : theory and practical applications
\"Simulating Distributional Impacts of Macro-dynamics: Theory and Practical Applications is a comprehensive guide for analyzing and understanding the effects of macroeconomic shocks on income and consumption distribution, as well as for using the ADePT Simulation Module. Since real-time micro data is rarely available, the Simulation Module (part of the ADePT economic analysis software) takes advantage of historical household surveys to estimate how current or proposed macro changes might impact household and individual welfare\"--Back cover.
Book
Learning macroeconomic principles using MAPLE
Economics has been dubbed the \"dismal science\" since Thomas Carlyle coined the phrase in 1849. The 2008 presidential candidate who said, \"Economics is something that I've really never understood,\" probably sides with this view. So, why is economics so dismal to so many? Is it because it has become too mathematical? Is it because traditional textbooks fail to connect topics and models in a concise, cohesive, and meaningful way? Is it because the computer simulations that are used to teach economic principles \"stifle students' imagination, contribute to a dependent learning style, and fail to stimulate interest in the subject matter\" (Wetzstein 1988)? Or, is it because economists from different schools of economic thought rarely agree on anything? This book uses MAPLE and the simulation models that I developed in Learning Basic Macroeconomics (2014) to make teaching or learning economics not so dismal. MAPLE is ideally suited for this because it allows users to assemble and systematically combine the various models that form the aggregate market model, frees users from doing tedious calculations and algebraic manipulations, and is as easy to use as Microsoft Word. Building and analyzing the macroeconomic model using MAPLE is a fun way to learn the dismal science.
eBook
Computational methods for the study of dynamic economies
Economists are increasingly using computer simulations to understand the implications of their theoretical models and to make policy recommendations. New model solution techniques are required to deal with the increasingly important role of dynamics and uncertainty in macroeconomics. This book consists of articles by leading contributors in the fie.
eBook
Simulating distributional impacts of macro-dynamics
The automated DEC poverty tables (ADePT) simulation module, one of several modules in the ADePT platform, offers a useful methodological framework for analysts interested in measuring how macroeconomic projections may affect households. The modules approach falls between simple extrapolation and the most sophisticated methods such as top-down or top-down-up models based on linking household data with computable general equilibrium (CGE) models. By using simple macroeconomic projections as the macro-linkages to a micro-behavioral model built from household data, the model captures the complexities that influence how macro impacts are transmitted to households. The ADePT simulation module is an improvement over existing approaches because with minimal data and computational requirements it can evaluate in advance the distributional impacts of macroeconomic projections. By focusing on adjustments in employment and earnings, non-labor income, and price changes, it accounts for multiple transmission mechanisms and captures micro-level impacts across the entire income distribution. Using existing macroeconomic data and household surveys, the ADePT simulation module helps in identifying and profiling those groups of individuals - defined by characteristics such as occupational sector, location, and education level who are most likely to suffer income losses as a consequence of the change. This manual is organized in two parts. Part one covers the motivation, overview, and illustrations of the method. Part two describes each step the user must follow to create or obtain proper macro- and microeconomic inputs required for the simulation. It also explains how to enter these inputs into the module and the different options available for tailoring simulations.
eBook
Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach
This paper presents a method for creating machine learning models, specifically a gradient boosting model and a random forest model, to forecast real GDP growth. This study focuses on the real GDP growth of Japan and produces forecasts for the years from 2001 to 2018. The forecasts by the International Monetary Fund and Bank of Japan are used as benchmarks. To improve out-of-sample prediction, the cross-validation process, which is designed to choose the optimal hyperparameters, is used. The accuracy of the forecast is measured by mean absolute percentage error and root squared mean error. The results of this paper show that for the 2001–2018 period, the forecasts by the gradient boosting model and random forest model are more accurate than the benchmark forecasts. Between the gradient boosting and random forest models, the gradient boosting model turns out to be more accurate. This study encourages increasing the use of machine learning models in macroeconomic forecasting.
Journal Article
Quantum Computing and Deep Learning Methods for GDP Growth Forecasting
Precise macroeconomic forecasting is one of the major aims of economic analysis because it facilitates a timely assessment of future economic conditions and can be used for monetary, fiscal, and economic policy purposes. Numerous works have studied the behavior of the macroeconomic situation and have developed models to forecast them. However, the existing models have limitations, and the literature demands more research on the subject given that the accuracy of the models is still poor, and they have only been expanded for developed countries. This paper presents a comparison of methodologies for GDP growth forecasting and, consequently, new forecasting models of GDP growth have been constructed with the ability to estimate accurately future scenarios globally. A sample of 70 countries was used, which has allowed the use of sample combinations that consider the regional heterogeneity of the warning indicators. To the sample under study, different methods have been applied to achieve a high accuracy model, comparing Quantum Computing with Deep Learning procedures, being Deep Neural Decision Trees, which has provided excellent prediction results thanks to large-scale processing with mini-batch-based learning and can be connected to any larger Neural Networks model. Our model has a great potential impact on the adequacy of macroeconomic policy, providing tools that help to achieve macroeconomic and monetary stability at the global level, and creating new methodological opportunities for GDP growth forecasting.
Journal Article
Macroeconomic impact of stranded fossil fuel assets
Several major economies rely heavily on fossil fuel production and exports, yet current low-carbon technology diffusion, energy efficiency and climate policy may be substantially reducing global demand for fossil fuels1–4. This trend is inconsistent with observed investment in new fossil fuel ventures1,2, which could become stranded as a result. Here, we use an integrated global economy–environment simulation model to study the macroeconomic impact of stranded fossil fuel assets (SFFA). Our analysis suggests that part of the SFFA would occur as a result of an already ongoing technological trajectory, irrespective of whether or not new climate policies are adopted; the loss would be amplified if new climate policies to reach the 2 °C target of the Paris Agreement are adopted and/or if low-cost producers (some OPEC countries) maintain their level of production (‘sell out’) despite declining demand; the magnitude of the loss from SFFA may amount to a discounted global wealth loss of US$1–4 trillion; and there are clear distributional impacts, with winners (for example, net importers such as China or the EU) and losers (for example, Russia, the United States or Canada, which could see their fossil fuel industries nearly shut down), although the two effects would largely offset each other at the level of aggregate global GDP.
Journal Article
A Simple Parametric Model Selection Test
We propose a simple model selection test for choosing among two parametric likelihoods, which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution. That is, both, one or neither is allowed to be correctly specified or misspecified, they may be nested, nonnested, strictly nonnested, or overlapping. Unlike in previous testing approaches, no pretesting is needed, since in each case, the same test statistic together with a standard normal critical value can be used. The new procedure controls asymptotic size uniformly over a large class of data-generating processes. We demonstrate its finite sample properties in a Monte Carlo experiment and its practical relevance in an empirical application comparing Keynesian versus new classical macroeconomic models. Supplementary materials for this article are available online.
Journal Article
A similarity-based approach for macroeconomic forecasting
In the aftermath of the recent financial crisis there has been considerable focus on methods for predicting macroeconomic variables when their behaviour is subject to abrupt changes, associated for example with crisis periods. We propose similarity-based approaches as a way to handle parameter instability and apply them to macroeconomic forecasting. The rationale is that clusters of past data that match the current economic conditions can be more informative for forecasting than the entire past behaviour of the variable of interest. We apply our methods to predict both simulated data in a set of Monte Carlo experiments, and a broad set of key US macroeconomic indicators. The forecast evaluation exercises indicate that similarity-based approaches perform well, in general, in comparison with other common time-varying forecasting methods, and particularly well during crisis episodes.
Journal Article
Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network
The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory of Internet financial risks is introduced and a theoretical framework for analyzing and forecasting internet financial risks is established. Second, the theory of the BPNN and the algorithms based on deep learning are introduced. Then, the model based on the BPNN and deep learning is implemented to improve the early warning of Internet financial risks, analyze the data image of China's Gross Domestic Product (GDP), currency (M2), non-performing loan records, and the Shanghai Composite Index from 2006 to 2020, and forecast the risks in 2021. Through the model based on deep learning and BPNN, it can be found that the trends of the growth rate of China's GDP take on the shapes of V and L, and the trend of M2 is opposite to that of GDP. In the whole year, there is a low at the beginning and the end of the year, and the monthly non-performing loans and the Shanghai Composite Index decrease. The forecast made by the model is that there will be many fluctuations in 2021. At present, China’s economy just enters the era of the new normal, which helps to build a more scientific and sensitive early warning system for financial risks. The model based on the BPNN and deep learning greatly improves the timeliness of forecasts and has a positive impact on the stability of China’s financial environment.
Journal Article