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"Economic forecasting Developed countries."
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Fluctuations in Uncertainty
Uncertainty is an amorphous concept. It reflects uncertainty in the minds of consumers, managers, and policymakers about possible futures. It is also a broad concept, including uncertainty over the path of macro phenomena like GDP growth, micro phenomena like the growth rate of firms, and noneconomic events like war and climate change. In this essay, I address four questions about uncertainty. First, what are some facts and patterns about economic uncertainty? Both macro and micro uncertainty appear to rise sharply in recessions and fall in booms. Uncertainty also varies heavily across countries—developing countries appear to have about one-third more macro uncertainty than developed countries. Second, why does uncertainty vary during business cycles? Third, do fluctuations in uncertainty affect behavior? Fourth, has higher uncertainty worsened the Great Recession and slowed the recovery? Much of this discussion is based on research on uncertainty from the last five years, reflecting the recent growth of the literature.
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
New Cohort Fertility Forecasts for the Developed World: Rises, Falls, and Reversals
With period fertility having risen in many low-fertility countries, an important emerging question is whether cohort fertility trends are also reversing. We produce new estimates of cohort fertility for 37 developed countries using a new, simple method that avoids the underestimation typical of previous approaches. Consistent with the idea that timing changes were largely responsible for the last decades' low period fertility, we find that family size has remained considerably higher than the period rates of 1.5 in many ''low-fertility\" countries, averaging about 1.8 children. Our forecasts suggest that the long-term decline in cohort fertility is flattening or reversing in many world regions previously characterized by low fertility. We document the marked increase of cohort fertility in the Englishspeaking world and in Scandinavia; signs of an upward reversal in many low-fertility countries, including Japan and Germany; and continued declines in countries such as Taiwan and Portugal. We include in our forecasts estimates of statistical uncertainty and the possible effects of the recent economic recession.
Journal Article
The next hundred years of growth and convergence
World gross domestic product per capita is forecast to grow at 2.6% annually over the next 100 years. Convergence of less-developed countries toward output levels of the world frontier accounts for much of the forecast. Projecting recent growth in China and India accounts for much of the forecast convergence. The forecast differs from the earlier literature because the facts of convergence have changed in recent decades. A Markov-switching model is estimated for each country, allowing each country to switch on or off a path of convergence to the world output frontier. Bayesian estimates of the historical process and posterior forecasts are offered.
Journal Article
Bayesian Probabilistic Projections of Life Expectancy for All Countries
We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we conducted an out-of-sample cross-validation experiment, fitting the model to the data from 1950-1995 and using the estimated model to forecast for the subsequent 10 years. The 10-year predictions had a mean absolute error of about 1 year, about 40 % less than the current UN methodology. The probabilistic forecasts were calibrated in the sense that, for example, the 80 % prediction intervals contained the truth about 80 % of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free, publicly available R software packages called bayesLife and bayesDem are available to implement the method.
Journal Article
Financial crisis, labor market frictions, and economic volatility
This article analyzes cross-country data encompassing 130 countries and regions from 2000 to 2019 to investigate the correlation between financial crises, labor market frictions, and economic volatility. The empirical findings demonstrate that financial crises have a milder impact on real gross domestic product (GDP) in developing countries with flexible labor markets. This trend also applies to non–eurozone developed countries, where labor market flexibility aids crisis mitigation. However, this pattern doesn’t hold for eurozone countries. Further examination of developing nations reveals that those with heightened labor market flexibility tend to experience reduced adverse effects on non-tradable sectors, thereby mitigating the impact on real GDP.
Journal Article
Forecasting stock prices using long short-term memory involving attention approach: An application of stock exchange industry
The Stability of the economy is always a great challenge across the world, especially in under developed countries. Many researchers have contributed to forecasting the Stock Market and controlling the situation to ensure economic stability over the past several decades. For this purpose, many researchers have built various models and gained benefits. This journey continues to date and will persist for the betterment of the stock market. This study is also a part of this journey, where four learning-based models are tailored for stock price prediction. Daily business data from the Karachi Stock Exchange (100 Index), covering from February 22, 2008 to February 23, 2021, is used for training and testing these models. This paper presenting four deep learning models with different architectures, namely the Artificial Neural Network model, the Recurrent Neural Network with Attention model, the Long Short-Term Memory Network with Attention model, and the Gated Recurrent Unit with Attention model. The Long Short-Term Memory with attention model was found to be the top-performing technique for accurately predicting stock exchange prices. During the Training, Validation and Testing Sessions, we observed the R-Squared values of the proposed model to be 0.9996, 0.9980 and 0.9921, respectively, making it the best-performing model among those mentioned above.
Journal Article
Aggregate Investment and Investor Sentiment
Using bottom-up information from corporate financial statements, we examine the relation between aggregate investment, future equity returns, and investor sentiment. Consistent with the business cycle literature, corporate investments peak during periods of positive sentiment, yet these periods are followed by lower equity returns. This pattern exists in most developed countries and survives controls for discount rates, equity flows, valuation multiples, operating accruals, and other investor sentiment measures. Higher aggregate investments also precede greater earnings disappointments, lower short-window earnings announcement returns, and lower macroeconomic growth. We conclude aggregate corporate investment is an alternative, and possibly sharper, measure of market-wide investor sentiment.
Journal Article
Trends in Mortality Decrease and Economic Growth
The vast literature on extrapolative stochastic mortality models focuses mainly on the extrapolation of past mortality trends and summarizes the trends by one or more latent factors. However, the interpretation of these trends is typically not very clear. On the other hand, explanation methods are trying to link mortality dynamics with observable factors. This serves as an intermediate step between the two methods. We perform a comprehensive analysis on the relationship between the latent trend in mortality dynamics and the trend in economic growth represented by gross domestic product (GDP). Subsequently, the Lee-Carter framework is extended through the introduction of GDP as an additional factor next to the latent factor, which provides a better fit and better interpretable forecasts.
Journal Article