Quarterly Gross Domestic Product Research Articles

Assessment of post-pandemic economic recovery in Beijing, China, using night-time light data

ABSTRACT The coronavirus disease 2019 (COVID-19) pandemic posed a significant impact on human society, resulting in substantial changes in night-time light (NTL). This study developed a novel NTL index to estimate the quarterly Gross Domestic Product (GDP) at the county level in Beijing. This study aimed to investigate the economic recovery status in Beijing by establishing regression models of economic development before and after the pandemic. The estimated quarterly GDP at the county level was found to have a strong correlation with actual quarterly GDP statistics, with an average coefficient of determination (R 2) of 0.790. The regression models of the economic development of Beijing county-level districts before and after the pandemic were established after seasonal decomposition of the quarterly GDP. Based on the economic recovery framework for the pandemic in Beijing, most districts in the city had exhibited low economic resilience in terms of resistance and recoverability before and after the pandemic. Although the economy of Beijing is recovering gradually, it has not yet restored to pre-pandemic normal levels. These findings imply that NTL data can help assess the impact of emergency events on the disturbance of economic activity.

The Impact of Foreign Direct Investment Management on Economic Growth Using Multiple Linear Regression (MLR)

Global economies are facing geopolitical and ongoing economic impacts from the COVID-19 pandemic, which brings back the topic of growth determinants. This study focuses on the United States, the world's largest economy and a significant recipient of foreign direct investment (FDI). For this reason, we analysed whether there is a positive correlation between the volume of FDI inflows and the growth of gross domestic product (GDP). A multiple linear regression (MLR) model was used to examine the effects of various exogenous control variables on the quarterly GDP growth in the USA from 1999 to 2022. During these 96 quarters, FDI was found to have a positive effect on GDP growth at statistical significance of 5% (p < 0.05). Specifically, a one-million increase in FDI ceteris paribus caused an estimated GDP growth of approximately 743.43 dollars. The results ultimately propose positive implications for less developed countries that could utilize emphasising their FDI management policies to achieve higher economic growth.

Foreign Exchange Management and Selected Macroeconomic Performance Nexus Evidence From Nigeria

The objective of economic stability is fundamental to relative wellbeing of any country. Therefore, exchange rate management is key to fueling growth potentials and relative macroeconomic stability. The study empirically examined the exchange rate management and selected macroeconomic nexus in Nigeria from 1981Q1 to 2021Q4. The technique of generalized auto regressive conditional heteroscedasticity GARCH (1,1) was employed to model the dependent variable of quarterly nominal effective exchange rate( QNEER) on the explanatory variables : quarterly external reserves(QEXRES);quarterly gross domestic product (QGDP) ,and Dummy of effective exchange rate policy (DUMEXPO). The research output showed exchange rate depreciation in Nigeria exhibited a statistically significant inverse relationship with external reserves and gross domestic product during the review period. Whereas, test of exchange rate management policy effectiveness showed a relatively weak policy thrust are implemented to stabilized exchange disequilibrium. The study however, recommended for a consistently well recalibration of exchange policy as well as monetary policy intervention strategies so as to restore convergence, attract capital inflow and macroeconomic equilibrium position. Keywords: Exchange rate management, Sterilization, Pass-through, Nominal exchange rate.

Reconstructing the Quarterly Series of the Chilean Gross Domestic Product Using a State Space Approach

In this work, we use a cointegration state space approach to estimate the quarterly series of the Chilean Gross Domestic Product (GDP) in the period 1965–2009. First, the equation of Engle–Granger is estimated using the data of the yearly GPD and some related variables, such as the price of copper, the exports of goods and services, and the mining production index. The estimated coefficients of this regression are then used to obtain a first estimation of the quarterly GDP series with measurement errors. A state space model is then applied to improve the preliminary estimation of the GDP with the main purpose of eliminating the maximum error of measurement such that the sum of the three-month values coincide swith the yearly GDP. The results are then compared with the traditional disaggregation methods. The resulting quarterly GDP series reflects the major events of the historical Chilean economy.

Scientific progress in information theory quantifiers

The Bandt and Pompe method (BPM) has been successfully applied to estimate the information theory quantifiers. The most significant limitation of the BPM is that this approach is based on point estimation. This research provides scientific progress into the several applications of the information theory quantifiers. In this way, we propose a new statistic confidence interval estimation method for the Permutation Entropy, Fisher Information Measure, and Macroeconophysics Indicator of Efficiency (MIEE). Therefore, we examine quarterly Gross Domestic Product (GDP) time series for 25 countries members of the Organization for Economic Co-operation and Development (OECD). The periods cover more than 59 years, from Q1-1960 to Q3-2019, with 238 data points. For each GDP time series, we employ the non-parametric method Local Block Bootstrap to provide a stylized fact about an empirical distribution for these data taking into account the information theory quantifiers. Based on the values of the empirical distribution of the information theory quantifiers, we construct the Shannon-Fisher causality plane (SFCP), which allows us to quantify the disorder and evaluate randomness present in the time series of quarterly GDP in each country. In addition, we use information theory quantifiers to rank the most efficient countries in allocating the resources arising from economic growth to generate a virtuous cycle of growth. Also, we apply the Principal Component Analysis (PCA) to check the results of the empirical distribution of the MIEE. We find the PCA promotes the same result as the MIEE. Our results suggest an alternative form of clustering using information theory quantifiers linked to the similarity of countries’ GDPs and the mutual influences of one country’s economic growth on the others.

Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Night-time light (NTL) data have been widely used as a remote proxy for the economic performance of regions. The use of these data is more advantageous than the traditional census approach is due to its timeliness, low cost, and comparability between regions and countries. Several recent studies have explored monthly NTL composites produced by the Visible Infrared Imaging Radiometer Suite (VIIRS) and revealed a dimming of the light in some countries during the national lockdowns due to the COVID-19 pandemic. Here, we explicitly tested the extent to which the observed decrease in the amount of NTL is associated with the economic recession at the subnational level. Specifically, we explore how the association between Gross Domestic Product (GDP) and the amount of NTL is modulated by the pandemic and whether NTL data can still serve as a sufficiently reliable proxy for the economic performance of regions even during stressful pandemic periods. For this reason, we use the states of the US and quarterly periods within 2014–2021 as a case study. We start with building a linear mixed effects model linking the state-level quarterly GDPs with the corresponding pre-processed NTL data, additionally controlling only for a long-term trends and seasonal fluctuations. We intentionally do not include other socio-economic predictors, such as population density and structure, in the model, aiming to observe the ‘pure’ explanatory potential of NTL. As it is built only for the pre-COVID-19 period, this model demonstrates a rather good performance, with R2 = 0.60, while its extension across the whole period (2014–2021) leads to a considerable worsening of this (R2 = 0.42), suggesting that not accounting for the COVID-19 phenomenon substantially weakens the ‘natural’ GDP–NTL association. At the same time, the model’s enrichment with COVID-19 dummies restores the model fit to R2 = 0.62. As a plausible application, we estimated the state-level economic losses by comparing actual GDPs in the pandemic period with the corresponding predictions generated by the pre-COVID-19 model. The states’ vulnerability to the crisis varied from ~8 to ~18% (measured as a fraction of the pre-pandemic GDP level in the 4th quarter of 2019), with the largest losses being observed in states with a relatively low pre-pandemic GDP per capita, a low number of remote jobs, and a higher minority ratio.

Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic’s impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered; at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government’s strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term.

Using a Fast Elitist Non-Dominated Genetic Algorithm on Multi-Objective Programming for Quarterly Disaggregation of the Gross Domestic Product

Abstract This research paper we use a fast elitist multiobjective genetic algorithm to solve the new approach that we propose to quarterly disaggregating of the Gross Domestic Product (GDP) by multiobjective programming. Thus, the quarterly disaggregation of the GDP is described as a quadratic multiobjective programming problem that generalizes Denton’s proportional method. The proposed approach has the advantage reduce to one the number of optimization programs to be solved. Our proposed method can be applied to the national accounts of any country that has adopted the National Accounting System. The simulation results are compared to those obtained using Denton’s proportional method and these results revealed the overall performance of the multiobjective programming approach for the quarterly disaggregation of GDP. Our approach is more suitable for taking into account the links between branches of national accounts, in terms of volumes and prices of products demanded during the production process. Also, it reduces forecast error and volatility of quarterly GDP. Besides, it is worth noting that our method is a usfull step for data processing such as chain-linked measures, overlap growth techniques, seasonal adjustment and calendar effects adjustment, in time series and econometrics analysis.

Changes of base-year and Indian GDP growth: an agnostic look

Purpose World over, change of base year in the gross domestic product (GDP) is a standard practice of GDP estimation. However, unless a consistent series of GDP is released with respect to the new base for the earlier period, the existence of multiple growth rates creates problems for applied researchers, policymakers and the general public alike. Faced with such a menu of GDP series researchers often try to interpolate a consistent series of GDP. The main purpose of this paper is to analyses the nature of the data generating process of such multiple interpolated series of quarterly growth rates and tries to discern the consistency of such processes. Design/methodology/approach The present paper tries to look into the statistical implications and complications of such interpolated quarterly GDP/growth series in India in terms of three series of GDP, namely, with 1999–2000, 2004–2005 and 2011–2012 as its bases. Findings The analysis reveals that as a result of a change of base year, the nature of the data generating process of the old and new GDP series could undergo changes and experience different breakpoints. While all these conclusions seem to be valid for GDP growth at quarterly intervals, taking the data at annual frequency is less problematic. Practical implications The observation suggests that in most applied work, researchers may not have the luxury of only working with annual data and certain consistency checks will be necessary to check the veracity of the results based on quarterly data with those based on annual data. Second, moving forward it may be useful for the Authorities to make a transition to a chain-based linking method rather than fixed time-period-based bases as is currently done. Originality/value The analysis of Indian GDP in this paper is, perhaps, indicative of the fact that usage of quarterly GDP data is to be handled with caution and it is preferable that any serious empirical analysis uses annual GDP data whenever it is available/feasible. The comparison of GDP growth rates at different frequencies and examining the true nature of the process are quite unique in their contribution towards empirical macroeconomic research.

An unconventional weekly economic activity index for Germany

We introduce an unconventional activity index for the German economy at weekly frequency in order to monitor economic developments in real time. The index contains nine high-frequency, rather unconventional weekly indicators. These are augmented by monthly industrial production and quarterly gross domestic product (GDP). The weekly activity index is then calculated as the common factor of the mixed-frequency dataset. We show that the index (i) exhibits a high correlation with quarterly GDP growth, (ii) is able to serve as a reliable weekly coincident indicator for economic activity, and (iii) can be used to generate timely nowcasts for GDP growth in Germany.

Evaluation of Mid-Term Load Forecasting Case Study Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs)

This paper presents the different techniques for Thailand's medium-term load forecasting. It expected that load forecasting can effectively improve the electrical load efficiency of the Electricity Generating Authority of Thailand (EGAT). In addition, the accuracy of load forecasting is an important part of decision-making for power plant investment and the planning of the power distribution system. In this study, the input data has been trained by several predictive models, which have been artificial neural networks (2 ,3 and 4 hidden layers) and adaptive neuro- fuzzy inference systems. Learning and prediction depends on three important factors, including Thailand's peak load history (simple moving average of 12 months, 9 months, 6 months and 3 months), month codes and Quarterly Gross Domestic Product (QGDP). The results show that training ANN with two hidden layers produces the best predictive performance. The most accurate load forecast using this method is 1.1527% of MAPE and 14.45 minutes of learning time.

Modelling and forecasting GDP using factor model: An empirical study from Bosnia and Herzegovina

Abstract In the most developed countries the first estimations of Gross Domestic Product (GDP) are available 30 days after the end of the reference quarter. In this paper, possibilities of creating an econometric model for making short-term forecasts of GDP in B&H have been explored. The database consists of more than 100 daily, monthly and quarterly time series for the period 2006q1-2016q4. The aim of this study was to estimate and validate different factor models. Due to the length limit of the series, the factor analysis included 12 time series which had a correlation coefficient with a quarterly GDP at the absolute value greater than 0.8. The principal component analysis (PCA) and the orthogonal varimax rotation of the initial solution were applied. Three principal components are extracted from the set of the series, thus together accounting for 73.34% of the total variability of the given set of series. The final choice of the model for forecasting quarterly B&H GDP was selected based on a comparative analysis of the predictive efficiency of the analysed models for the in-sample period and for the out-of-sample period. The unbiasedness and efficiency of individual forecasts were tested using the Mincer-Zarnowitz regression, while a comparison of the accuracy of forecast of two models was tested by the Diebold-Mariano test. We have examined the justification of a combination of two forecasts using the Granger-Ramanathan regression. A factor model involving three factors has shown to be the most efficient factor model for forecasting quarterly B&H GDP.

A Proposed New Model for Denton Proportional Method Generalization in Quarterly Disaggregation of the Gross Domestic Product

The estimation of a quarterly Gross Domestic Product (GDP) using an indirect approach based on quarterly indicators is often done by quarterly disaggregation of the national accounts branch per branch. The proportional method proposed by Denton (1971) leads to solving several optimization programs and doesn’t take into account the links between branches when searching for a quarterly GDP through indirect approach. This paper proposes a brand new approach to quarterly disaggregation of a GDP broken down into several branches or sectors, that takes into account the links between this branches. This approach generalizes the Denton (1971) proportional method and is an integrated approach in which the quarterly disaggregation of GDP is formulated as a multiobjective optimization problem. In fact, we group together on the one hand, all the elementary objective functions identified with respect to the various branches, and on the other hand, all the prescribed constraints according to the branches. Our technique relies on the minimization (in the least squares sense) of the difference in adjustment from neighbouring quarters under constraints provided by the annual benchmark data. The proposed model can be applied to the national accounts of any country using any approach for GDP compilation and it also takes also into account the usual inter dependence of its branches lacking in Denton method. Besides, it is worth noting that an algorithmic method needs to be devised due to the large scale of concrete GDP disaggregation problems.

GDP revisions and nowcasting in Serbia

This paper addresses the issues of Quarterly National Accounts compilation and Gross Domestic Product (GDP) revisions as well as GDP shortterm forecasting based on available monthly series of economic and financial indicators. If GDP is promptly and properly measured, policy makers and the general public can closely monitor implementation of the fiscal consolidation program. Reputation of the program depends on achievements that should be beyond any doubt. Since figures on quarterly GDP and its components are provisional until autumn of next year, and subject for revision over the next two years - which is a standard ESA 2010 methodology - accuracy of data might interfere with prompt availability. Additionally, nowcasting can provide timely estimates of current GDP. Figures on this quarter GDP are available two months after the end of the quarter. Flash estimates of GDP are available one month after the end of the quarter. The nowcasting technique can substantially shorten this gap. However, the challenging issue is related to a choice of the monthly series that should be included in Mixed Data Sampling econometrics. We address both of these issues in this paper.

A century of macro-financial linkages

Purpose The recent international financial crisis and the subsequent Great Recession has underlined the need to gain a better understanding of the linkages between financial factors and the real economy. The purpose of this paper is to explore the contributions of financial shocks to macroeconomic fluctuations in Denmark, USA and Canada over the past century. Design/methodology/approach The paper compiles a new data set with break-adjusted quarterly time series of real gross domestic product (GDP) and six other key macroeconomic indicators for the three countries since 1921. It then explores the time-variation of macro-financial linkages by estimating structural vector-autoregressive models separately for three sample periods: 1921-1949, 1950-1979 and 1980-2014. Findings For all three countries, there seems to have been a non-trivial contribution from financial shocks to volatility in output and unemployment in all sample periods, even in the period 1950-1979, which was characterised by tight regulation of the financial sector and widespread financial stability. These findings underscore the general importance of financial factors to macroeconomic fluctuations. Originality/value The paper is the first to offer regression-based estimates of quarterly real GDP for Denmark for 1921-1947. As a result, quarterly figures for real GDP are now available for Denmark for the entire period 1921-2014. Such long-span time series of quarterly real GDP do not exist for any other European countries.

Estimating Quarterly Indicators of Economic Activity for the States of the Eastern Caribbean Currency Union

Indicators of economic activity provide key inputs into policymakers’, business persons’, investors’ and consumers’ decision making processes. However, the Eastern Caribbean Central Bank (ECCB) currently only publishes annual gross domestic product (GDP) series for each of the eight member states on which it reports, with quarterly reports providing qualitative guidance as to the general direction of economic activity. This study applies the technique of Chow and Lin (1971) to related, high-frequency macroeconomic variables in an attempt to produce quarterly estimates of economic activity for each member of the Eastern Caribbean Currency Union (ECCU) over the period 1993 to 2014. The results indicated that changes in our quarterly GDP estimates generally track the ECCB’s approximations regarding the direction of economic activity for most economies and at least match estimates from a univariate model for seven of the eight countries.

A method for constructing the Composite Indicator of business cycles based on information granulation and Dynamic Time Warping

Composite indicators of business cycles play a paramount role in the analysis of macroeconomy, which provide decision makers with much meaningful information. This paper develops a novel constructing method of the business cycle composite indicator based on information granulation and Dynamic Time Warping (DTW), which not only takes the most important indicator real Gross Domestic Product (GDP) into account but avoids some impractical assumptions in dynamic factor models. First, the quarterly real GDP sequence is divided into information granules by the principle of justifiable granularity. Next, monthly coincident indicators are split into corresponding segments relying on the information granules of real GDP, and DTW is applied to measure the similarity between monthly and quarterly segments. The weights are derived by normalizing reciprocals of the above distance values. Finally, the monthly composite indicator of business cycles is obtained by taking a weighted cross-section average of those monthly coincident indicators. The numerical experiment reveals that the composite indicator established by the proposed method can reflect the dynamics of business cycles and accurately catch the turning points in business cycles.

Predicting Recessions with Factor Linear Dynamic Harmonic Regressions

ABSTRACTWe propose a new framework for building composite leading indicators for the Spanish economy using monthly targeted predictors and small‐scale dynamic factor models. Our leading indicator index, based on the low‐frequency components of four monthly economic variables, is able to predict the onset of the Spanish recessions as well as the gross domestic product (GDP) growth cycles and classical industrial production cycles, both historically and in real time. Also, our leading indicator provides substantial aid in forecasting annual and quarterly GDP growth rates. Using only real data available at the beginning of each forecast period, our indicator one‐step‐ahead forecasts shows substantial improvements over other alternatives. Copyright © 2013 John Wiley & Sons, Ltd.

Quarterly GDP Revisions in G-20 Countries: Evidence from the 2008 Financial Crisis

This paper presents a statistical analysis of revisions in quarterly gross domestic product (GDP) of the Group of Twenty countries (G-20) since 2000. The main objective is to assess whether the reliability of early estimates of quarterly GDP has been weakened from the turmoil of the 2008 financial crisis. The results indicate that larger and more downward revisions were observed during the years 2008 and 2009 than in previous years.

Quarterly GDP Revisions in G-20 Countries: Evidence from the 2008 Financial Crisis

This paper presents a statistical analysis of revisions in quarterly gross domestic product (GDP) of the Group of Twenty countries (G-20) since 2000. The main objective is to assess whether the reliability of early estimates of quarterly GDP has been weakened from the turmoil of the 2008 financial crisis. The results indicate that larger and more downward revisions were observed during the years 2008 and 2009 than in previous years.