JournalTOCs

Journal of Quantitative Economics
Number of Followers: 0

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0971-1554 - ISSN (Online) 2364-1045
Published by Springer-Verlag

[2467 journals]

Typical States and Their Risks for Mortgage Loans
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  Abstract: Abstract In this study, we construct a compartmental model that tracks the different states and their respective hazards for typical mortgage loans. We consider that an active mortgage loan could become delinquent in light of either common systemic risks or idiosyncratic risks in the job market. These two groups of employment-related perils jeopardize the sources of income underlying the mortgage monthly payments to lenders and could hurt the ability of mortgage loan borrowers to retire their debt. We also contemplate ongoing risks of a collapse in the housing market, which might transform the mortgage loan to be “underwater” and consequently diminish borrowers’ incentives to service the outstanding balance. We develop the necessary derivations, illustrate the functionality of the model over several hypothetical simulations and sensitivity analyses, suggest variable estimation specific guidelines, conclude, and discuss potential extensions for the proposed model.
  PubDate: 2023-02-28
Hierarchical Bayes Measurement Error Small Area Model for Estimation of
Disaggregated Level Workers Mobility Pattern in India
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  Abstract: Abstract Periodic Labour Force Survey (PLFS) is the major source of data on various labour force indicators in India at annual or quarterly basis which is on the field since 2017–18. It has strategically reformed the previous quinquennial Employment and Unemployment Survey of National Statistical Office, India. Mobility pattern of workers, basically in terms of commuting is one of the key information contained therein which essentially entails the workplace characteristics of the workforce. In this article PLFS 2017–18 and 2018–19 data is analysed which depicts state-wise large disparities in the commuting behaviour of workers, whereas most of the workers are out-commuting from rural areas. The potential reason behind is the rapid pace of urbanization and associated improved transportation facilities as well as search for stable non-farm employment opportunities by the rural workforce. Further, the planning of urbanization or creation of employment opportunities at rural places in each state requires within-state regional or disaggregated level information of workplaces, spatial concentration of works and workers. To pursue that, disaggregated level analysis of commuting pattern of workers is done using small area estimation approach. In particular, this article describes hierarchical Bayes (HB) measurement error (ME) small area model for binary variable of interest indicating whether individual in the workforce is commuting or not. The HBME model has been implemented to obtain district level rural commuters proportions in Uttar Pradesh state of India. This state specifically tops amongst the states in the number of rural commuters. A spatial map has been generated for visual inspection of disparity in commuting behaviour of workers, also such map is useful to the policy makers and administration for framing decentralized level plans or strategies eyeing stable mobility behaviour to persuade improvement in employment rate.
  PubDate: 2023-02-10
Switching Towards LPG: Indian Household Perspectives
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  Abstract: Abstract The study examines the patterns of domestic energy consumption and fuel preference for cooking by households in rural and urban India over the past two and a half decades to track the progress towards the use of Liquefied Petroleum Gas (LPG) and clean and modern cooking fuel. The fuel mix of households has been changing with an increase in income and infrastructure over time. The relatively affluent households tend to use cleaner fuels in larger proportions in their cooking fuel mix. The change is more visible in urban areas. In rural areas, the use of firewood, considered a harmful traditional fuel, for cooking purposes is still prevalent. This is due to the easy, cheap/and free availability of firewood in these areas. Logistic regression has been applied to determine the probability of LPG being the main source of cooking by households and the factors which, over time, influence the choice. The results show that the preference for LPG increases with an increase in the education level of members of a household, along with affordability.
  PubDate: 2023-02-10
Correction: Abductive Inference and C. S. Peirce: 150 Years
Later
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  PubDate: 2023-02-02
Oil Demand and Supply Shocks in Canada’s Economy
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  Abstract: Abstract This paper investigates how oil supply shocks, aggregate demand shocks, and speculative oil demand shocks affect Canada’s economy, within an estimated Dynamic Stochastic General Equilibrium (DSGE) model. The estimation is conducted using Bayesian methods, with Canadian quarterly data from 1983Q1 to 2021Q4. The results suggest that the dynamic effects of oil price shocks on Canadian macroeconomic variables vary according to their sources. In particular, a 10 percent increase in the real price of oil driven by positive foreign aggregate demand shocks has a positive effect of about 1.2 percent on Canada’s real GDP upon impact and the effect remains positive over time. In contrast, an increase in the real price of oil driven by negative foreign oil supply shocks or by positive speculative oil demand shocks causes a small effect of about 0.15 percent on Canada’s real GDP upon impact but causes a slightly decline afterwards. At the same time, an oil price increase that originates from aggregate demand shock causes an increase in consumption and investment, while an oil price increase that originates from oil supply shocks or from speculative oil demand shocks cause a decline in consumption and investment. Furthermore, among the identified oil shocks, aggregate demand shocks have been by fare more important in explaining the variations of most of Canadian macroeconomic variables over the estimation period. In contrast, speculative oil demand shock appears to be the first source of variations in real oil price.
  PubDate: 2023-01-27
Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying
Coefficient Regression Model (FA-TVCRM)
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  Abstract: Abstract Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007–08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015–16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid–19 shock of 2020–21.
  PubDate: 2023-01-11
Can Income Inequality be Affected by the Interaction Between ICTs and
Human Capital': The Evidence from Developing Countries
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  Abstract: Abstract Income inequality in developing countries remains a major concern. It has been established that higher inequality makes a greater proportion of the population vulnerable to poverty. This paper aimed to analyse the effect of the interaction between ICTs and human capital on income inequality in developing countries. Covering 89 developing countries for the period 2000 to 2015 and based on panel fixed effects instrumental variables technique, this study finds that the interaction between ICTs and human capital reduces overall income inequality on the one hand, and on the other, leads to an increase in the income shares of the poorest, and in particular relative to the richest in developing countries. Furthermore, the interaction between ICTs and human capital reinforces the impact of ICTs on income inequality in developing countries. These results suggest that prioritizing the acquisition of human capital by the poorest, as well as promoting access to and use of ICTs for the benefit of the poorest would significantly contribute to reduce overall income inequality and increase income shares of the poorest in developing countries.
  PubDate: 2023-01-11
The Determinants of Firm’s Growth in the Telecommunication Services
Industry: Empirical Evidence from India
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  Abstract: Abstract The growth and evolution of the industry has an important bearing on the economic development of a country. The extant literature on firm growth provides valuable insights into firm behavior and factors influencing the evolution of the industry over time. The topic becomes even more relevant in the context of the telecommunication industry because of its positive impact on economic growth and productivity, which has been well documented in both the developed and developing country context. Based on the firm-growth literature, this study analyzes the factors influencing the growth of the Indian telecommunication industry using an unbalanced panel of 204 firms across two decades from 2000 to 2020. Dynamic Panel estimation technique (System GMM) is used to take care of endogeneity issues caused by the dynamic nature of firm growth models. Results indicate that the growth of firms in the Indian telecom services industry is explained by systematic factors like size, age, profitability, financial leverage, and trade orientation. The study finds that the larger firms grow at a decreasing rate compared to small firms. The firm's age negatively impacts the growth rate of firms, i.e., younger firms have a faster growth rate than the older ones supporting the case of convergence of firm growth in the Indian telecom services sector. Factors such as lagged R&D intensity, financial leverage, and profitability negatively impact the firms’ growth rate. Export intensity is found to have a negative and significant impact on the growth rate of the firms. The findings have important policy implications in the context of the growth of the telecommunication industry in India, which has witnessed intense competition, steep decline in profitability, and high debt structure over a period of time.
  PubDate: 2023-01-11
Abductive Inference and C. S. Peirce: 150 Years Later
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  Abstract: Abstract This paper is about two things: (i) Charles Sanders Peirce (1837–1914)—an iconoclastic philosopher and polymath who is among the greatest of American minds. (ii) Abductive inference—a term coined by C. S. Peirce, which he defined as “the process of forming explanatory hypotheses. It is the only logical operation which introduces any new idea.” 1. Abductive inference and quantitative economics. Abductive inference plays a fundamental role in empirical scientific research as a tool for discovery and data analysis. Heckman and Singer (2017) strongly advocated “Economists should abduct.” Arnold Zellner (2007) stressed that “much greater emphasis on reductive [abductive] inference in teaching econometrics, statistics, and economics would be desirable.” But currently, there are no established theory or practical tools that can allow an empirical analyst to abduct. This paper attempts to fill this gap by introducing new principles and concrete procedures to the Economics and Statistics community. I termed the proposed approach as Abductive Inference Machine (AIM). 2. The historical Peirce’s experiment. In 1872, Peirce conducted a series of experiments to determine the distribution of response times to an auditory stimulus, which is widely regarded as one of the most significant statistical investigations in the history of nineteenth-century American mathematical research (Stigler in Ann Stat 239–265, 1978). On the 150th anniversary of this historical experiment, we look back at the Peircean-style abductive inference through a modern statistical lens. Using Peirce’s data, it is shown how empirical analysts can abduct in a systematic and automated manner using AIM.
  PubDate: 2022-12-30
  DOI: 10.1007/s40953-022-00332-9
Examining the Time Varying Spillover Dynamics of Indian Financial
Indictors from Global and Local Economic Uncertainty
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  Abstract: Abstract The research aims to excavate the role of global (Fed Rate, Crude, Real Dollar Index) and endogenous economic variables (GDP and Consumer Price Index) in shaping the spillover amongst the major Indian Financial indicators, viz. Nifty Index, MCX Gold, USDINR, Govt. Bond 10Y maturity and agricultural index N-Krishi. To facilitate cross-comparison decomposition of time-varying spillover output generated from Time-Varying Vector Autoregression (TVP-VAR) with aggregation at three layers is performed. The research finds that Indian Financial Indicators are vulnerable to spillover shocks from global variables predominantly driven by Fed Rate and Real Dollar Index. USDINR turns out to be most sensitive to global shocks and transgresses the shock to other financial indicators. Importantly, persistently high inflation has brought volatility spikes in the directional spillover to financial indicators. Though spillover subsidence is observed post-2014, with an all-time high during GFC, a sudden spurt in all financial indicators has been observed post-Covid-19, with Govt. bonds showing a sporadic rise. An important observation relates to staunch spillover from GDP during GFC with reoccurrence post-Covid. Additionally, a closely knit spillover tie is observed among USDINR, N-Krishi, and Crude. The study is beneficial to RBI to proactively monitor the weakening rupee along with Fed tapering to manage the rising spillover post-Covid-19. The effort of RBI has to be reciprocated by the government in inflation targeting to reinforce the curbing efforts of rising shock spillover.
  PubDate: 2022-12-17
  DOI: 10.1007/s40953-022-00333-8
Multiscale Agricultural Commodities Forecasting Using Wavelet-SARIMA
Process
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  Abstract: Abstract Forecasts of spot or future prices for agricultural commodities make it possible to anticipate the favorable or above all unfavorable development of future profits from the exploitation of agricultural farms or agri-food enterprises. Previous research has shown that cyclical behavior is a dominant feature of the time series of prices of certain agricultural commodities, which may be affected by a seasonal component. Wavelet analysis makes it possible to capture this cyclicity by decomposing a time series into its frequency and time domains. This paper proposes a time-frequency decomposition based approach to choose a seasonal auto-regressive aggregate (SARIMA) model for forecasting the monthly prices of certain agricultural futures prices. The originality of the proposed approach is due to the identification of the optimal combination of the wavelet transformation type, the wavelet function and the number of decomposition levels used in the multi-resolution approach (MRA), that significantly increase the accuracy of the forecast. Our SARIMA hybrid approach contributes to take into account the cyclicity and of the seasonality when predicting commodity prices. As a relevant result, our study allows an economic agent, according to his forecasting horizon, to choose according to the available data, a specific SARIMA process for forecasting.
  PubDate: 2022-12-17
  DOI: 10.1007/s40953-022-00329-4
Long-Memory, Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk
Estimation: Empirical Evidence from the Global Real Estate Markets
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  Abstract: Abstract In this paper, we address the question of whether long memory, asymmetry, and fat-tails in global real estate markets volatility matter when forecasting the two most popular measures of risk in financial markets, namely Value-at-risk (VaR) and Expected Shortfall (ESF), for both short and long trading positions. The computations of both VaR and ESF are conducted with three long memory GARCH-class models including the Fractionally Integrated GARCH (FIGARCH), Hyperbolic GARCH (HYGARCH), and Fractionally Integrated Asymmetric Power ARCH (FIAPARCH). These models are estimated under three alternative innovation’s distributions: normal, Student, and skewed Student. To test the efficacy of the forecast, we employ various backtesting methodologies. Our empirical findings show that considering for long memory, fat-tails, and asymmetry performs better in predicting a one-day-ahead VaR and ESF for both short and long trading positions. In particular, the forecasting ability analysis points out that the FIAPARCH model under skewed Student distribution turns out to improve substantially the VaR and ESF forecasts. These results may have several potential implications for the market participants, financial institutions, and the government.
  PubDate: 2022-12-17
  DOI: 10.1007/s40953-022-00331-w
Electricity Tariff Changes and Consumer Sentiment on Household Consumption
Expenditure in Malaysia
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  Abstract: Purpose This study investigates the impact of the current electricity tariff, represented by optimized tariff calculation, on Malaysian household’s consumption across different levels of income. Design/methodology/approach The input-output (IO) model has vastly been used in many energy economics literature that presented the matrix of production between various sectors in an economy. This study aggregated the 124 subsectors in IO Price Table 2015 into 12 groups of consumption of goods and services based on Household Expenditure Survey 2019 (HES 2019) to meet the study’ objectives. Findings This study found that in all simulations, high-income earners would be highly affected by the tariff changes. The lower the increment level in electricity tariff rate, the lower the magnitude would be on the changes of household expenditure level. Research limitations/implications Optimization in electricity tariff consumptions can pattern the Malaysian household’s consumption across different levels of income efficiently. Practical implications Useful to all consumer in the Malaysia economic business sector to predict their energy consumption up to optimum level. Social implications The study’s findings can benefit the society in optimiza their electricity consumption since everyone requires the energy for basic needs in their life.
  PubDate: 2022-12-17
  DOI: 10.1007/s40953-022-00327-6
Heckscher-Ohlin Theory or the Modern Trade Theory: How the Overall Trade
Characterizes at the Global Level'
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  Abstract: Abstract This paper has two objectives: to locate the global trade pattern and to compute the export potential of world economies. Considering the maximum number of countries and maintaining a good representative sample of the overall international trade, an empirical examination is conducted by utilizing the trade complementary index and the per-capita income variable in the standard gravity model. The main aim is to determine which of the two theoretical frameworks―either the Heckscher-Ohlin theory, which is based on factor endowments or the Modern Trade theory of Krugman-Helpman and Linder, based on the intra-industry trade―is explaining the overall global trade flows. The estimated results support the factor endowments trade theory. In other words, the observed trade patterns conform to the Heckscher-Ohlin theory of trade over intra-industry Modern trade theories. The inference drawn is based on the significantly positive coefficient of the trade complementarity index and the absolute differenced PCI variable. Furthermore, as far as export potential is concerned, there exists a vast scope for the export potential across economies. These countries can exploit the existing export potential through trade cooperation and integration at the regional and the bilateral level.
  PubDate: 2022-12-01
  DOI: 10.1007/s40953-022-00330-x
A Socio Economic Perspective of Intergenerational Educational Mobility:
Experience in West Bengal
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  Abstract: Abstract Intergenerational educational mobility has been at the heart of development discourse particularly in the sense of equality of long term income opportunity. There have been persistent gaps in educational attainment across alternative socio-economic groups. Higher mobility in the marginalized classes, however, can bring about a convergence between the less and more privileged in the society. This paper explores primary data from survey in selected districts of West Bengal to analyse the extent of intergenerational educational mobility and its association with selected socio economic variables. This paper uses two related methodologies; (1) intergenerational simple regression coefficient and (2) ordered logistic technique. We have observed a strong association between parental education and child’s mean years of schooling, i.e. the society is largely immobile. Moreover, the analysis shows that the probability of the ward’s attaining tertiary level of education increases if father’s education falls in the tertiary level. However, for cross pairs i.e. father-daughter and mother-son the association is more pronounced. Furthermore, disaggregating the data at subgroups the analysis looks into the inter relationship between parental educational attainment and the child’s probability of reaching different educational levels across religion, castes, gender, economic condition and region. The results have been varied across the subgroups.
  PubDate: 2022-12-01
  DOI: 10.1007/s40953-022-00313-y
Quantitative Easing Policy and Income Inequality in the U.S. Economy:
Evidence from a FAVAR Model
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  Abstract: Abstract The aim of this paper is to analyze the impact of monetary base shocks on different measures of inequality in the U.S. economy for the periods before and after the implementation of the quantitative easing (QE) policy. In order to take additional information into account, the Factor-Augmented Vector Autoregressive (FAVAR) model is built and used in estimation. To extract the component factors, the Principal Component Analysis (PCA) method is applied on data of 115 series of monthly data in the U.S. economy for the period of January 1997 to September 2018. The Impulse Response Function (IRFs) results of FAVAR models show that the expansionary monetary policy does not affect different measures of inequality for the periods before and after QE policy implementation in the short run, but there is a long-run relationship between them for the two periods. The results also show that there are no signs of a “price puzzle” nor “liquidity puzzle” or “exchange rate puzzle” for the period after QE policy implementation.
  PubDate: 2022-12-01
  DOI: 10.1007/s40953-022-00316-9
Monetary Response to Oil Price Shock in Asian Oil Importing Countries:
Evaluation of Inflation Targeting Framework
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  Abstract: Abstract Oil price shock can have serious inflation consequences and, therefore, elicit timely response from the Central Banks (CB). Our paper analyses the monetary policy response to oil prices in 5 Asian EMDEs during 2008–2019. Not only these countries are heavily dependent on oil imports for their increasing demand for oil, but also the CBs of these countries have gradually adopted inflation targeting (IT) framework of monetary policy. Therefore, it is also imperative to examine the weightage the CBs assign to oil price shock, while operating in IT-framework as against in non-IT-framework. This forms the second important objective of our paper. We estimate the CBs’ decision on interest rates as response to Brent crude oil price using augmented Taylor rule, in a macro panel data framework. We employ estimators that allows for heterogeneity across countries and are robust to cross sectional dependence. Oil importing Asian countries face a negative supply shock, due to trade linkages, that exerts a greater impact on inflation. Consequently, our finding that CBs raised interest rates to prevent subsequent negative demand effects in the long run, is intuitive. Interestingly, our results also reveal that CBs following IT-framework resorted to interest setting at a lower level as a response to oil price shock. This asserts the efficacy of the IT-framework in terms of absorbing oil shocks.
  PubDate: 2022-11-02
  DOI: 10.1007/s40953-022-00328-5
Hazard Analysis of Unemployment Duration of Return Migrants: The Case of
Indian State of Kerala
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  Abstract: Abstract Unfavourable conditions in the Middle East countries opened the door for a high influx of Indian migrants to their countries of origin. However, retrofitting them into domestic economy is a difficult task. Being aware of the duration of unemployment of return migrants and its determinants is crucial for evaluating labour market activities and implementation of policies. This paper fills the gap by examining the factors that determine the duration of unemployment of return migrants in Indian State of Kerala. By applying Kaplan Meier Survival Function and Cox Proportional hazard regression models, the study found that of all variables, a strong social network enabled returnees to be reabsorbed into labour market at home faster. The paper, therefore, makes a strong case for the government for befitting returnees in the labour market.
  PubDate: 2022-10-18
  DOI: 10.1007/s40953-022-00325-8
Money and Happiness in India: Is Relative Comparison Cardinal or Ordinal
and Same for All'
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  Abstract: Abstract The relative income hypothesis is often invoked to explain the absence of a systematic long-run relationship between income and happiness. It is not yet clear with what and with whom individuals compare their income, whether the social comparison is cardinal or ordinal, and whether the effect of such reference income is the same for all in their evaluation of life satisfaction. Studies often estimate the relative income effect on happiness using cardinal average reference income by ordered probit regression, in which the covariate effects are constant across happiness levels. To overcome these twin issues, this paper specifies two alternative ordinal relative income measures, rich or poor relative to average income and rank position within the reference group income distribution, and estimates their differential effects across happiness distribution by panel random effects generalised ordered probit method. The panel REGOPROB estimates of WVS data of India over a longer period of 24 years from 1990 to 2014 across states show that Indian people are more sensitive to social comparison than to individual income and the ordinal comparison is stronger than the cardinal comparison in the evaluation of life satisfaction. A rise in the rank position within the reference group is relatively more important for people with average levels of life satisfaction than for individuals at the extremes of life satisfaction distribution, either dissatisfied/unhappy or satisfied/happy Indians.
  PubDate: 2022-10-12
  DOI: 10.1007/s40953-022-00326-7
An Optimal Crime Control Policy in a Dynamic Setting
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  Abstract: Abstract Existing literature does not capture efficiency losses on the dynamic adjustment path of crime control market from initial to final equilibrium after a shock in order to formulate an optimal crime control policy. Furthermore, number of public service units and crime control rate are major determinants of crimes controlled in a society, and a policy without taking into consideration such vital determinants cannot ensure adjustment of number of crimes controlled as a result of cost movement in desired time, which may lead to extra efficiency losses than those envisaged during policy formulation for an optimal level of crime control in a society. This article designs a comprehensive optimal crime control policy mechanism by modeling a three dimensional crime control system in society capturing number of public service units, crime control rate, and cost, while taking into account efficiency losses during adjustment of crime control market, crime control rate and number of public service units in addition to those which result due to movements from initial to final equilibriums.
  PubDate: 2022-09-06
  DOI: 10.1007/s40953-022-00323-w