JOURNAL OF QUANTITATIVE ECONOMICS最新文献

In order to shed light on the possible factors responsible for volatility in the financial performance of Indian banks, we primarily consider four novel variables in the study, including the COVID-19 crisis, NPLs, systemic risk, and government response. For this, we employ bank-level observations of 412 Indian commercial banks spanning 2018–2022. Using fixed-effects and 2SLS methods, we find that government response, COVID-19, and income diversification play a significant role in positively affecting the financial performance of Indian banks. However, non-performing loans, provisioning, systemic risk, and bank size are responsible for their poor performance. Projected macro-economic statistics suggest that the GDP growth rate and inflation have significantly increased the strength and resilience of Indian banks. The main evidence mainly supports the ‘bad-management’, ‘too-big-too-fail’, and ‘diversification opportunity’ hypotheses. The heterogeneity test and robustness check results are nearly identical to those reported in the main evidence. Overall, our findings reduce the concern of policymakers, though not completely eliminated, that tighter government regulation and provisioning for Indian banks may expedite the bank’s ability to withstand their credit risk, systemic risk, and exogenous shocks, which can lead to a rapid improvement in their performance.

The paper explores the significance of a borrower’s socioeconomic network in assessing creditworthiness using a novel theoretical framework. We introduce a method for a lender to consolidate the individuals’ trustworthiness of the borrower within her socioeconomic network. From the borrower’s perspective, we consider the adverse social consequences of default within their socioeconomic network, which acts as a disincentive for the borrower to default on the credit obligation. This social pressure discourages credit default. Building on this connection between trust in a socioeconomic network, our paper develops a model that incorporates aggregate trust, project riskiness, and the social cost of default to evaluate credit risk. In this framework, a borrower with a secure project and a high social cost of default is more likely to honour their credit commitments. Conversely, for a similar project, a borrower with a low social cost of default may be more inclined to wilfully default on their credit obligations.

In this article, we investigate the interrelationships between political stability, corruption, and public governance, in association with foreign direct investment (FDI) and Gross Fixed Capital formation (GFCF), and economic growth (GDP) for a global panel of 46 middle-income countries over the period 1996–2016. A multivariate panel model was employed to evaluate the long-run relationship and the panel Granger causality tests was used to judge the causality direction among different variables. The obtained results reveal that political instability in these countries affect clearly the positive relationship between FDI, GFCF and economic growth. The empirical results from the Granger causality test reveal a bidirectional causality relationship between the FDI, GFCF and GDP in presence of political factors and corruption. Moreover, our empirical findings confirm the existence of unidirectional causality running from GDP, FDI and GFC to corruption, from Government Effectiveness to FDI and GFCF. The policy implications of these results are also proposed and discussed.

When collusion is analyzed for Independent private value auctions, it is implicitly assumed that ring presence is commonly known to colluding and non-colluding bidders. We drop this assumption and analyze a simple model of a first price Independent Private Value auction with uniformly distributed values where a single bidder knows privately of the existence of collusion by others. We show that this knowledge leads him to bid shading (weakly) in the first price auction compared to what he would have bid otherwise. This in turn yields the result that the second price auction dominates the first price auction in terms of seller revenue. This contrasts results from the literature showing that under our framework, when bidding is done while the presence of colluding bidders is common knowledge, the first price auction dominates the second price auction.

This paper revisits the threshold level of inflation for India. The empirical analysis follows spline regression for the period 1996–97Q1 to 2019–20Q4. The results suggest the existence of a statistically significant threshold level of inflation at 5 to 5.5% in terms of both CPI and WPI. Below this level, the impact of inflation on growth is generally positive whereas it is negative above this level, and therefore injurious to growth. Hence, policymakers in India may consider reducing the tolerable band from 4 ± 2% to 4 ± 1.5% under the flexible inflation targeting regime with a vision to further compress it to 4 ± 1% in due course.

Abstract

This paper explores the role of crude oil in determining corn prices for data on the weekly front future prices in the United States. With 38% of corn production allocated toward fuel ethanol, a possible effect of crude oil price variation on corn price fluctuations is theoretically indicated. To test this theory, two complementary approaches—a parametric multiple regression and a non-parametric multivariate adaptive regression splines approach are employed. Along with indicating a weak relationship between corn and crude oil prices, the results suggest that corn price responds nonlinearly to the changes in soybean and wheat prices.

In this study, we investigate and apply the models from the machine learning (ML) paradigm to forecast the inflation rate. The models identified are ridge, lasso, elastic net, random forest, and artificial neural network. We carry out the analysis using a data set with 56 features of 132 monthly observations from January 2012 to December 2022. The random forest (RF) model can forecast the inflation rate with greater accuracy than other ML models. A comparison to benchmark econometric models like auto-regressive integrated moving average demonstrates the superior performance of the RF model. Moreover, nonlinear ML models are proven to be more successful than a linear ML or time series models and this is mostly due to the unpredictability and interactions of variables. It indicates that the significance of nonlinear structures for forecasting inflation is important. Furthermore, the ML models outweigh the benchmark econometric model in forecasting the undulations due to the COVID-19 impact. The findings in this study support the benefit of applying ML models to forecast the inflation rate. Even without considering the sporadicity of pandemic, nonlinear model like artificial neural network (ANN) outweighs other models. Additionally, the ML models like RF and ANN model yield variable importance measures for each explanatory variable. ML models shows capability to not only better forecasting but also able to provide the insight regarding the covariates for improved forecasting results and policy prescriptions.

How have social movements in the United States been impacted by simultaneously evolving economic realities such as episodes of development and inequality across time? This paper empirically examines how the structural forces of the economy such as growth (income per-capita) and decline (income inequality) interact with the regional characteristics to derive patterns of social movements in United States from 1960 to 1995. I suggest that—unlike the arguments found in popular social movement theories such as relative deprivation and economic grievances that the society will express resentment against lack of financial resources through protesting and riots—there will be less collective action formations during heightened inequality even when there is growth in per-capita income. This paper provides novel application of methodological approaches in social movement studies such as the Generalized Additive Models with smoothing functions and Synthetic Control Method to extract micro-level inferences on the relationship between economic factors and social movement formations. I gauge the implications of the main argument with a new dataset that is a composition of aggregated levels of social movements per-capita, real per-capita personal income, income inequality index, labor unemployment laws, social policy liberalization index and equal pay laws among other variables. The empirical exercises reveal that when accounting for the full range of socio-economic variables with fixed effects and instrumental variables, the dual impact of economic growth and decline on social movements is non-linear and U-shaped in the US states across time.

This paper examines persistence in tax revenues in a set of 21 OECD countries over the period 1965–2021 using long-range dependence techniques based on fractional integration. The results imply that there are only a few cases of mean reversion: one for total revenue (Switzerland); three for VAT (Belgium, Italy, and Spain), and six for tax on income (Austria, Belgium, Finland, Spain, Sweden and USA). The analysis is also carried out for inflation in the same set of countries. Again the I(1) hypothesis cannot be rejected in most cases, mean reversion only occurring in Korea, Iceland, Norway and Sweden. However, stronger evidence of mean reversion is found for the differences between the three original tax series and inflation compared to the tax series themselves, which points to the existence of a linkage between taxation and inflation, especially in the case of VAT and tax on income.

In alignment with classical investment theory, this study explores the enduring relationships and causal linkages among total private investment, profit rate, unit labour costs, and demand growth within the European Union throughout the period spanning from 1961 to 2019. The empirical approach adopted involves the use of advanced econometric techniques designed to address cross-sectional dependence and slope heterogeneity. As a first stage, we examine stationarity and cointegration by employing second-generation panel unit root and cointegration tests. Subsequently, we estimate long-run equations through estimators intended to control for cross-sectional dependence and slope heterogeneity. As a further step, we use the Dumitrescu-Hurlin procedure to examine potential bidirectional causality between the variables and detect whether there exists endogeneity in the data. Finally, we apply the dynamic common correlated effects estimator mean group with instrumental variables to control for the potential presence of endogeneity. The outcomes of the analysis underscore a positive association between private investment and the profit rate, unit labour costs, and demand growth, thus providing robust empirical support for the classical theory of investment.