Econometric Framework Research Articles

Trade Specialisation Pattern of Indian Agriculture under the Influence of WTO: A Study based on Galtonian Regression

The present study was undertaken to analyse the impact of WTO on the trade specialisation pattern of Indian agriculture. Twentyfive major agricultural commodities were selected at four-digit data based on HS 1988/92 classification, which were India's top agricultural export items. The study used three RCA indices such as Balassa Index, Symmetric RCA index and Normalised RCA index in an econometric framework by applying Galtonian Regression. It was found that WTO led to a convergence in the trade specialisation pattern of Indian agriculture. The commodities with initial comparative disadvantage improved their position, and those with initial comparative advantage had fallen back. This study contributed to the existing literature as Galtonian Regression's application filled up a major research gap on the trade specialisation pattern of Indian agriculture.

The Stock Market and Household Financial Behavior

In this JPMorgan Chase Institute report, we document a high-frequency relationship between stock market returns and patterns observed in consumer spending and investing behavior. The analysis draws from a core sample of approximately 12 million active Chase credit card users since 2012, and we seek to explain how the distribution of monthly credit card spending changes responds to stock market returns. The right tail of this distribution—characterized by spending increases double or triple a person’s typical level— is over two times more sensitive to the market than the center of the distribution. The relationship is more pronounced for male investors than non-investors and women. Applying the same econometric framework for stock market returns to changes in checking account-based spending and changes in labor income does not yield a statistically discernible relationship in our sample. Meanwhile, individuals’ transfers to investment accounts display a notable correlation with lagged stock returns, consistent with “returns chasing.” Such transfers roughly doubled around the onset of the COVID national emergency, alongside sharp declines in spending. Our findings imply that policies seeking to exert control over business cycles via the stock market may be successful over short time horizons. However, since stock market gains are associated with spending “splurges” on credit cards and flows into investment brokerage accounts, stimulus aimed at supporting asset prices can come with costs in the form of households’ financial vulnerability. If gains in stock prices are not followed by an improving labor market, households that over-extend themselves in terms of spending or equity market exposure would face risks.

Revisiting environmental Kuznets curve with HDI: new evidence from cross-country panel data

ABSTRACT This study investigates the existence of Environmental Kuznets curve (so for EKC) for a panel of 30 countries (as three sub-groups viz. developed economies, emerging economies and developing economies) over the period of 27 years from 1990 to 2016. The EKC proposes an inverted U-shape relation between environmental degradation and economic development. Unlike other similar studies where economic development proxied through GDP growth, in this study Human Development Index (HDI) is taken as a measure of development. Thereby the EKC existence is found here as relationship between HDI and emission (environmental degradation measured in terms of emission). The econometric framework for this study consists of Pooled OLS model, fixed effect model and fixed effect with instrumental variable model. Results showed the strong evidence of EKC for this panel of countries and also for each individual group of the countries.

Market price of risk estimation: Does distribution matter?

The econometric framework of the contemporaneous asset pricing model used by Theodossiou and Savva and Savva and Theodossiou to investigate the relationship between risk and expected returns in financial markets is generalized to a class of two-sided, asymmetry separable distributions. The latter class of distributions includes as special cases the skewed forms for the normal, Student’s t, Laplace, generalized error, generalized t, logistic and generalized type III logistic. All distributions document a positive and statistically significant relationship between risk and expected returns. A comparison of their data fitting ability shows that the generalized t distribution provides the best overall results.

Price Discovery and Market Microstructure Noise

We show that the standard econometric framework typically yields inconsistent estimates of price discovery measures in the presence of richer market microstructure noise dynamics. We address this errors-in-variable issue using instrumental variables. We devise valid instruments for two alternative microstructure noise settings, and then establish the asymptotic behavior of the corresponding price discovery measures. Our empirical analysis reveals that market leadership conclusions depend heavily on whether we account or not for the market microstructure noise.

Teams: Heterogeneity, Sorting, and Complementarity

How much do individuals contribute to team output? I propose an econometric framework to quantify individual contributions when only the output of their teams is observed. The identification strategy relies on following individuals who work in different teams over time. I consider two production technologies. For a production function that is additive in worker inputs, I propose a regression estimator and show how to obtain unbiased estimates of variance components that measure the contributions of heterogeneity and sorting. To estimate nonlinear models with complementarity, I propose a mixture approach under the assumption that individual types are discrete, and rely on a mean-field variational approximation for estimation. To illustrate the methods, I estimate the impact of economists on their research output, and the contributions of inventors to the quality of their patents.

The ECB's Tracker: Nowcasting the Press Conferences of the ECB

This paper proposes an econometric framework for nowcasting the monetary policy stance and decisions of the European Central Bank (ECB) exploiting the ow of conventional and textual data that become available between two consecutive press conferences. Decompositions of the updated nowcasts into variables' marginal contribution are also provided to shed light on the main drivers of the ECB's reaction function at every point in time. In out-of-sample nowcasting experiments, the model provides an accurate tracking of the ECB monetary policy stance and decisions. The inclusion of textual variables contributes significantly to the gradual improvement of the model performance. JEL Classification: E37, E47, E52

Teaching Them How to Fish: The Makings of Business Accelerators

One-third of early-stage venture financing currently goes to business accelerator-backed startups. We examine whether startups join accelerators to alleviate financing constraints or to improve human capital for long-term growth. Using a novel three-stage two-sided matching econometric framework, we quantify and compare explanatory powers of short-term financing prospects and long-term growth prospects for the revealed preference during the accelerator admission process. We find that short-term financing prospects explain 48% of the variations in the matching values, while variations of key five-year startup performance prospects can explain 96%.

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to mine variables of interest from available data, followed by the inclusion of those variables into an econometric framework, with the objective of estimating causal effects. Recent work highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables are likely to suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the ensemble learning technique known as the random forest. We propose employing random forest not just for prediction, but also for generating instrumental variables to address the measurement error embedded in the prediction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make different mistakes, i.e., have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the other trees serve as its instruments. Simulation experiments demonstrate the efficacy of the proposed approach in mitigating estimation biases, and its superior performance over an alternative method (simulation-extrapolation), which has been suggested by prior work as a reasonable method of addressing the measurement error problem.

Ripple Effects of House Prices of China’s Three key Regions Including First Tier and Second Tier Main 29 Cities

This paper identifies the ripple effects of house prices of China’s key three regions including first tier 5 cities (LOG(ALL5)), second tier top 8 cities (LOG(ALL8)) and second tier 16 cities (LOG(ALL16)) for 2010Q1 to 2019Q2 periods, together with the impulses from fixed assets investments toward China three key regions’ house prices covering period from 2010Q1 to 2019Q2. The empirical analysis is conducted using the robust econometric multiple frameworks, undertaken the forms of principal component method (PC), VAR and Generalized Impulse Response (GIR) techniques. Over the 20 quarters tested, strong evidences of ripple effects were found from Q1 to Q10. Stronger positive ripple effects were identified between two regions of second tier cities (LOG(ALL16) and LOG(ALL8)) versus first tier region (LOG(ALL5)) and second tier regions (LOG(ALL16) and LOG(ALL8)). This result suggests the first-tier cities in China have become less attractive to households and investors whereas second tier cities became more attractive due to their pristine nature and improved infrastructure. Furthermore, over the 20 quarters tested, a negative response was noticed on house price return of LOG(ALL5) toward LOG(FAINVEST) while house price returns of LOG(ALL8) and LOG(ALL16) toward impulses from LOG(FAINVEST) are positive. The result confirms our finding of stronger ripple effects of two regions of second tier cities due to improved infrastructure from fixed asset investments. This paper also assists readers who are interested in investing in China real estate markets. Policy makers could reference this research to set feasible housing policy with the aim to better housing market stability.

Network Effects in Corporate Financial Policies

We present a spatial econometrics framework for estimating peer effects in capital structure. This approach exploits the heterogeneous and intransitive nature of peer networks to identify economically informative structural coefficients. In models of leverage levels, we detect significant peer-effect leverage coefficients that are on the order of 0.20, indicating a moderate but substantive level of strategic complementarity in capital structure decisions. We argue that prior estimates in the literature substantially overstate the magnitude of the underlying relation. Our evidence is robust to a wide variety of model modifications and supports the hypothesis that leverage is an important strategic choice variable.

Productive efficiency in processing social security disability claims: a look back at the 1989–95 surge

Using panel data from a relatively volatile time period 1988–95, we have identified factors that account for over 80% of the longitudinal variation in the processing time of disability applications. Pending claims, workloads, percentage of SSI applications (children and adults), and the proportion of cases considered at different stages of the disability determination process explain a significant part of the variation. We found strong evidence that observed gains in organizational productivity were attained at the cost of timeliness in case dispositions. The dynamic panel data model estimated in this paper is used to compare the productive efficiency of different disability offices in a general econometric framework in which claim forecasts, staff allocations, and the number of adjudications are treated as endogenous. Our analysis suggests that there are persistent differences in the average processing time between states that can be attributed to organizational inefficiency. The importance of good forecasts of disability applications at sub-national levels is emphasized.

Resolvable and Near‐epistemic Uncertainty in Stated Preference for Olive Oil: An Empirical Exploration

AbstractWe elicit subjective probabilities of choice using a choice experiment designed to study preferences of German consumers for high quality Italian extra‐virgin olive oils. We develop an econometric framework to address the issues of resolvable and near‐epistemic uncertainty. Focussing on behaviourally meaningful quantities linked to changes in oil attributes, such as marginal probabilities and marginal willingness to pay, we found estimates of these quantities to be robust to both types of uncertainty from both discrete and fractional elicitation formats of the response variable. The frequency and presence of potentially near‐epistemic responses are explained by socio‐economic covariates, but their effects are not always in the same direction as in models of heteroskedastic choice, suggesting the existence of different sources. Rounding behaviour in subjective probabilities statements conforms with previous findings in the literature.

A Structural Analysis of Simple Contracts

This paper provides an econometric framework for analyzing simple contracts where an agent chooses between a fixed-price option and a cost-reimbursement option provided by a principal in each contracting period during possibly multiple periods. First, we propose a consistent procedure for testing the null hypothesis of a corresponding cost function being linear, which is widely assumed for tractability in the literature. Motivated by the rejection of such a null based on our empirical data, next we establish nonparametric identification, without restricting the cost function to be linear, for all model primitives conditioned on the agent exerting nonzero effort. These primitives include agent's cost and disutility functions, distribution of agent efficiency type, and parameters that characterize agent's bargaining power and intertemporal preference. Moreover we propose a consistent procedure to implement the identification results for estimation. In our empirical study, we find strong evidence against linearity of the cost function. The importance of this empirical finding is further evidenced by a welfare analysis, which shows the welfare assessment to be sensitive to the specification of cost function.

INVESTIGATING ABNORMAL VOLATILITY TRANSMISSION PATTERNS BETWEEN EMERGING AND DEVELOPED STOCK MARKETS: A CASE STUDY

The main aim of this paper is to investigate volatility spillover effects, the impact of past volatility on present market movements, the reaction to positive and negative news, among selected financial markets. The sample stock markets are geographically dispersed on different continents, respectively North America, Europe and Asia. We also investigate whether selected emerging stock markets capture the volatility patterns of developed stock markets located in the same region. The empirical analysis is focused on seven developed stock market indices, i.e. IBEX35 (Spain), DJIA (USA), FTSE100 (UK), TSX Composite (Canada), NIKKEI225 (Japan), DAX (Germany), CAC40 (France) and five emerging stock market indices, i.e. BET (Romania), WIG20 (Poland), BSE (India), SSE Composite (China) and BUX (Hungary) from January 2000 to June 2018. The econometric framework includes symmetric and asymmetric GARCH models i.e. EGARCH and GJR which are performed in order to capture asymmetric volatility clustering, interdependence, correlations, financial integration and leptokurtosis. Symmetric and asymmetric GARCH models revealed that all selected financial markets are highly volatile, including the presence of leverage effect. The stock markets in Hungary, USA, Germany, India and Canada exhibit high positive volatility after global financial crisis.

Beyond the single farm – A spatial econometric analysis of spill-overs in farm diversification in the Netherlands

Farm diversification is an important component of rural development and policy in Europe. We examine the influence of neighbouring farms on farm diversification decisions. Our analysis investigates spill-over effects between farms and different activities in a spatial econometric framework. Using census data from about 66,000 farms in the Netherlands, we find significant correlations of diversification activities between spatially proximate farms. These are positive for some activities, for example for nature conservation. On a local level, positive spatial dependencies between farms may result from (tacit) cooperation and information sharing within neighbourhoods. However, for other activities, such as on-farm sales, we find negative correlations on a higher spatial level, i.e. within the region, which could result from competition. Spatial aspects of cooperation and competition have important consequences for the success of policies supporting the uptake of farm diversification. Our findings thus reveal that policy measures promoting farm diversification require implementation beyond the scale of individual farms and single activities.

Empirical asset pricing with multi-period disaster risk: A simulation-based approach

We propose a simulation-based strategy to estimate and empirically assess a class of asset pricing models that account for rare but severe consumption contractions that can extend over multiple periods. Our approach expands the scope of prevalent calibration studies and tackles the inherent sample selection problem associated with measuring the effect of rare disaster risk on asset prices. An analysis based on postwar U.S. and historical multi-country panel data yields estimates of investor preference parameters that are economically plausible and robust with respect to alternative specifications. The estimated model withstands tests of validity; the model-implied key financial indicators and timing premium all have reasonable magnitudes. These findings suggest that the rare disaster hypothesis can help restore the nexus between the real economy and financial markets when allowing for multi-period disaster events. Our methodological contribution is a new econometric framework for empirical asset pricing with rare disaster risk.

Are oil and gas futures markets efficient? A multifractal analysis

ABSTRACT Energy futures markets have shown high volatility, giving rise to challenges regarding their pricing and efficiency. This study investigates the weak-form efficiency hypothesis for two major energy futures markets (gas and oil) over both calm and crisis periods, using a multifractal approach and intraday data to deal with the flexible econometric framework and rich information. Our results are threefold. First, we show that multifractality in lower frequency might be more biased than in intraday data, which motivates the use of the fractal approach when testing the intraday efficiency. Second, we highlight that high frequency data are characterized by a true long memory with a higher degree of persistence during the post-crisis period for the oil market. However, for lower frequencies, the long memory becomes spurious for both markets. Finally, our forecasting results show that, for the oil market, the proposed multifractal approach outperforms conventional methodologies, especially during turmoil periods.

Output Growth of Uganda’s Agriculture Sector: Does Public Expenditure on Education Matter?

We examine the multiple dimensions of the effect of public investment in education on agriculture sector output in a multivariate econometric framework. The study is underpinned by the growing interest in empirical investigations on the effects of public education expenditure on economic growth in developing countries to inform the education sector policy environment. The research employed a longitudinal study approach to examine the extent of public investment in education and effects on agriculture sector output in Uganda. The study relied on data from national statistics for the period 1982- 2017. Overall, public expenditure on education has a net positive effect on agriculture sector output. The impact of education on agriculture output has been proven to promote agriculture output through supporting farmer adoption of new productivity-enhancing technologies.

Taxation and capital formation: Non-linear effects and asymmetry between developing and developed countries

This study endeavours to shed light on the threshold for the association between corporate income taxation and capital accumulation. In particular, the study focuses on the difference between developing countries and developed countries. The new econometric framework of the dynamic panel threshold model developed by Seo and Shin (2016) and Seo et al. (2019) is employed for a global sample of 72 countries, consisting of 35 developed and 37 developing countries over the 1994–2016 period. The threshold effects of corporate income taxation on capital accumulation are documented for both the short- and long-run. Interestingly, an inverted U-shaped impact of corporate income taxation on capital accumulation is found in developing countries. In contrast, a U-shaped curve exists for developed countries. The results are checked for robustness by different measures of capital accumulation and corporate income taxation. The results imply a need to consider different tax designs for developed and developing countries.