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A banking system is essential for financial stability, especially economic growth and development. The authors investigate the dynamic linkage of key banking system stability measures, namely, liquidity, capital, profitability, and credit risk. To this end, the authors employ Panel Vector Autoregressive (VAR) to a panel data set of country-level banking system indicators from seven developing countries; from March 2010 to December 2020 (308 country quarter observations). A nation is selected on the basis of similar characteristics large and bank-based economy with the considerably same stage of economic development. The authors find a remarkable resilient feature of the banking system in which both liquidity risk and credit risk appears significant only in the short run (within three quarters). Shocks from both risk sources dissipate quickly, suggesting an internal mechanism is at work. This study provides evidence of how a good performance of financial safety net should be.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

We investigate how firing notification procedures influence wage growth. Using a sample of 33 countries over the period 2006–2015, we show that administrative requirements in cases of dismissal have a positive and significant effect on wage growth. The result is robust even after controlling for the endogeneity of the firing notification restrictions, the involvement of third parties in the wage bargaining process, the minimum wage, the firms' training policy, and the composition of employment. These findings suggest that firing notification procedures foster the growth of wages by increasing the bargaining power of incumbent workers.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

Purpose: This study analyses how investments and specific financial factors affect the financial performance of businesses in Kosovo. Exploring the relationship between investments and financial performance and their impact on performance volatility, performance is assessed using return on assets (ROA) and return on equity (ROE) investments.

Methodology: Quantitative methods using secondary data from audited financial statements of Kosova manufacturing and commercial enterprises cover a 3-year period (2019–2021), involving 40 enterprises with 120 observations. Statistical tests such as descriptive statistics, correlation analysis, linear regression, Hausman–Taylor regression, fixed effects, random effects, and generalised estimating equations (GEE) model are applied. The study also utilises ARCH–GARCH analysis to assess the relationship between investments and performance volatility.

Findings: Investments positively impact the financial performance of Kosova businesses and significantly reduce performance volatility. Long-term liabilities, retained earnings, and short-term liabilities also play a role in reducing asset return volatility, while cash flow from financial activities increases it. Investments, cash flows from financial activities, long-term liabilities, short-term liabilities, retained earnings, and solvency affect equity return volatility.

Practical Implications: The study sheds light on how investments and financial factors influence the financial performance and volatility of Kosova businesses. Policymakers can use these insights to create policies that foster the development of commercial and manufacturing enterprises, given their importance in Kosovo’s economy.

Significance: This research provides valuable insights for business managers to enhance investment strategies and improve financial performance. Policymakers can rely on this academic study to enhance the economic environment and promote the growth of businesses in Kosovo.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.