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Baltagi and Li (2001) derived Lagrangian multiplier tests to jointly test for functional form and spatial error correlation. This companion paper derives Lagrangian multiplier tests to jointly test for functional form and spatial lag dependence. In particular, this paper tests for linear or log-linear models with no spatial lag dependence against a more general Box-Cox model with spatial lag dependence. Conditional LM tests are also derived which test for (i) zero spatial lag dependence conditional on an unknown Box-Cox functional form, as well as, (ii) linear or log-linear functional form given spatial lag dependence. In addition, modified Rao-Score tests are also derived that guard against local misspecification. The performance of these tests are investigated using Monte Carlo experiments.

Research and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.

The authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.

The estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.

This paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.

This paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.

Introduction Many recent articles on monetary economics devote considerable effort to empirically testing various current theories of money demand. Their authors search for new and better proxies to give empirical content to ‘demand‐for‐money’, ‘income’, and ‘interest‐rate’ magnitudes, standard components of money demand equations. They consider questions of which interest rate to choose from among the manifold, and whether to use Ml or perhaps some other money supply measure to represent ‘demand‐for‐money’. But these economists do not exert the same effort when giving specific form to general money demand functions. The usual research practice is to rather arbitrarily express estimating equations in either a linear or a log‐log functional form (1).

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.

The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.

There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.

With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.

In this paper we investigate the influence of institutional investors on share prices using data from companies quoted on the Athens Stock Exchange. For finance theorists the value of an investment, real or financial, is a function of its expected benefits and the riskiness of these benefits. Whatever influences are exerted by the structure of equity ownership are diversified away by efficient risk‐averse investors. Managerial and agency theorists argue that the particular ownership structure may have an effect on share value or returns. Their arguments are based (mainly) on the consequences of the separation of ownership from control. In addition to traditional methods of estimation we have used Chamberlain’s (1982) multivariate panel data estimator, which allows for arbitrary patterns of error autocorrelation and parameter temporal behavior. Among all alternative methods of estimation used, only this one produced a statistically significant and econometrically well specified relationship between share prices and institutional shareholdings.

While the extant literature is replete with theoretical and empirical studies of value at risk (VaR) methods, only a few papers have applied the concept of VaR to quantify market risk in the context of agricultural finance. Furthermore, papers that have done so have largely relied on parametric methods to recover estimates of the VaR. The purpose of this paper is to assess extreme market risk on investment in three actively traded agricultural commodity futures.

A nonparametric Kernel method was implemented which accommodates fat tails and asymmetry of the portfolio return density as well as serial correlation of the data, to estimate market risk for investments in three actively traded agricultural futures contracts: corn, soybeans, and wheat. As a futures contract is a zero‐sum game, the VaR for both short and long sides of the market was computed.

It was found that wheat futures are riskier than either corn or soybeans futures over both periods considered in the study (2000‐2008 and 2006‐2008) and that all three commodities have experienced a sharp increase in market risk over the 2006‐2008 period, with VaR estimates 10‐43 percent higher than the long‐run estimates.

Research is based on cross‐sectional data and does not allow for dynamic assessment of expenditure elasticities.

This paper differs methodologically from previous applications of VaR in agricultural finance in that a nonparametric Kernel estimator was implemented which is exempt of misspecification risk, in the context of risk management of investment in agricultural futures contracts. The application is particularly relevant to grain elevator businesses which purchase grain from farmers on a forward contract basis and then turn to the futures markets to insure against falling prices.

This paper attempts to reconcile an apparent contradiction between short‐run and long‐run movements in the price of gold. The theoretical model suggests a set of conditions under which the price of gold rises over time at the general rate of inflation and hence be an effective hedge against inflation. The model also demonstrates that short‐run changes in the gold lease rate, the real interest rate, convenience yield, default risk, the covariance of gold returns with other assets and the dollar/world exchange rate can disturb this equilibrium relationship and generate short‐run price volatility. Using monthly gold price data (1976–1999), and cointegration regression techniques, an empirical analysis confirms the central hypotheses of the theoretical model.

This study takes advantage of abundant data from the Economics Department at National Tsing Hua University to empirically evaluate whether there exist academic performance differentials between undergraduate students from two entrance channels (exam-based and application-based methods) across courses and grades. We first evaluate the academic performance between the students based on two entrance channels, and then incorporate the General Scholastic Ability Test (GSAT) score (including five subjects of Chinese Literature, Mathematics, English, Science, and Society) into the independent variables to control for the students' ability. Our empirical results exhibit the students recruited through the application-based method outperform those admitted from the exam-based method in required courses after controlling for the students' individual characteristics. Nevertheless, we found that the advantage disappears for the elective courses. Furthermore, the academic gaps between the two groups of students tend to decline or disappear when students are seniors. The findings indicate that entrance exam scores (e.g., the Scholastic Assessment Test (SAT) scores in the United States) are good indicators for predict college academic performance, making the potential function of entrance exam in Taiwan relatively comparable to that in the United States. The findings also detail that individual GSAT scores on English, Math, and Society are positively and significantly associated with his/her performance on the core courses in Economics, supporting a significant learning progression from the curricula of senior high school to the undergraduate college education.