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The authors derive a risk lower bound in estimating the threshold parameter without knowing whether the threshold regression model is continuous or not. The bound goes to zero as the sample size n grows only at the cube-root rate. Motivated by this finding, the authors develop a continuity test for the threshold regression model and a bootstrap to compute its p-values. The validity of the bootstrap is established, and its finite-sample property is explored through Monte Carlo simulations.

Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series. The initial history, published in 2012 for the 30th Anniversary Volume, describes key events in the history of the series and provides information about key authors and contributors to Advances in Econometrics. The authors update the original history and discuss significant changes that have occurred since 2012. These changes include the addition of five new Senior Co-Editors, seven new AIE Fellows, an expansion of the AIE conferences throughout the United States and abroad, and the increase in the number of citations for the series from 7,473 in 2012 to over 25,000 by 2022.

This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.

The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, the authors define the notion of forecast depth, which provides a ranking among different forecasts based on their normalized forecast errors during the training period. The forecast combination is in the form of a depth-weighted trimmed mean. The authors derive the limiting distribution of the depth-weighted forecast combination, based on which the authors can readily construct prediction intervals. Using this novel forecast combination, the authors predict the national level of new COVID-19 cases in the United States and compare it with other approaches including the ensemble forecast from the Centers for Disease Control and Prevention (CDC). The authors find that the depth-weighted forecast combination yields more accurate and robust predictions compared with other popular forecast combinations and reports much narrower prediction intervals.

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross-sectional and panel data models, and in this chapter we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point-identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a “non-standard” exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor.

Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact on the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between alternative oil market models, let alone the basis for the sometimes divergent conclusions reached in the literature. The purpose of this survey is to provide a guide to this literature. Our focus is on the econometric foundations of the analysis of oil market models with special attention to the identifying assumptions and methods of inference.

In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

The authors consider the quasi maximum likelihood (MLE) estimation of dynamic panel models with interactive effects based on the Ahn et al. (2001, 2013) quasi-differencing methods to remove the interactive effects. The authors show that the quasi-difference MLE (QDMLE) over time is inconsistent when N→∞ whether T is fixed or goes to infinity. On the other hand, the QDMLE is consistent and asymptotically unbiased if the difference is taken over individuals when T is large whether N is fixed or large. Monte Carlo studies are conducted to compare the performance of the QDMLE using different quasi-difference methods.