In econometrics, Autoregressive Conditional Duration (ACD) models use high-frequency economic or financial duration data, which mostly exhibit irregular time intervals. The ACD model is widely used to examine the duration of transaction volume and duration of price variations in stock markets....

Detecting heterogeneity within a population is crucial in many economic and financial applications. Econometrically, this requires a credible determination of multimodality in a given data distribution. We propose a straightforward yet effective technique for mode inference in discrete data...

We develop a new model of cycles and crises in emerging markets, featuring an occasionally binding borrowing constraint and stochastic volatility, and estimate it with quarterly data for Mexico since 1981. We propose an endogenous regime‐switching formulation of the occasionally binding...

Belief‐updating biases hinder the correction of inaccurate beliefs and lead to suboptimal decisions. We complement Rabin and Schrag's (1999) portable extension of the Bayesian model by including conservatism in addition to confirmatory bias. Additionally, we show how to identify these two...

We discuss the timing and strength of the Fed's reaction to the recent inflation surge within an estimated macroeconomic model where long-run inflation expectations are heterogeneous and can lose their anchoring to the target. The resulting inflation scare worsens the real cost of disinflation....

The natural rate of interest, often interpreted as the equilibrium real interest rate, serves as a critical benchmark for evaluating the stance of monetary policy. This paper investigates the natural rate of interest in Uzbekistan using three econometric approaches: the HLW-type model1 , a...

We utilize Bayesian model averaging to estimate a stochastic discount factor (SDF) for single-stock options. A Bayesian model averaging SDF outperforms reduced-form benchmark models in-sample and out-of-sample in pricing option return anomalies and portfolios. We document that the SDF is dense...

This study investigates the application of Factor-Augmented Vector Autoregression (FAVAR) and Bayesian Vector Autoregression (BVAR) models for inflation forecasting. FAVAR models deal with high-dimensional data by extracting latent factors from extensive macroeconomic indicators, while BVAR...

In today's context of escalating environmental pressure, traditional methods of natural resource conservation face numerous challenges. The use of big data analytics to support the formulation of environmental policies has become a crucial approach for enhancing the efficiency and scientific...

A modified dynamic model averaging framework, which allows for inferences regarding the shifting relevance and significance of explanatory variables, is employed to evaluate the in-sample performance of exchange rate models. This analysis is based on a set of 16,384 model specifications derived...

Several process parameters affect product reliability. Traditional reliability improvement methods primarily focus on maximizing product lifetime, often overlooking the variation in product lifetime. Manufacturers, however, aim to produce products with minimal variations in their performance....

A modified dynamic model averaging framework, which allows for inferences regarding the shifting relevance and significance of explanatory variables, is employed to evaluate the in-sample performance of exchange rate models. This analysis is based on a set of 16,384 model specifications derived...

Behavioral science interventions like incentives, nudges, and boosts are increasingly used in public policy, but their effectiveness remains debated. We conducted a meta-meta-analysis on behavior change interventions across health, finance, and sustainability outcomes. Our analysis covers 838...

We develop a new method for deriving high-frequency synthetic distributions of consumption, income, and wealth. Modern theories of macroeconomic dynamics identify the joint distribution of consumption, income, and wealth as a key determinant of aggregate dynamics. Our novel method allows us to...

I develop a methodology for Bayesian estimation of globally solved, non-linear macroeconomic models. A novel feature of my method is the use of a mixture density network to approximate the distribution of initial states. I use the methodology to estimate a medium-scale, two-agent New Keynesian...

In many manuscripts, researchers use multivariable logistic regression to adjust for potential confounding variables when estimating a direct relationship of a treatment or exposure on a binary outcome. After choosing how variables are entered into that model, researchers can calculate an...

I use Bayesian VARs with stochastic volatility to forecast global temperatures and sea level and ice cover in the Northerin emisphere until 2010, by exploiting (i) their long-run equilibrium relationship with climate change drivers (CCDs) and (ii) the relationship between world GDP and...

Purpose - Developing price forecasts for various agricultural commodities has long been a significant undertaking for a variety of agricultural market players. The weekly wholesale price of edible oil in the Chinese market over a ten-year period, from January 1, 2010 to January 3, 2020, is the...

The main objective of the research described in the article is to study the maximum likelihood (ML) estimation and the Bayesian approach for parameter estimation of the Lomax distribution. Additionally, the study aims to determine the approximate intervals for the parameters and the survival...

Schaeuffele et al. (2024) examined the effect of Transdiagnostic Cognitive Behavioural Psychotherapy (TD-CBT) on emotional disorders through a meta-analysis of 53 studies involving 6,705 participants. Their main findings indicated that TD-CBT has larger treatment effects on depression, g = 0.74,...

Due to the rapid growth of the Chinese housing market over the past ten years, forecasting home prices has become a crucial issue for investors and authorities alike. In this research, utilising Bayesian optimisations and cross validation, we investigate Gaussian process regressions across...

Contrary to claims in studies on financial economics, a sparse database often obscures the identification of parameters in macroeconomic models. These identification problems originate from the poorly defined mapping between a structural model and reduced-form parameters. Hence, researchers rely...

We study optimal auctions in a symmetric private values setting, where bidders have signaling concerns: they care about winning the object and a receivers inference about their type. Signaling concerns arise in various economic situations such as takeover bidding, charity auctions, procurement...

We assess how changes in the scientific consensus around equilibrium climate sensitivity (ECS), as captured by the IPCC’s Fifth (AR5) and Sixth (AR6) Assessment Reports, impact policymakers’ willingness to take climate action. Taking the IPCC’s reports at face value, the ECS estimates in...