This paper considers transformations of nonlinear semiparametric mean functions that yield moment conditions for estimation. Such transformations are said to be information equivalent if they yield the same asymptotic efficiency bound. I derive a unified theory of algebraic equivalence for...

We propose two robust bootstrap-based simultaneous inference methods for time series models featuring time-varying coefficients and conduct an extensive simulation study to assess their performance. Our exploration covers a wide range of scenarios, encompassing serially correlated,...

This study investigates the asymmetric relationship between global and national factors and domestic food prices in Turkey, considering the recent rapid and continuous increase in domestic food prices. In this context, six global and three national explanatory variables were included, and...

Overconfidence behavior, one form of positive illusion, has drawn considerable attention throughout history because it is viewed as the main reason for many crises. Investors' overconfidence, which can be observed as overtrading following positive returns, may lead to inefficiencies in stock...

This article presents theoretical foundations for original Phillips curve formulation and an empirical investigation, where the structure of the theoretical model serves as a template for the creation of the empirical model. For a couple of decades the majority of empirical Phillips curve type...

This paper develops a new econometric framework to estimate and classify exchange rate regimes. They are classified into four distinct categories: fixed exchange rates, BBC (band, basket and crawl), managed floating, and freely floating. The procedure captures the patterns of exchange rate...

We propose a novel methodology for modeling and forecasting multivariate realized volatilities using graph neural networks. This approach extends the work of Zhang et al. [2022] (Graph-based methods for forecasting realized covariances) and explicitly incorporates the spillover effects from...

We consider penalized extremum estimation of a high-dimensional, possibly nonlinear model that is sparse in the sense that most of its parameters are zero but some are not. We use the SCAD penalty function, which provides model selection consistent and oracle efficient estimates under suitable...

In this paper we use the enhanced consumption data in the Panel Survey of Income Dynamics (PSID) from 2005-2017 to explore the transmission of income shocks to consumption. We build on the nonlinear quantile framework introduced in Arellano, Blundell and Bonhomme (2017). Our focus is on the...

This paper introduces non-linear dimension reduction in factor-augmented vector autoregressions to analyze the effects of different economic shocks. We argue that controlling for non-linearities between a large-dimensional dataset and the latent factors is particularly useful during turbulent...

Methodologies such as situational importance and Shapley Additive explanations (SHAP) are increasingly used to explain machine learning (black-box) models predictions. Based on parametric inference, we develop a methodological framework that uses Shapley values to generalize the data-generating...

This paper presents an estimation of the fiscal multipliers for Saudi Arabia, conducted by applying the local projection (LP) method. It also presents an exploration of the non-linear features of fiscal multipliers. The findings showed that (i) consistent with earlier studies, fiscal multipliers...

Density forecasts of euro area inflation are a fundamental input for a medium-term oriented central bank, such as the European Central Bank (ECB). We show that a quantile regression forest, capturing a general non-linear relationship between euro area (headline and core) inflation and a large...

We use a Bayesian Threshold Vector Autoregression model identified through sign and narrative restrictions to uncover non-linearities in the propagation of energy supply shocks. We find that the transmission of energy supply shocks on consumer prices is stronger in high-inflation regimes,...

While demand models require a sound understanding of economic processes and should be flexible enough to capture nonlinearities, endogeneity can greatly hinder the identification of (nonlinear) causal relationships. To tackle these issues, we extend the instrument-free Gaussian copula approach...

Consumer prices have increased in Japan since the pandemic began, when commodity prices increased worldwide. This study investigates commodity price pass-through into inflation in the low and high inflation regimes in Japan. By estimating the threshold autoregressive model that considers past...

This paper studies the design of Ramsey optimal monetary policy in a Health New Keynesian (HeNK) model with Susceptible, Infected and Recovered (SIR) agents. The nonlinear model is estimated with maximum likelihood techniques on Euro Area data. Our objective is to deconstruct the mechanism by...

The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN) algorithm. The data used in this study is...

Background Inflation is the industrious and non-stop ascent in the overall prices of any given commodity in an economy. During the global food crisis, Ethiopia experienced an unprecedented increase in inflation ranked the highest in Africa. It is among the most macroeconomic variable described...

We propose a statistical identification procedure for recursive structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise...

Accurately modelling and forecasting electricity consumption is a key prerequisite for strategic sustainable energy planning and development. In this study, we use four advanced econometrics time series models and four machine learning (ML) and deep learning models including an AR with...

We present a pathwise deep Backward Stochastic Differential Equation (BSDE) method for Forward Backward Stochastic Differential Equations with terminal conditions that time-steps the BSDE backwards and apply it to the differential rates problem as a prototypical nonlinear problem of independent...

We show that US financial uncertainty has nonlinear spillover effects on the conditional distribution of forecasted GDP growth worldwide. This nonlinearity stems from asymmetric responses of domestic and international credit conditions following a US financial uncertainty shock. Through the...

Tail risk protection is a mantra in portfolio allocation. A common method in this context is the NMFRB allocation. Here, we extend it to drawdown risk measures and show that the proposed portfolios compete with machine learning-based portfolios such as Hierarchical Risk Parity (HRP) and...