The primary objective of this paper is to propose two nonlinear extensions for macroeconomic forecasting using large datasets. First, we propose an alternative technique for factor estimation, i.e., kernel principal component analysis, which allows the factors to have a nonlinear relationship to...

The debate on the forecasting ability in economics of non-linear models has a long history, and the Great Recession provides us with an opportunity for a re-assessment of the forecasting performance of several classes of non-linear models, widely used in applied macroeconomic research. In this...

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

We develop optimal formulations for nonlinear autoregressive models by representing them as linear autoregressive models with time-varying temporal dependence coefficients. We propose a parameter updating scheme based on the score of the predictive likelihood function at each time point. The...

We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The...

We study the exclusionary properties of nonlinear price-quantity schedules in an Aghion-Bolton style model with elastic demand and product differentiation. We distinguish three regimes depending on whether and how the price of the incumbent good is linked to the quantity purchased from the rival...

We adapt the exclusion model of Choné and Linnemer (2014) to reflect the notion that dominant firms are unavoidable trading partners. In particular, we introduce the share of the buyer's demand that can be addressed by the rival as a new dimension of uncertainty. Nonlinear price-quantity...

The aim of this paper is to contribute to the understanding of the behavior of participation rates in terms of gender differences. We employed smooth autoregressive transition models for the quarterly Turkish labor force participation rates (LFPR) data between 2000: Q1-2011: Q4 to present an...

We use top income data and the newly developed regime switching Gaussian mixture vector autoregressive model to explain the dynamics of income inequality in developed economies during the last 100 years. Our results indicate that the process of income inequality consists of two equilibriums...

The paper studies inference in nonlinear models where identification loss presents in multiple parts of the parameter space. For uniform inference, we develop a local limit theory that models mixed identification strength. Building on this non-standard asymptotic approximation, we suggest robust...