Inference on varying coefficients in spatial autoregressions
We present simple to implement Wald-type statistics that deliver a general nonparametric inference theory for linear restrictions on varying coefficients in a range of spatial autoregressive models. Our theory covers error dependence of a general form, allows for a degree of misspecification robustness via nonparametric spatial weights and permits inference on both varying regression and spatial coefficients. One application of our method finds evidence for constant returns to scale in the production function of the Chinese nonmetal mineral industry, while another finds a nonlinear impact of the distance to the employment center on housing prices in Boston. A simulation study confirms that our tests perform well in finite-samples.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demand…
Computational EfficiencySubgeometrically ergodic autoregressions with autoregressive conditional heteroskedasticity
In this paper, we consider subgeometric (specifically, polynomial) ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The notion of subgeometric ergodicity was i…
Measuring international uncertainty using global vector autoregressions with drifting parameters
This paper investigates the time-varying impacts of international macroeconomic uncertainty shocks. We use a global vector autoregressive specification with drifting coefficients and factor stochastic volatility in the e…
BVARs and Stochastic Volatility
Bayesian vector autoregressions (BVARs) are the workhorse in macroeconomic forecasting. Research in the last decade has established the importance of allowing time-varying volatility to capture both secular and cyclical …
Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies. However, as the number of series and high-frequency observations per low-frequency period grow, MF-V…