Peter Robinson

Associate and Tooke Emeritus Professor of Economic Science and Statistics

Expertise: econometrics, nonparametric & semiparametric inference

Telephone:

+44 (0)20 7955 7516

Email:

p.m.robinson@lse.ac.uk

Connect:

Website

Links:

Econometrics
Department of Economics

Biography

Peter Robinson's current research interests are:

Econometrics
Time series analysis
Spatial analysis
Nonparametric inference
Semiparametric inference

Latest

Econometrics Papers

Adaptive Inference on Pure Spatial Models

26 January 2018

Jungyoon Lee and Peter M Robinson

We consider adaptive tests and estimates which are asymptotically efficient in the presence of unknown, nonparametric, distributional form in pure spatial models. A novel adaptive Lagrange Multiplier testing procedure f... Read more...

Joint Econometrics and Statistics Workshop

Inference on trending panel data

02 December 2016

Peter Robinson (LSE)

Econometrics Papers

Non-Nested Testing of Spatial Correlation

25 November 2013

Miguel A. Delgado and Peter M Robinson

We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, o... Read more...

Econometrics Papers

Panel Nonparametric Regression with Fixed Effects

25 November 2013

Jungyoon Lee and Peter M Robinson

Nonparametric regression is developed for data with both a temporal and a cross-sectional dimension. The model includes additive, unknown, individual-specifi?c components and allows also for cross-sectional and temporal ... Read more...

Econometrics Papers

Series Estimation under Cross-sectional Dependence

25 November 2013

Jungyoon Lee and Peter M Robinson

An asymptotic theory is developed for nonparametric and semiparametric series estimation under general cross-sectional dependence and heterogeneity. A uniform rate of consistency, asymptotic normality, and sufficient con... Read more...

Econometrics Papers

Improved Lagrange Multiplier Tests in Spatial Autoregressions

20 November 2013

Peter M Robinson and Francesca Rossi

For testing lack of correlation against spatial autoregressive alternatives, Lagrange multiplier tests enjoy their usual computational advantages, but the (x squared) first-order asymptotic approximation to critical valu... Read more...

Econometrics Papers

Improved Tests for Spatial Correlation

20 November 2013

Peter M Robinson and Francesca Rossi

We consider testing the null hypothesis of no spatial autocorrelation against the alternative of first order spatial autoregression. A Wald test statistic has good first order asymptotic properties, but these may not be ... Read more...

Econometrics Papers

Efficient Inference on Fractionally Integrated Panel Data Models with Fixed Effects

20 November 2013

Peter M Robinson and Carlos Velasco

A dynamic panel data model is considered that contains possibly stochastic individual components and a common fractional stochastic time trend. We propose four different ways of coping with the individual effects so as t... Read more...

Econometrics Papers

Inference on Power Law Spatial Trends (Running Title: Power Law Trends)

01 May 2011

Peter M Robinson

Power law or generalized polynomial regressions with unknown real-valued exponents and coefficients, and weakly dependent errors, are considered for observations over time, space or space-time. Consistency and asymptotic... Read more...

Econometrics Papers

Asymptotic Theory for Nonparametric Regression with Spatial Data

01 September 2010

Peter M Robinson

Nonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects ... Read more...

Econometrics Papers

Statistical Inference on Regression with Spatial Dependence

01 April 2010

Peter M Robinson and Supachoke Thawornkaiwong

Central limit theorems are developed for instrumental variables estimates of linear and semi-parametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanato... Read more...