Presidential Approval in Peru: An Empirical Analysis Using a Fractionally Cointegrated VAR

La aprobación presidencial en Perú depende de los resultados económicos. Sin embargo, los votantes no pueden distinguir entre los resultados resultantes de las políticas económicas y los causados ​​por choques exógenos. Los resultados de la estimación de siete modelos VAR fraccional cointegrado (FCVAR) sugieren que la aprobación presidencial aumenta con la tasa de interés de política monetaria, los términos de intercambio y el empleo manufacturero; y disminuye con el tipo de cambio nominal PEN / USD y la volatilidad ináation. Además, un Análisis de Componentes Principales (PCA) realizado sobre un amplio conjunto de indicadores macroeconómicos apunta a una mayor influencia de factores externos que internos para explicar la aprobación presidencial; es decir, los resultados económicos que determinan la dinámica de la aprobación presidencial no están bajo el control presidencial en Perú. Se puede argumentar que estas conclusiones identifican una fuente importante de inestabilidad política y un desafío considerable para la gobernabilidad democrática. Según el conocimiento de los autores, esta es la primera aplicación del análisis de cointegración fraccional a la economía política en América Latina.

The Stochastic Stationary Root Model

We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement error. Unlike the cointegrated VAR model, estimation and inference for the SSR model is complicated by a lack of closed-form expressions for the likelihood function and its derivatives. To overcome this, we introduce particle filter-based approximations of the log-likelihood function, sample score, and observed Information matrix. These enable us to approximate the ML estimator via stochastic approximation and to conduct inference via the approximated observed Information matrix. We conjecture the asymptotic properties of the ML estimator and conduct a simulation study to investigate the validity of the conjecture. Model diagnostics to assess model fit are considered. Finally, we present an empirical application to the 10-year government bond rates in Germany and Greece during the period from January 1999 to February 2018.

Searching for a Theory that fits the Data: A Personal Research Odyssey

This survey paper discusses the Cointegrated VAR methodology and how it has evolved over the last 30 years. The first section is a description of major steps in the econometric development of the CVAR model that facilitated serious real world applications. The next three sections are primarily methodological and discuss (i) difficulties and puzzles when confronting theory with the data, (ii) the formulation of a viable link between theory and the data, a so called theory-consistent CVAR scenario, and (iii) how all this was inspired by Trygve Haavelmo and his Nobel prize winning monograph "The Probability Approach to Economics". The next two sections discuss early applications of the Cointegrated VAR model to monetary transmission mechanisms, international transmission mechanisms and wage, price and unemployment dynamics. They report puzzling evidence, discuss the need for new theory, and propose a method for combining partial CVAR analyses into a larger macroeconomic model. The following sections propose a new, empirically-based, approach to macroeconomics in which imperfect knowledge based expectations replace so called rational expectations and in which the financial sector plays a key role for understanding the long persistent movements in the data. The last section argues that the CVAR can act as a "design of experiment for passive observations" and illustrates with several applications including unemployment dynamics under crises periods and aid effectiveness in South Saharan African countries.