We suggest a new parametric approach to estimate the extent of downward nominal wage rigidity in ten European countries between 1994 and 2001. The data base used throughout is the User Data Base (UDB) of the European Community Household Panel (ECHP). The proposed approach is based on the very...

In this paper we extend the traditional GARCH(1,1) model by including a functional trend term in the conditional volatility of a time series. We derive the main properties of the model and apply it to all agricultural commodities in the Mexican CPI basket, as well as to the international prices...

The purpose of this paper is to compare the accuracy of the three types of models: Autoregressive Integrated Moving Average (ARIMA) models, Holt-Winters models and Neural Network Auto-Regressive (NNAR) models in forcasting the Harmonized Index of Consumer Prices (HICP) for the countries of...

This article proposes a new identification strategy and a new estimation method for the hybrid New Keynesian Phillips curve (NKPC). Unlike the predominant Generalized Method of Moments (GMM) approach, which leads to weak identification of the NKPC with U.S. postwar data, our non-parametric...

Estimates of potential output are an important component of a structured forecasting and policy analysis system. Using information on capacity utilization, this paper extends the multivariate filter developed by Laxton and Tetlow (1992) and modified by Benes and others (2010), Blagrave and...

The price of a single-family house can be viewed as the bundle of the land price, and the replacement/construction cost of the structure, adjusted for depreciation. Given the difficulty in obtaining accurate estimates of each of the elements, previous researches estimate land price as the...

We examine the asymptotic efficiency of OLS and IV estimators in a simple dynamic structural model with a constant and two explanatory variables: the lagged dependent variable and an explanatory variable, which is also autoregressive and may include lagged or instantaneous feedbacks from the...

This contribution studies the application of heteroskedasticity robust estimation of Vector-Autoregressive (VAR) models. VAR models have become one of the most applied models for the analysis of multivariate time series. Econometric standard software usually provides parameter estimators that...

We account for time-varying parameters in the conditional expectile based value at risk (EVaR) model. EVaR appears more sensitive to the magnitude of portfolio losses compared to the quantile-based Value at Risk (QVaR), nevertheless, by fitting the models over relatively long ad-hoc fixed time...

We introduce a new estimation framework which extends the Generalized Method of Moments (GMM) to settings where a subset of the parameters vary over time with unknown dynamics. To filter out the dynamic path of the time-varying parameter, we approximate the dynamics by an autoregressive process...