Structural Inflation Models with Real Wage Rigidities: The Case of Canada

Real wage rigidities have recently been proposed as a way of building intrinsic persistence in inflation within the context of New Keynesian Phillips Curves. Using two recent illustrative structural models, we evaluate empirically the importance of real wage rigidities in the data and the extent to which such models provide useful information regarding price stickiness. Structural estimation and testing is carried out using Canadian data and identification-robust methods. Results based on one of the models are relatively uninformative. Our tests reveal important identification difficulties and considerable estimate uncertainty, as can be seen from the wide projections for the estimates. However, we obtain economically reasonable ranges for estimates of average frequency of price changes and some evidence for rigidity in real wages (as measured by a rigidity index) based on the other model we examine. In addition, our specification for the latter model yields significant [at usual levels] and correctly-signed reduced-form coefficient estimates, showing a trade-off between unemployment and inflation in the New Keynesian Phillips curve. From a methodological perspective, these results derive from our treatment of the productivity term as observable although with error, which seems to capture vital information and improve overall identification. From a substantive perspective, our findings suggest that wage-rigidity based New Keynesian Phillips Curves hold promise empirically and provide interesting research directions.Inflation and prices; Labour markets; Econometric and statistical methods

Inference for High-Dimensional Sparse Econometric Models

This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is well-approximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regression function effectively by searching for approximately the right set of regressors. We discuss methods for identifying this set of regressors and estimating their coefficients based on $\ell_1$-penalization and describe key theoretical results. In order to capture realistic practical situations, we expressly allow for imperfect selection of regressors and study the impact of this imperfect selection on estimation and inference results. We focus the main part of the article on the use of HDS models and methods in the instrumental variables model and the partially linear model. We present a set of novel inference results for these models and illustrate their use with applications to returns to schooling and growth regression

Econometrics: A bird's eye view

As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledge and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks and forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of the "real time econometrics". This paper attempts to provide an overview of some of these developments

System identification, time series analysis and forecasting:The Captain Toolbox handbook.

CAPTAIN is a MATLAB compatible toolbox for non stationary time series analysis, system identification, signal processing and forecasting, using unobserved components models, time variable parameter models, state dependent parameter models and multiple input transfer function models. CAPTAIN also includes functions for true digital control

The New Keynesian Phillips Curve and Lagged Inflation: A Case of Spurious Correlation?

The New Keynesian Phillips Curve (NKPC) specifies a relationship between inflation and a forcing variable and the current period’s expectation of future inflation. Most empirical estimates of the NKPC, typically based on Generalized Method of Moments (GMM) estimation, have found a significant role for lagged inflation, producing a “hybrid” NKPC. Using U.S. quarterly data, this paper examines whether the role of lagged inflation in the NKPC might be due to the spurious outcome of specification biases. Like previous investigators, we employ GMM estimation and, like those investigators, we find a significant effect for lagged inflation. We also use time varying coefficient (TVC) estimation, a procedure that allows us to directly confront specification biases and spurious relationships. Using three separate measures of expected inflation, we find strong support for the view that, under TVC estimation, the coefficient on expected inflation is near unity and that the role of lagged inflation in the NKPC is spurious.New Keynesian Phillips curve; time-varying coefficients; spurious relationships.