Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend

Does theory aid inflation forecasting? To address this question, we develop a novel forecasting procedure based upon a New Keynesian Phillips Curve that incorporates time-varying trend inflation, to capture shifts in central bank preferences and monetary policy frameworks. We generate theory-implied predictions for both the trend and cyclical components of inflation, and recombine them to obtain an overall inflation forecast. Using quarterly data for the Euro Area and the United States that cover almost half a century, we compare our inflation forecasting procedure against the most popular time series models. We find that our theory-based forecasts outperform these benchmarks that previous studies found difficult to beat. Our results are shown to be robust to structural breaks, geographic areas, and variants of the econometric specification. Our findings suggest that the scepticism concerning the use of theory in forecasting is unwarranted, and theory should continue to play an important role in policymaking.     

Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach*

ABSTRACT

In this article, the multihorizon predictive power of the Hybrid New Keynesian Phillips Curve (HNKPC) is analysed by making use of several close- and open-economy specifications for the headline inflation of six developed countries. The key element is the use of direct measures of inflation expectations – Consensus Forecast – embedded in a compact-scale Global VAR (GVAR) environment, becoming the baseline open-economy HNKPC (OE-HNKPC) specification. These OE-HNKPC point forecasts are evaluated using the Root Mean Squared Forecast Error (RMSFE) statistic and statistically compared with several benchmarks, including traditional atheoretical models. Several OE-HNKPC as well as a closed-economy HNKPC (CE-HNKPC) specifications are also analysed. The results indicate that in four out of six countries, the CE-HNKPC is the best forecasting model, whereas for the same countries, a parsimonious OE-HNKPC is the second-best alternative, and in most cases, outperforming traditional statistical benchmarks. The RMSFE is obviously affected by the unanticipated effects of the Great Financial Crisis (GFC), spoiling out the performance of a number of competing forecasts. However, when considering an evaluation sample just before the crisis, both the CE-HNKPC and the parsimonious OE-HNKPC still come out as the best forecasting models. Furthermore, these preferred models also do an excellent job tracking inflation better than the best atheoretical models during the GFC.     

Detecting periods of exuberance: A look at the role of aggregation with an application to house prices

The recently developed SADF and GSADF unit root tests of Phillips and Yu (2011) and Phillips et al. (2015a,b) have become popular in the literature for detecting exuberance in asset prices. In this paper, we examine through simulation experiments the effect of cross-sectional aggregation on the power properties of these tests. The simulation design considered is based on simulated data and actual housing data for both U.S. metropolitan areas and international housing markets and thus allows us to draw conclusions for different levels of aggregation. Our findings suggest that aggregation lowers the power of both the SADF and GSADF tests. The effect, however, is much larger for the SADF test. We also provide evidence that tests based on panel data techniques, namely the panel GSADF test recently proposed by Pavlidis et al. (2016), can perform substantially better than univariate tests applied to aggregated series. Furthermore, we also illustrate the date-stamping procedure under the univariate/panel GSADF procedure uncovering novel evidence on the role of interest rates and policy uncertainty as factors explaining episodes of widespread mildly explosive dynamics in housing markets.