Multi-horizon inflation forecasts using disaggregated data

In this paper we use multi-horizon evaluation techniques to produce monthly inflation forecasts for up to twelve months ahead. The forecasts are based on individual seasonal time series models that consider both, deterministic and stochastic seasonality, and on disaggregated Consumer Price Index (CPI) data. After selecting the best forecasting model for each index, we compare the individual forecasts to forecasts produced using two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. Applying these techniques to 16 indices of the Mexican CPI, we find that the best forecasts for headline inflation are able to compete with those taken from surveys of experts.     

The use of consumer and business surveys in forecasting

Surveys improve forecasting performance by adding explanatory power to a model which is based on only past values of manufacturing growth. The issue addressed in this paper is whether surveys of production expectations, when added to equations that contain lagged values of a headline index pertaining to the real economy, improve forecasting performance. If so, it may be better for researchers to use not just the headline index, but production expectations or the Economic Sentiment Indicator if they wish to better predict manufacturing growth.     

Time scale evaluation of economic forecasts

A maximal overlap discrete wavelet transform is used to obtain time scale decompositions of economic forecasts and their errors. The generated time scale components can be used in loss measures and tests for comparing forecast accuracy to evaluate whether the forecasts accurately capture the cyclical features of the data.     

News and monetary shocks at a high frequency: A simple approach

We develop a simple approach to identify economic news and monetary shocks at a high frequency. The approach is used to examine financial market developments in the United States following the Federal Reserve’s May 22, 2013 taper talk suggesting that it would begin winding down its quantitative easing program. Our findings show that the sharp rise in 10-year Treasury bond yields immediately after the taper talk was largely due to monetary shocks, with positive economic news becoming increasingly important in subsequent months.     

The choice of a foreign price measure in a Bayesian estimated new-Keynesian model for Israel

We estimate a small DSGE model by full information Bayesian techniques on the basis of Israeli data from 1995 to 2006. The model was first developed and estimated by means of classical GMM in Argov and Elkayam (2010), and since then it has been used at the Bank of Israel for monetary policy analysis. It is widely believed that in 2007 (out of sample year) as elsewhere worldwide, inflation rose in Israel due to high commodity prices in global markets. However, our baseline model attributes most of the high inflation in 2007 to supply shocks. One conjecture is that this model's result derives from the inappropriate original use of the unit value of imported consumer goods (which do not include unprocessed food and energy) as the main foreign price measure. We test this conjecture by re-estimating the model with various other foreign price measures that typically do reflect the global rise in commodity prices and compare the log-marginal likelihoods. We find that no other price measure outperforms the original choice in the sample period. Only the foreign trade-weighted CPI equals the performance of the original choice while improving the 2007 interpretation of inflation, and should therefore be considered the main foreign price measure. The proposed methodology for comparing the suitability of alternative measures for observable variables can be applied to any model with exogenous variables that are characterized by univariate equations.     

Testing for adjustment costs and regime shifts in BRENT crude futures market

This paper, using a threshold vector error-correction (TVECM) model, examines whether BRENT crude spot and futures oil prices are cointegrated. By employing this methodology we are able to evaluate the degree and dynamics of transaction costs resulting from various market imperfections. TVECM model is applied on daily spot and futures oil prices covering the period 1990-2009. The hypothesis we test is to what extent BRENT crude is indeed an integrated oil market in terms of threshold effects and adjustment costs. Our findings support that market follows a gradual integration path. We find that BRENT crude spot and futures are cointegrated, though two regimes are clearly identified. This implies that a threshold exists and it is indeed significant. Adjustment costs in the error correction are present, and they are valid at the typical regime that is the dominant, and as a result should not be ignored.     

Do disaggregated CPI data improve the accuracy of inflation forecasts?

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.     

Real-time data revisions and the PCE measure of inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.     

UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.     

How do central banks react to wealth composition and asset prices?

We assess the response of monetary policy to developments in asset markets in the euro area, the US and the UK. We estimate the reaction of monetary policy to wealth composition and asset prices using: (i) a linear framework based on a fully simultaneous system approach in a Bayesian environment; and (ii) a nonlinear specification that relies on a smooth transition regression model.     

Empirical determination of aggregate demand and supply curves: The example of the RWI Business Cycle Model

Macroeconometric models are often criticised for being too complex and difficult to read in theoretical terms. To overcome these difficulties, Hickman suggested the calculation of a model's implicit aggregate demand/supply (AD/AS) structure. The method helps to understand models and their main properties in theoretical terms and facilitates detailed model comparisons. This paper uses the AD/AS–IS/LM apparatus to analyse the simulation properties of the RWI (Rheinisch-Westfälisches Institut für Wirtschaftsforschung) Business Cycle Model, a medium-sized short-term macroeconometric model for Germany. The results confirm theoretical expectations for AD and AS elasticities and reveal particular reactions linked to peculiar model specifications such as an endogenous government sector. The results are also much in line with a previous study in a multi-country model context.     

Nonlinear interest rate reaction functions for the UK

We empirically analyze Taylor-type equations for short-term interest rates in the United Kingdom using quarterly data from 1970Q1 to 2006Q2. Starting from strong evidence against a simple linear Taylor rule, we model nonlinearities using logistic smooth transition regression (LSTR) models. The LSTR models with time-varying parameters consistently track actual interest rate movements better than a linear model with constant parameters. Our preferred LSTR model uses lagged interest rates as a transition variable and suggests that in times of recessions the Bank of England puts more weight on the output gap and less so on inflation. A reverse pattern is observed in non-recession periods. Parameters of the model change less frequently after 1992, when an inflation target range was announced. We conclude that for the analysis of historical monetary policy, the LSTR approach is a viable alternative to linear reaction functions.