Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?

Monitoring and forecasting price developments in the euro area is essential in the light of the second pillar of the ECB's monetary policy strategy. This study analyses whether the forecasting accuracy of forecasting aggregate euro area inflation can be improved by aggregating forecasts of subindices of the Harmonized Index of Consumer Prices (HICP) as opposed to forecasting the aggregate HICP directly. The analysis includes univariate and multivariate linear time series models and distinguishes between different forecast horizons, HICP components and inflation measures. Various model selection procedures are employed to select models for the aggregate and the disaggregate components. The results indicate that aggregating forecasts by component does not necessarily help forecast year-on-year inflation twelve months ahead. JEL Classification: E31, E37, C53, C32Euro Area Inflation, HICP subindex forecast aggregation, linear time series models

A Simple Test of the Effect of Interest Rate Defense

High interest rates to defend the exchange rate signal that a government is committed to fixed exchange rates, but may also signal weak fundamentals. We test the effectiveness of the interest rate defense by disaggregating into the effects on future interest rates differentials, expectations of future exchange rates, and risk premia. While much previous empirical work has been inconclusive due to offsetting effects, tests that "disaggregate" the effects provide significant information. Raising overnight interest rates strengthens the exchange rate over the short-term, but also leads to an expected depreciation at a horizon of a year and longer and an increase in the risk premium, consistent with the argument that it also signals weak fundamentals.

Trade consistency in the context of the Eurosystem projection exercises – an overview

The Eurosystem macroeconomic projection exercises are part of the input prepared for the Governing Council’s decision-making meetings. Under the economic analysis pillar of the ECB’s monetary policy strategy, they are a key element in the assessment of economic prospects and of the short to medium-term risks to price stability. The projection exercises are conducted on the basis of a number of “technical” assumptions. In particular, assumptions are made about future developments in world trade, foreign prices and nominal exchange rates. The purpose of the trade consistency exercise (TCE) is to ensure that individual country forecasts are consistent with each other regarding the assumptions made about the international environment. Trade consistency is ensured in two directions: first, the cross-trade consistency part of the TCE involves examining the consistency of the trade projections at any given point in time; and second, the ex ante/ex post trade consistency part involves comparing the projections for a given variable across different projection rounds. This paper provides a comprehensive description of the data and techniques underlying the trade consistency exercises in the context of the projection exercises of the Eurosystem and the ECB. JEL Classification: C23, D92, E22, E52, G31, G32competitiveness, cross-country consistency, market shares, Trade projections

Forecasting economic aggregates by disaggregates

We suggest an alternative use of disaggregate information to forecast the aggregate variable of interest, that is to include disaggregate information or disaggregate variables in the aggregate model as opposed to first forecasting the disaggregate variables separately and then aggregating those forecasts or, alternatively, using only lagged aggregate information in forecasting the aggregate. We show theoretically that the first method of forecasting the aggregate should outperform the alternative methods in population. We investigate whether this theoretical prediction can explain our empirical findings and analyse why forecasting the aggregate using information on its disaggregate components improves forecast accuracy of the aggregate forecast of euro area and US inflation in some situations, but not in others. JEL Classification: C51, C53, E31Disaggregate information, Factor models, forecast model selection, Predictability, VAR

Delusional Belief induced by clomiphene treatment

We report the case of a 35 year old women who developed a complex paranoid delusion during the course of clomiphene treatment für ovulation. The psychopathology was remarkable, because after a short hypomanic period the patient was without severe cognitive disturbance but struggled with a complex monothematic delusion. The delusion vanished in the course of a combination treatment with olanzapine a cognitive behavioral therapy. We could not entirely rule out the possibility of an endogenous psychiatric disease but nevertheless we encountered an unusual monothematic delusion which showed a strong temporal correlation with the intake of clomiphene. We provide some speculation about underlying neurobiological mechanisms on the basis of the dopamine theory of delusion

Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate

To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecastorigin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data. JEL Classification: C51, C53, E31Aggregate forecasts, Disaggregate information, forecast combination, inflation

Forecast evaluation of small nested model sets

We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the bench-mark to all the alternative models simultaneously rather than sequentially, and do not require re-estimation of models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different models for U.S. inflation. JEL Classification: C32, C53, E37Inflation forecasting, multiple model comparisons, Out-of-Sample, prediction, testing

On the importance of sectoral shocks for price-setting

We use a novel disaggregate sectoral euro area dataset with a regional breakdown that allows explicit estimation of the sectoral component of price changes (rather than interpreting the idiosyncratic component as sectoral as done in other papers). Employing a new method to extract factors from over-lapping data blocks, we find for our euro area data set that the sectoral component explains much less of the variation in sectoral regional inflation rates and exhibits much less volatility than previous findings for the US indicate. Country- and region-specific factors play an important role in addition to the sector-specific factors. We conclude that sectoral price changes have a “geographical” dimension, as yet unexplored in the literature, that might lead to new insights regarding the properties of sectoral price changes

Forecasting inflation with gradual regime shifts and exogenous information

We propose a new method for medium-term forecasting using exogenous information. We first show how a shifting-mean autoregressive model can be used to describe characteristic features in inflation series. This implies that we decompose the inflation process into a slowly moving nonstationary component and dynamic short-run fluctuations around it. An important feature of our model is that it provides a way of combining the information in the sample and exogenous information about the quantity to be forecast. This makes it possible to form a single model-based inflation forecast that also incorporates the exogenous information. We demonstrate, both theoretically and by simulations, how this is done by using the penalised likelihood for estimating the model parameters. In forecasting inflation, the central bank inflation target, if it exists, is a natural example of such exogenous information. We illustrate the application of our method by an out-of-sample forecasting experiment for euro area and UK inflation. We find that for euro area inflation taking the exogenous information into account improves the forecasting accuracy compared to that of a number of relevant benchmark models but this is not so for the UK. Explanations to these outcomes are discussed. JEL Classification: C22, C52, C53, E31, E47Nonlinear forecast, nonlinear model, nonlinear trend, penalised likelihood, structural shift, time-varying parameter