WORKING PAPER SERIES No 56 / 2018
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WORKING PAPER SERIES No 56 / 2018 Slicing up inflation: analysis and forecasting of Lithuanian inflation components EXERCISE EVALUATION FORECAST REAL-TIME PSEUDO A DATASETS: MONTHLY LARGE USING GDP OF FORECASTING SHORT-TERM No 1 / 2008 By Julius Stakėnas BANK OF LITHUANIA. WORKING PAPER SERIES PAPER WORKING LITHUANIA. OF BANK 1 ISSN 2029-0446 (ONLINE) WORKING PAPER SERIES No 56 / 2018 SLICING UP INFLATION: ANALYSIS AND FORECASTING OF LITHUANIAN INFLATION COMPONENTS* Julius Stakėnas† * We are grateful for the participants of the internal Bank of Lithuania seminars for their helpful comments. The views expressed and the conclusions reached in this publication are those of the author and do not necessarily represent the official views of the Bank of Lithuania or the Eurosystem. † Bank of Lithuania, e.a.: [email protected]. © Lietuvos bankas, 2018 Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged. Address Totorių g. 4 LT-01121 Vilnius Lithuania Telephone (8 5) 268 0103 Internet http://www.lb.lt Working Papers describe research in progress by the author(s) and are published to stimulate discussion and critical comments. The Series is managed by Applied Macroeconomic Research Division of Economics Department and Center for Excellence in Finance and Economic Research. All Working Papers of the Series are refereed by internal and external experts. The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania. ISSN 2029-0446 (ONLINE) Abstract In this paper we model five Lithuanian HICP subcomponents in a medium scale Bayesian VAR framework. We deal with the parameter proliferation problem by setting the appropri- ate amount of shrinkage determined in the out-of-sample forecasting exercise. The main body of the paper consists of displaying the model's performance in two applications: forecasting and analysis of inflation determinants. We find the model's forecasts to be competitive against the univariate statistical models, particularly in the cases of predicting processed food and energy goods inflation. What is more, exercises based on conditional forecasting show that these two indices make the best use of accurate conditional information in terms of improving predicting accuracy. In the decomposition of the drivers of HICP components, we demonstrate that both, domestic and foreign factors can be prevalent inflation determinants in certain time periods. We also find some evidence on employees' bargaining power playing a role in determining the Lithuanian consumer price inflation. Keywords: HICP subindices, Bayesian VAR, Bayesian shrinkage, inflation forecasting, structural decomposition JEL classification: C32, C53, E37 4 1 Introduction Monitoring and forecasting inflation is one of the primary interests of any inflation-targeting central bank, government concerned about its citizens' income/wealth (re)distribution (as well as tax collection), or any economic agent basing his/her consumption and investment decisions on inflation outcomes. As inflation determinants vary over time, it is instructive to go past monitoring just one measure of inflation and study its constituent parts in order to better understand its causes and persistence. In this paper we use a Bayesian VAR (BVAR) model to study inflation of 5 main HICP components: unprocessed food (UF), processed food (PF), services (SERV), non-energy industrial goods (NEIG) and energy goods (ENERG). The choice of modelling the specific 5 HICP components was primarily motivated by the ECB's requirement for the Bank of Lithuania to provide forecasts of these price indices. On the other hand, the model is not reliant on the particular disaggregation scheme of consumer prices and can be straightforwardly adjusted to incorporate a different number of HICP subindices of various definitions. The benefit of modelling the HICP prices on a rather disaggregated level lies, firstly, in the ability to study prices that have quite different determinants separately (modelling standpoint), and secondly, in the ability to address concerns of policy makers and consumer groups regarding the price dynamics in a more detailed way (consumer standpoint). We believe, that our chosen set of 5 HICP indices serves well in achieving these goals, while at the same time keeping the level of aggregation high enough to justify the macroeconomic viewpoint. Note as well, that the HICP subindices used in the study are provided by the Eurostat for all European Union countries, making potential cross-country comparative analysis much easier. The direct application of our research results is closely linked (but not limited) to the Narrow Inflation Projection Exercise (NIPE) performed by the central banks in the Eurosystem. In this exercise, central banks provide the ECB with short-term forecasts of 5 HICP components, which then are aggregated at the euro area level. Our paper is motivated by the work of Giannone et al. (2010), who used a BVAR model to perform this exercise for the euro area data. In their study, they list the apparent advantages of modelling HICP components in a single framework: availability of all possible interactions between the HICP components, ability to capture second- round effects (i.e. impact of assumptions on the future values of variables these assumptions are set for), easy scenario analysis (availability of incomplete conditioning, consistent inclusion of expert judgement), model-based risk assessment around the projections, etc. All these potential uses and applications motivated our choice of a VAR model (over a framework of a set of univariate equations) for analysis and forecasting of HICP subindices. The Lithuanian HICP component forecasts were already studied in Stak_enas(2015), where the forecasts were generated on a rather disaggregate level with 44 univariate equations. The paper concluded that forecasting HICP components on a disaggregate level and later aggregating the forecasts, produces predictions that are hard to beat regarding their accuracy, however, it also implies that the there are no spillovers between the HICP components and any scenario analysis becomes quite restrictive. In this paper, our objective is twofold { we aim not only for forecasting accuracy of the 5 HICP components, but also for their structural interpretation. We are interested in identifying the drivers of Lithuanian HICP components, their origin (global vs. local), potential dependence on labour market conditions, differences in components' factors, etc. Finally, multivariate modelling should also allow us to study interactions/spillovers between the components before making any restrictions. The flexibility of the model, allowing for interactions between the HICP subindices and de- pendence on a number of different determinants, comes at a cost of parameter proliferation and Bayesian shrinkage presents itself as a natural candidate to counter its adverse effects. As 5 demonstrated by De Mol et al. (2008), Bayesian regression can be a valid alternative to principal components { the authors find that using a normal prior distribution, Bayesian regression gen- erates forecasts that are highly correlated with principal component forecasts. The authors also show that in case of growing number of parameters, coefficients have to be increasingly shrunk towards zero in order to obtain consistent forecasts. Banbura et al. (2010) used this result to estimate high-dimensional VAR models (up to 131 variables) and found that, when the degree of shrinkage is set in relation to cross-sectional dimension of the data, forecasts can be improved by adding more variables. The results of our paper link to the literature in three directions. First, we contribute to the Bayesian hyperparameter selection literature, studying it in the small open economy setting with a focus on inflation dynamics. We find that in this setting, the parameter shrinkage schemes suggested by Litterman (1985) are applicable, i.e. more distant lags and cross-variable lags ought to be shrunk more. We also reiterate the results by De Mol et al. (2008), observing how high forecast RMSE (root mean squared error), that was induced by parameter proliferation, can be lowered by applying appropriate amount of shrinkage. The out-of-sample forecasting part of the paper relates to the studies on the forecasting performance of BVAR models, such as the already-mentioned Giannone et al. (2010) and Banbura et al. (2010), who find BVAR models to produce competitive forecasts (see also Karlsson (2013) for the extensive review on this strand of literature). While our benchmark model specifications are rather standard, we also test some more recently suggested specifications using stochastic search variable selection to allow for more heterogeneity across the equations. Lastly, our paper relates to the studies on business cycle drivers in a small open economy, attempting to find the balance between the findings that inflation is largely (and increasingly) a global phenomenon (see e.g. studies by Ciccarelli and Mojon (2010), Mumtaz and Surico (2012)) and the research that finds little support for the globalisation hypothesis (see e.g. the works by Calza (2009), Ihrig et al. (2010)). The main contribution of our paper, in our view, lies in the disaggregate analysis of inflation dynamics in a small open economy, while at the same time retaining consistent and transparent treatment of the data. This allows us to raise a number of questions which would not be possible in an aggregate inflation analysis.