Inflation forecasts using the tips yield curve

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Improving accuracy of inflation forecasts using macroeconomic information

This thesis examines the effects of macroeconomic factors on inflation level and volatility in the Euro Area to improve the accuracy of inflation forecasts with econometric modelling. Inflation aggregates for the EU as well as inflation levels of selected countries are analysed, and the difference between these inflation estimates and forecasts are documented. The research proposes alternative models depending on the focus and the scope of inflation forecasts. I find that models with a Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) in mean process have better explanatory power for inflation variance compared to the regular GARCH models. The significant coefficients are different in EU countries in comparison to the aggregate EU-wide forecast of inflation. The presence of more pronounced GARCH components in certain countries with more stressed economies indicates that inflation volatility in these countries are likely to occur as a result of the stressed economy. In addition, other economies in the Euro Area are found to exhibit a relatively stable variance of inflation over time. Therefore, when analysing EU inflation one have to take into consideration the large differences on country level and focus on those one by one.

Quantile forecasts of inflation under model uncertainty

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for different predictors to affect different quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future inflation by providing superior predictive densities compared to mean regression models with and without BMA.

Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set

We evaluate conditional predictive densities for U.S. output growth and inflationusing a number of commonly used forecasting models that rely on a large number ofmacroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly used normality assumption fit actual realizationsout-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can improve or deteriorate point forecasts, they might have theopposite effect on higher moments. We find that normality is rejected for most modelsin some dimension according to at least one of the tests we use. Interestingly, however,combinations of predictive densities appear to be correctly approximated by a normaldensity: the simple, equal average when predicting output growth and Bayesian modelaverage when predicting inflation.

Early warning with calibrated and sharper probabilistic forecasts

Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In order to enhance the quality of the density forecasts, one can mix them with the unconditional density. This paper examines the value of combining conditional density forecasts with the unconditional density. The findings have positive implications for issuing early warnings in different disciplines including economics and meteorology, but UK inflation forecasts are considered as an example.

Do US macroeconomic forecasters exaggerate their differences?

Application of the Bernhardt et al. (Journal of Financial Economics 2006; 80(3): 657–675) test of herding to the calendar-year annual output growth and inflation forecasts suggests forecasters tend to exaggerate their differences, except at the shortest horizon, when they tend to herd. We consider whether these types of behaviour can help to explain the puzzle that professional forecasters sometimes make point predictions and histogram forecasts which are mutually inconsistent.

Expectativas de inflação sob o regime de metas no Brasil

Esta dissertação faz uma avaliação das expectativas de inflação dos analistas profissionais utilizadas pelo Banco Central na sua formulação de política monetária. Usando o procedimento proposto por Thomas Jr (1999) constatamos que as previsões, extraídas por meio de entrevistas juntos aos analistas financeiros, publicadas no Boletim Focus, são viesadas e inconsistentes, portanto incapazes de antecipar corretamente movimentos futuros da inflação. Alguns trabalhos, como Estrella e Mishkin (1997), Kozicki (1997) e Kotlan (1999) utilizaram com bons resultados a inclinação da estrutura a termo da taxa de juros (ETTJ) para prever variações da inflação. Adaptamos estes modelos para o Brasil e obtivemos resultados significantes nos horizontes de curto e médio prazos, mostrando que a inclinação da ETTJ pode contribuir para a política de metas de inflação do Banco Central. No entanto, como o Brasil ainda possui uma estrutura a termo muito curta a capacidade de previsão do modelo não vai além de 9 meses. Com a estabilização da economia, se espera que esta curva se alongue, tornando este instrumento de previsão cada vez mais poderoso.

Os impactos da comunicação do Banco Central no mercado brasileiro : o choque de notícias

Este trabalho tem como objetivo avaliar o modo que a comunicação do Banco Central do Brasil (comunicados das decisões de política monetária, atas do COPOM e relatórios de inflação) pode impactar os mercados - a reação do mercado financeiro às notícias da autoridade monetária. Nesse sentido, atenta-se para a diferença entre o que se espera que seja a informação e o que de fato a informação é: o choque de notícias. A partir dessa diferença entre a expectativa e o realizado, procura-se analisar o quanto tal desvio é relevante para as variações nos preços de alguns ativos. Encontra-se evidências de que os comunicados das decisões parecem ser bastante eficientes enquanto informantes do futuro da política monetária, o que não acontece para as atas. Ao analisarmos a interação entre comunicados e atas a partir de 2003, vemos que há uma complementaridade dos dois veículos, com o choque de notícias dos comunicados tendo mais impactos sobre maturidades de juros mais curtas e o choque das atas sobre os vértices mais longos. Por fim, as projeções de inflação dos relatórios parecem ser relevantes para movimentar a curva de juros futuros em diversos pontos.

Breaks in the UK household sector money demand function

We use non-parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P-star models are also estimated for out-of-sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P-star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.

Evaluating the Bank of England density forecasts of inflation

We consider evaluating the UK Monetary Policy Committee's inflation density forecasts using probability integral transform goodness-of-fit tests. These tests evaluate the whole forecast density. We also consider whether the probabilities assigned to inflation being in certain ranges are well calibrated, where the ranges are chosen to be those of particular relevance to the MPC, given its remit of maintaining inflation rates in a band around per annum. Finally, we discuss the decision-based approach to forecast evaluation in relation to the MPC forecasts

Evaluating the Survey of Professional Forecasters probability distributions of expected inflation based on derived event probability forecasts

Techniques are proposed for evaluating forecast probabilities of events. The tools are especially useful when, as in the case of the Survey of Professional Forecasters (SPF) expected probability distributions of inflation, recourse cannot be made to the method of construction in the evaluation of the forecasts. The tests of efficiency and conditional efficiency are applied to the forecast probabilities of events of interest derived from the SPF distributions, and supplement a whole-density evaluation of the SPF distributions based on the probability integral transform approach.

Probability distributions or point predictions? Survey forecasts of US output growth and inflation

We consider whether survey respondents’ probability distributions, reported as histograms, provide reliable and coherent point predictions, when viewed through the lens of a Bayesian learning model. We argue that a role remains for eliciting directly-reported point predictions in surveys of professional forecasters.