Forecast revisions of Mexican inflation and GDP growth

We analyze the forecasts of inflation and GDP growth contained in the Banco de México’s Survey of Professional Forecasters for the period 1995–2009. The forecasts are for the current and the following year, and comprise an unbalanced three-dimensional panel with multiple individual forecasters, target years, and forecast horizons. The fixed-event nature of the forecasts enables us to examine their efficiency by looking at the revision process. The panel structure allows us to control for aggregate shocks and to construct a measure of the news that impacted expectations in the period under study. We find that respondents anchor to their initial forecasts, updating their revisions smoothly as they receive more information. In addition, they do not seem to use publicly-known information in an efficient manner. These inefficiencies suggest clear areas of opportunity for improving the accuracy of the forecasts, for instance by taking into account the positive autocorrelation found in forecast revisions.     

Understanding survey-based inflation expectations

This paper examines the behavior of inflation expectations in the United States. After documenting deviations from rationality in survey-based inflation expectations, I apply a model selection algorithm, boosting, to the inflation expectations of households and professionals. The algorithm builds a regression-like model of expected inflation using a large panel of macroeconomic data as possible covariates. The algorithm achieves a very strong fit in-sample, and finds that the inflation expectations of households correlate with different macroeconomic variables from the expectations of professionals. However, it is difficult to exploit the predictability of inflation expectations in order to improve forecasts of the realized inflation.     

New tips from TIPS: Identifying inflation expectations and the risk premia of break-even inflation

This paper decomposes the break-even inflation rates derived from inflation-indexed bonds into inflation risk premia, liquidity risk premia, and inflation expectations. I estimate a common factor model with autoregressive conditionally heteroscedastic (ARCH) errors that extracts co-movements from twenty-two monthly and quarterly indicators to identify these three components. The results indicate that the sharp declines in the 10-year and 5-year break-even inflation rates in 2009 reflect a substantial increase in liquidity risk rather than a decrease in inflation expectations. Break-even inflation rates underestimate inflation expectations over nearly the entire sample due to the liquidity risk premia carried by the inflation indexed bond yields. Also, the model-implied inflation expectations show better forecast performance for the average annual inflation rates than raw break-even inflation rates, the Survey of Professional Forecasters, and the Surveys of Consumers inflation forecasts.     

Central bank’s forecasts and lack of transparency: An assessment of the effect on private expectations in a large emerging economy

We analyze whether the Central Bank of Brazil’s Inflation Reports projections influences the private’s inflation expectations. Specifically, we investigate how the central bank’s inflation forecasts affect the private sector’s inflation expectations through a qualitative and quantitative examination of the disagreement measure between them. Furthermore, we appraise if the lack of transparency resulting from the difference between the central bank’s inflation forecasts and the realized inflation affects the private’s inflation expectations. Although the findings confirm the previous studies that point out that the central bank transparency can affect the readjustment of market expectations, the results do not rule out the possibility of the central bank’s forecast and private’s inflation expectations being affected reciprocally.     

The COVID-19 shock and challenges for inflation modelling

We document the impact of COVID-19 on inflation modelling within a vector autoregression (VAR) model and provide guidance for forecasting euro area inflation during the pandemic. We show that estimated parameters are strongly affected, leading to different and sometimes implausible projections. As a solution, we propose to augment the VAR by allowing the residuals to have a fat-tailed distribution instead of a Gaussian one. This also outperforms with respect to unconditional forecasts. Yet, what brings sizeable forecast gains during the pandemic is adding meaningful off-model information, such as that entailed in the Survey of Professional Forecasters. The fat-tailed VAR loses part, but not all of its relative advantage compared to the Gaussian version when producing conditional inflation forecasts in a real-time setup. It is the joint fat-tailed errors and multi-equation modelling that manage to robustify models against extreme observations; in a single-equation model the same solution is less effective.     

Inflation perceptions and expectations in Sweden – Are media reports the missing link?

We analyze the interrelation between consumers' quantitative inflation perceptions and expectations as well as actual inflation rates in Sweden. The role of media reports about inflation is emphasized. Structural vector error correction models show stable cointegration between actual, perceived and expected inflation. Impulse responses and forecast error variance decompositions suggest strong interaction between perceived and expected inflation, with a lesser role for actual inflation. Media effects are generally small, but imply an asymmetric reaction of inflation expectations and perceptions to news on increasing vs. decreasing inflation. Thus, to anchor inflation expectations, central banks should explore better communication channels to inform consumers about actual inflation.     

Fitting observed inflation expectations

The paper provides evidence on the extent to which inflation expectations generated by a standard Christiano et al. (2005)/Smets and Wouters (2003)-type DSGE model are in line with what observed in the data. We consider three variants of this model that differ in terms of the behavior of, and the public's information on, the central banks' inflation target, allegedly a key determinant of inflation expectations. We find that (i) time-variation in the inflation target is needed to capture the evolution of expectations during the post-Volcker period; (ii) the variant where agents have Imperfect Information is strongly rejected by the data; (iii) inflation expectations appear to contain information that is not present in the other series used in estimation, and (iv) none of the models fully capture the dynamics of this variable.     

Learning,disagreement and inflation forecasting

This paper studies inflation forecasting based on the Bayesian learning algorithm which simultaneously learns about parameters and state variables. The Bayesian learning method updates posterior beliefs with accumulating information from inflation and disagreement about expected inflation from the Survey of Professional Forecasters (SPF). The empirical results show that Bayesian learning helps refine inflation forecasts at all horizons over time. Incorporating a Student’s t innovation improves the accuracy of long-term inflation forecasts. Including disagreement has an effect on refining short-term inflation density forecasts. Furthermore, there is strong evidence supporting a positive correlation between disagreement and trend inflation uncertainty. Our findings are helpful for policymakers when they forecast the future and make forward-looking decisions.     

Kernel-based calibration diagnostics for recession and inflation probability forecasts

A probabilistic forecast is the estimated probability with which a future event will occur. One interesting feature of such forecasts is their calibration, or the match between the predicted probabilities and the actual outcome probabilities. Calibration has been evaluated in the past by grouping probability forecasts into discrete categories. We show here that we can do this without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating the predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on the calibration error in a number of economic applications, including the prediction of recessions and inflation, using both forecasts made and stored in real time and pseudo-forecasts made using the data vintage available at the forecast date. The outcomes are evaluated using both first-release outcome measures and subsequent revised data. We find substantial evidence of incorrect calibration in professional forecasts of recessions and inflation from the SPF, as well as in real-time inflation forecasts from a variety of output gap models.     

Context effects in inflation surveys: The influence of additional information and prior questions

Context effects are known to affect responses to surveys. We report effects of information and task contexts in surveys of inflation expectations. Information context refers to contextual information about earlier inflation rates or other economic indicators. Task context refers to judgement tasks performed prior to the inflation judgement task under consideration. In three experiments, we show that contextual information improves judgement accuracy. As this information is given in expert, but not in lay surveys, its provision may partly explain why expert judgements are superior to those of lay people. In both expert and lay surveys, respondents make inflation judgements in the context of already having made other inflation judgements. We show that when different groups of people make inflation judgements either for the current or for the upcoming year, their judgements do not differ. However, when the same people make judgements for both the current and the upcoming years, the latter are significantly higher than the former, perhaps because people expect inflation to increase over time.     

Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts

If ‘learning by doing’ is important for macro-forecasting, newcomers might be different from regular, established participants. Stayers may also differ from the soon-to-leave. We test these conjectures for macro-forecasters’ point predictions of output growth and inflation, and for their histogram forecasts. Histogram forecasts of inflation by both joiners and leavers are found to be less accurate, especially if we suppose that joiners take time to learn. For GDP growth, there is no evidence of differences between the groups in terms of histogram forecast accuracy, although GDP point forecasts by leavers are less accurate. These findings are predicated on forecasters being homogeneous within groups. Allowing for individual fixed effects suggests fewer differences, including leavers’ inflation histogram forecasts being no less accurate.     

Professional forecasters and real-time forecasting with a DSGE model

This paper analyses the real-time forecasting performance of the New Keynesian DSGE model of Galí, Smets and Wouters (2012), estimated on euro area data. It investigates the extent to which the inclusion of forecasts of inflation, GDP growth and unemployment by professional forecasters improve the forecasting performance. We consider two approaches for conditioning on such information. Under the “noise” approach, the mean professional forecasts are assumed to be noisy indicators of the rational expectations forecasts implied by the DSGE model. Under the “news” approach, it is assumed that the forecasts reveal the presence of expected future structural shocks in line with those estimated in the past. The forecasts of the DSGE model are compared with those from a Bayesian VAR model, an AR(1) model, a sample mean and a random walk.     

Experimental evidence on inflation expectation formation

Using laboratory experiments within a New Keynesian sticky price framework, we study the process of inflation expectation formation. We focus on adaptive learning and rational expectations contrary to the previous literature that mostly studied simple heuristics. Using a test for rational expectations that allows heterogeneity of expectations we find that we cannot reject rationality for about 40% of subjects. More than 20% of subjects are also best described by adaptive learning models, where they behave like econometricians and update their model estimates every period. However, rather than using a single forecasting model, switching between models describes their behavior better. Switching is more likely to occur when experimental economy is in a recession.     

Forecasting with news sentiment: Evidence with UK newspapers

We investigate the performance of newspapers for forecasting inflation, output and unemployment in the United Kingdom. We concentrate on whether the economic policy content reported in popular printed media can improve on existing point forecasts. We find no evidence supporting improved nowcasts or short-term forecasts for inflation. The sentiment inferred from printed media, can however be useful for forecasting unemployment and output. Considerable improvements are also noted when using individual newspapers and keyword based indices.     

Forecasting inflation with online prices

Are survey-based forecasts unbeatable? They are not. This paper uses online price indices to forecast the Consumer Price Index. We find that online price indices anticipate changes in official inflation trends more than one month in advance. Our baseline one-month forecast outperforms Bloomberg surveys of forecasters, which only predict the contemporaneous inflation rate. Our baseline specification also outperforms statistical benchmark forecasts for Australia, Canada, France, Germany, Greece, Ireland, Italy, the Netherlands, the United Kingdom, and the United States. Similarly, our quarterly forecast for the US inflation rate substantially outperforms the Survey of Professional Forecasters.     

Inversion copulas from nonlinear state space models with an application to inflation forecasting

We propose the construction of copulas through the inversion of nonlinear state space models. These copulas allow for new time series models that have the same serial dependence structure as a state space model, but with an arbitrary marginal distribution, and flexible density forecasts. We examine the time series properties of the copulas, outline serial dependence measures, and estimate the models using likelihood-based methods. Copulas constructed from three example state space models are considered: a stochastic volatility model with an unobserved component, a Markov switching autoregression, and a Gaussian linear unobserved component model. We show that all three inversion copulas with flexible margins improve the fit and density forecasts of quarterly U.S. broad inflation and electricity inflation.     

Forecasting inflation in Latin America with core measures

We explore the ability of core inflation to predict headline CPI annual inflation for a sample of eight developing economies in Latin America over the period January 1995–May 2017. Our in-sample and out-of-sample results are roughly consistent in providing robust evidence of predictability in four of the countries in our sample. Mixed evidence is found for the other four countries. The bulk of the out-of-sample evidence of predictability concentrates on the short horizons of one and six months. In contrast, at the longest horizon of 24 months, we only find out-of-sample evidence of predictability for two countries: Chile and Colombia, with robust results only for the latter. This is both important and challenging, given that the monetary authorities in our sample of developing countries are currently implementing or are taking steps toward the future implementation of inflation targeting regimes, which are based heavily on long-run inflation forecasts.     

Epidemiology of inflation expectations and internet search: an analysis for India

Journal of Economic Interaction and Coordination - This paper investigates how inflation expectations of individuals are formed in India. We investigate if the news on inflation plays a role in the...     

Inflation forecast contracts

We introduce a new type of incentive contract for central bankers: inflation forecast contracts, which make central bankers׳ remunerations contingent on the precision of their inflation forecasts. We show that such contracts enable central bankers to influence inflation expectations more effectively, thus facilitating more successful stabilization of current inflation. Inflation forecast contracts improve the accuracy of inflation forecasts, but have adverse consequences for output. On balance, paying central bankers according to their forecasting performance improves welfare. Optimal inflation forecast contracts stipulate high rewards for accurate forecasts.     

The (de-)anchoring of inflation expectations: New evidence from the euro area

Well-anchored inflation expectations are a key factor for achieving economic stability. This paper provides new empirical results on the anchoring of long-term inflation expectations in the euro area. In line with earlier evidence, we find that euro area inflation expectations have been anchored until fall 2011. Since then, however, they respond significantly to macroeconomic news. Our results obtained from multiple endogenous break point tests suggest that euro area inflation expectations have remained de-anchored ever since.