Nowcasting food inflation with a massive amount of online prices

The consensus in the literature on providing accurate inflation forecasts underlines the importance of precise nowcasts. In this paper, we focus on this issue by employing a unique, extensive dataset of online food and non-alcoholic beverages prices gathered automatically from the webpages of major online retailers in Poland since 2009. We perform a real-time nowcasting experiment by using a highly disaggregated framework among popular, simple univariate approaches. We demonstrate that pure estimates of online price changes are already effective in nowcasting food inflation, but accounting for online food prices in a simple, recursively optimized model delivers further gains in the nowcast accuracy. Our framework outperforms various other approaches, including judgmental methods, traditional benchmarks, and model combinations. After the outbreak of the COVID-19 pandemic, its nowcasting quality has improved compared to other approaches and remained comparable with judgmental nowcasts. We also show that nowcast accuracy increases with the volume of online data, but their quality and relevance are essential for providing accurate in-sample fit and out-of-sample nowcasts. We conclude that online prices can markedly aid the decision-making process at central banks.     

Does Output Gap,Labour's Share or Unemployment Rate Drive Inflation?

We propose a new methodology for ranking in probability the commonly proposed drivers of inflation in the new Keynesian model. The approach is based on Bayesian model selection among restricted vector autoregressive (VAR) models, each of which embodies only one or none of the candidate variables as the driver. Simulation experiments suggest that our procedure is superior to the previously used conventional pairwise Granger causality tests in detecting the true driver. Empirical results lend little support to labour share, output gap or unemployment rate as the driver of US inflation.     

The accuracy of IMF crises nowcasts

The International Monetary Fund (IMF) provides loans to countries in economic crises as a lender of last resort. IMF loan approvals are tied to policy reforms and quantitative targets that reflect the IMF’s crisis assessment. An extensive literature scrutinizes the efficacy of IMF loan programs, instead, we examine the accuracy of the IMF’s crisis assessments (nowcasts) that predicate program designs. Analyzing an unprecedented 602 IMF loan programs from 1992 to 2019, we contradict previous findings that IMF nowcasts are generally optimistic. Disentangling the structure of the IMF’s nowcast bias, we find the IMF systematically overestimates high-growth recoveries GDPs, while low-growth recoveries for low-income countries (LICs) are underestimated. In contrast, non-LICs’ nowcasts exhibit no statistically significant optimistic and pessimistic bias. Interestingly, shorter nowcast horizons do not improve accuracy, and GDP growth nowcasts improved substantially since 2013, while inflation nowcasts remain inefficient. We also isolate the sources of IMF nowcast inefficiencies according to ((i) program objectives, ((ii) program conditionality type, ((iii) geographic regions, ((iv) global crises, and ((v) geopolitics (elections, conflicts, and disasters).     

Inflation Uncertainty, Output Growth Uncertainty and Macroeconomic Performance

We use a bivariate generalized autoregressive conditionally heteroskedastic (GARCH) model of inflation and output growth to examine the causality relationship among nominal uncertainty, real uncertainty and macroeconomic performance measured by the inflation and output growth rates. The application of the constant conditional correlation GARCH(1,1) model leads to a number of interesting conclusions. First, inflation does cause negative welfare effects, both directly and indirectly, i.e. via the inflation uncertainty channel. Secondly, in some countries, more inflation uncertainty provides an incentive to Central Banks to surprise the public by raising inflation unexpectedly. Thirdly, in contrast to the assumptions of some macroeconomic models, business cycle variability and the rate of economic growth are related. More variability in the business cycle leads to more output growth.     

ARE EMPIRICAL MEASURES OF MACROECONOMIC UNCERTAINTY ALIKE?

Abstract There is a plethora of time series measures of uncertainty for inflation and real output growth in empirical studies but little is known whether they are comparable to the uncertainty measure reported by individual forecasters in the survey of professional forecasters. Are these two measures of uncertainty inherently distinct? This paper shows that, compared with many uncertainty proxies produced by time series models, the use of real‐time data with fixed‐sample recursive estimation of an asymmetric bivariate generalized autoregressive conditional heteroskedasticity model yields inflation uncertainty estimates which resemble the survey measure. There is, however, overwhelming evidence that many of the time series measures of growth uncertainty exceed the level of uncertainty obtained from survey measure. Our results highlight the relative merits of using different methods in modelling macroeconomic uncertainty which are useful for empirical researchers.     

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.     

Nowcasting with payments system data

We consider the potential usefulness of a large set of electronic payments data, comprising the values and numbers of both debit card transactions and cheques that clear through the banking system, for the problem of reducing the current-period forecast (‘nowcast’) loss for (the growth rates of) GDP and retail sales. The payments system variables capture a broad range of spending activity and are available on a very timely basis, making them suitable current indicators. We generate nowcasts of GDP and retail sales growth for a given month on seven different dates, over a period of two and a half months preceding the first official releases, which is the period over which nowcasts would be of interest. We find statistically significant evidence that payments system data can reduce the nowcast error for both GDP and retail sales growth. Both debit transaction and cheque clearance data are of value in reducing nowcast losses for GDP growth, although the latter are of little or no value when debit data are also included. For retail sales, cheque data appear to produce no further nowcast loss reductions, regardless of whether or not debit transactions are included in the nowcasting model.     

The relationship between forecast dispersion and forecast uncertainty: Evidence from a survey data—arch model

This paper examines empirically the relationship between measures of forecast dispersion and forecast uncertainty from data on inflation expectations from the Livingston survey series and the Survey Research Center (SRC) survey series. Because the survey series do not provide probabilistic forecasts of inflation, we derive measures of inflation uncertainty by modelling the conditional variance of the inflation forecast errors from the survey series as an autoregressive conditional heteroscedastic (ARCH) process. The analysis is complicated by the fact that the overlap of forecast horizons for the survey series does not preclude the model's disturbance terms from displaying autocorrelation, and also places a restriction on the specification for the ARCH measures of inflation uncertainty. We estimate the model using Hansen's (1982) generalized method of moments (GMM) procedure to account for the presence of serial correlation and conditional heteroscedasticity in the disturbance terms. The results generally support the hypothesis that the measures of forecast dispersion across survey respondents are positively and statistically significantly associated with the measures of inflation uncertainty. However, the appropriateness of using forecast dispersion measures as proxies for inflation uncertainty is sensitive to the choice of the survey series.     

Evaluating Point and Density Forecasts of DSGE Models

This paper investigates the accuracy of forecasts from four dynamic stochastic general equilibrium (DSGE) models for inflation, output growth and the federal funds rate using a real‐time dataset synchronized with the Fed's Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation are the model forecasts dominated by the Greenbook projections. A comparison with forecasts from Bayesian vector autoregressions shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting with Bayesian Vector Autoregressions Estimated Using Professional Forecasts

We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short‐term survey forecasts as an additional source of information about model parameters. In particular, we augment the vector of dependent variables by their survey nowcasts, and claim that each variable modelled in the VAR and its nowcast are likely to depend in a similar way on the lagged dependent variables. In an application to macroeconomic data, we find that the forecasts obtained from a VAR fitted by our new shrinkage approach typically yield smaller mean squared forecast errors than the forecasts obtained from a range of benchmark methods. Copyright © 2015 John Wiley & Sons, Ltd.     

The time-varying correlation between output and prices in the United States over the period 1800–2014

In this study, we examine the time-varying correlations between output and prices, while controlling for the impact of the monetary policy stance and output and inflation uncertainties over the period 1800–2014. The results of the empirical analysis reveal that the dynamic correlations of output and prices were typically negative, suggesting a countercyclical behaviour of prices, apart from the early 1840s and from the beginning until the middle of the 20th century, when the correlation was positive, indicating a procyclicality of prices. A historical decomposition analysis based on a sign-restricted structural vector autoregressive model is able to relate the procyclical and countercyclical behaviour to the predominance of aggregate supply and aggregate demand and/or monetary policy shocks, respectively. Moreover, inflation uncertainty (monetary policy stance) was found to have a positive (negative) effect on inflation over the last 215 years.     

Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis

We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed-frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track using a specific form of intercept correction. Among these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and subsequent recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment, and very persistent negative effects on trend growth. Similar findings also emerge for forecasts by institutions, for survey forecasts, and for the other G7 countries.     

Nowcasting unemployment insurance claims in the time of COVID-19

Near-term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. The state-level panel model, exploiting the variation in timing of state-of-emergency declarations, also performs better than models including Google Trends. Our results suggest that in times of structural change there is a bias–variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.     

Information rigidities and the news-adjusted output gap

A vector-autoregressive model of actual output and expected output obtained from surveys is used to test for information rigidities and to provide a characterisation of output dynamics that accommodates these information structures. News on actual and expected outputs is decomposed to identify innovations understood to have short-lived effects and these are used with the model to derive a ‘news-adjusted output gap׳ measure. The approach is applied to US data over 1970q1–2014q2 and the new gap measure is shown to provide a good leading indicator of inflation.     

The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models vs. Simple Linear Econometric Models*

In this paper we construct output gap and inflation predictions using a variety of dynamic stochastic general equilibrium (DSGE) sticky price models. Predictive density accuracy tests related to the test discussed in Corradi and Swanson [Journal of Econometrics (2005a), forthcoming] as well as predictive accuracy tests due to Diebold and Mariano [Journal of Business and Economic Statistics (1995) , Vol. 13, pp. 253–263]; and West [Econometrica (1996) , Vol. 64, pp. 1067–1084] are used to compare the alternative models. A number of simple time‐series prediction models (such as autoregressive and vector autoregressive (VAR) models) are additionally used as strawman models. Given that DSGE model restrictions are routinely nested within VAR models, the addition of our strawman models allows us to indirectly assess the usefulness of imposing theoretical restrictions implied by DSGE models on unrestricted econometric models. With respect to predictive density evaluation, our results suggest that the standard sticky price model discussed in Calvo [Journal of Monetary Economics (1983), Vol. XII, pp. 383–398] is not outperformed by the same model augmented either with information or indexation, when used to predict the output gap. On the other hand, there are clear gains to using the more recent models when predicting inflation. Results based on mean square forecast error analysis are less clear‐cut, although the standard sticky price model fares best at our longest forecast horizon of 3 years, it performs relatively poorly at shorter horizons. When the strawman time‐series models are added to the picture, we find that the DSGE models still fare very well, often outperforming our forecast competitions, suggesting that theoretical macroeconomic restrictions yield useful additional information for forming macroeconomic forecasts.     

Monetary policy,financial uncertainty,and secular stagnation

A monetary policy framework describing how to cope with a financial crisis might alleviate a recession; however, it might also result in subsequent secular stagnation. Based on an empirical New Keynesian model with financial uncertainty, this study investigates how monetary policy can avoid sluggish economic recovery in response to financial shocks. The results show that a protracted sluggish response of an output gap to a financial shock is triggered by inflation targeting, without considering interest rate variations. In such a policy, the uncertainty causes additional sluggish behavior after a sharp reduction in the output gap. In contrast, in a speed limit policy, the output gap recovers rapidly, regardless of the central bank’s approach to interest rate variations, and the uncertainty mitigates reductions in the output gap. Finally, the results are robust under several alternative settings.     

Monetary policy trade-offs in an estimated open-economy DSGE model

This paper studies the trade-offs between stabilizing CPI inflation and alternative measures of the output gap in Ramses, the Riksbank׳s estimated dynamic stochastic general equilibrium (DSGE) model of a small open economy. Our main finding is that the trade-off between stabilizing CPI inflation and the output gap strongly depends on which concept of potential output in the output gap between output and potential output is used in the loss function. If potential output is defined as a smooth trend this trade-off is much more pronounced compared to the case when potential output is defined as the output level that would prevail if prices and wages were flexible.     

Do global output gaps help forecast domestic inflation? Evidence from Phillips curves for Poland

This study confronts domestic and global views on inflation through the use of the Hybrid New Keynesian Phillips Curve (HNKPC) models estimated for headline and core inflation in Poland. We analyse the roles of the global vs. domestic output gaps in affecting price changes. We ensure that our conclusions are robust by taking into consideration various proxies for inflation expectations, imported inflation, the domestic output gap and the global output gap.Our results suggest that the global demand conditions are statistically insignificant in the majority of the estimated global versions of HNKPC, independently of the measure of them that is considered. In terms of empirical fit, and especially of the out-of-sample forecasting accuracy, the specifications of the Phillips curve with the domestic and global output gaps among the explanatory variables are not superior to traditional Phillips curves. Interestingly, the relative importance of the global output gap is much smaller in models that are estimated in terms of core inflation, excluding foodstuffs and energy, than in CPI inflation models. This suggests that global demand conditions affect the inflation in Poland indirectly, mainly through the prices of food and energy raw materials.The main conclusion from our study is that external factors that are already considered in the traditional hybrid versions of the new Keynesian Phillips curve are sufficient to account for global influences on prices in the domestic economy. The concept of the global output gap improves neither the explanatory nor the predictive power of HNKPC models.     

Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy

This paper constructs hybrid forecasts that combine forecasts from vector autoregressive (VAR) model(s) with both short- and long-term expectations from surveys. Specifically, we use the relative entropy to tilt one-step-ahead and long-horizon VAR forecasts to match the nowcasts and long-horizon forecasts from the Survey of Professional Forecasters. We consider a variety of VAR models, ranging from simple fixed-parameter to time-varying parameters. The results across models indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. Accuracy improvements are achieved for a range of variables, including those that are not tilted directly but are affected through spillover effects from tilted variables. The accuracy gains for hybrid inflation forecasts from simple VARs are substantial, statistically significant, and competitive to time-varying VARs, univariate benchmarks, and survey forecasts. We view our proposal as an indirect approach to accommodating structural change and moving end points.     

Mind the Output Gap: The Disconnect of Growth and Inflation during Recessions and Convex Phillips Curves in the Euro Area

We develop a theoretical model that features a business cycle‐dependent relation between output, price inflation and inflation expectations, augmenting the model by Svensson (1997) with a nonlinear Phillips curve that reflects the rationale underlying the capacity constraint theory (Macklem, 1997). The theoretical model motivates our empirical assessment, based on a regime‐switching Phillips curve and a regime‐switching monetary structural VAR, employing different filter‐based, semi‐structural model‐based and Bayesian factor model‐implied output gaps. The analysis confirms the presence of a convex relationship between inflation and the output gap, meaning that the coefficient in the Phillips curve on the output gap recurringly increases during times of expansion and abates during recessions. Sign‐restricted monetary policy shocks based on a regime‐switching monetary SVAR reveal that expansionary monetary policy induces less pressure on inflation at times of weak as opposed to strong growth; thereby rationalizing relatively stronger expansionary policy, including unconventional volume‐based policy, during times of deep recession. A further augmented model shows that an effective euro exchange rate shock, too, implies business cycle state‐dependent responses, with more upward pressure on prices arising from unexpected currency depreciation at times of expansion than during recession phases.