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Can we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices. [ABSTRACT FROM AUTHOR]

Building on the work of Stock and Watson (2007), this paper empirically shows that a negative correlation between innovations to trend inflation and the inflation gap plays an important role in the dynamics of postwar U.S. inflation. Additional features that we incorporate in our model include regime‐switching inflation gap persistence and association between inflation and inflation uncertainty. The resulting estimate of trend inflation is smooth, and our model provides superior out‐of‐sample forecasts than Stock and Watson's (2007) unobserved components model with stochastic volatility or than Atkeson and Ohanian's (2001) random walk model does. [ABSTRACT FROM AUTHOR]

To create an estimable version for annual data of the hybrid new Keynesian Phillips curve, one needs an expression for the expectation of next year's inflation. The rational expectations literature assumes that this expectation is equal to the realization in the next year and an associated forecast error. This paper argues that this assumption goes against the Wold decomposition theorem, and that it introduces correlation between the error and a regressor. A more appropriate approach resorts to a MIDAS type of model, where forecast updates for next year are created when for example monthly inflation rates come in. An illustration to annual USA inflation, 1956–2016, shows the merits of this MIDAS approach. [ABSTRACT FROM AUTHOR]

Although oil price shocks have long been viewed as one of the leading candidates for explaining U.S. recessions, surprisingly little is known about the extent to which oil price shocks explain recessions. We provide a formal analysis of this question with special attention to the possible role of net oil price increases in amplifying the transmission of oil price shocks. We quantify the conditional effect of oil price shocks in the net oil price increase model for all episodes of net oil price increases since the mid-1970s, analyze its determinants, and show that the linear model fits the data better. [ABSTRACT FROM AUTHOR]

This article studies the role of confidence in the transmission of uncertainty shocks during U.S. recessions. I use smooth-transition vector-autoregression (ST-VAR) to examine the regime-dependent effect of uncertainty shocks, and a counterfactual decomposition to isolate the role of confidence when the economy is in different regimes, recessions and non-recessions. I find that shutting down the confidence channel leads to greatly dampened and less persistent effects of uncertainty shocks, especially during recessions. I also find that the cross-regime difference in the role of confidence can largely explain the cross-regime short-run difference in the effects of uncertainty shocks. [ABSTRACT FROM PUBLISHER]

This article examines the predictive content of the shadow rates for US real activity and inflation in a data-rich environment. We find that the shadow rates contain substantial out-of-sample predictive power for inflation in nonzero lower bound and zero lower bound periods. In contrast, the shadow rates are uninformative about future real activity. [ABSTRACT FROM AUTHOR]

The response of US inflation to the high levels of spare capacity during the Great Recession of 2007–09 was rather muted. At the same time, some have argued that the short-term unemployment gap has a more prominent role than the standard unemployment gap in determining inflation, and either the closing of this gap or non-linearities in the Phillips curve could lead to a sudden pick-up in inflation. In this context, our main aim is to provide guidance to policymakers as regards the reliability of the Phillips curve to forecast inflation. Our main findings from Phillips curves estimated since the early-1990s suggest that the consideration of a time-variation in the Phillips curve slope is more relevant than just focusing on finding the “correct” slack measure. Although the Phillips curve may be relatively flat over the full sample (1992Q1–2015Q1), time-varying estimates with rolling windows and with the Kalman filter suggest that the slope does vary over time and that it has increased slightly since 2013. These non-linear specifications outperform the benchmark linear model in an out-of-sample exercise. The main policy implication is that decision-makers should not exclude the possibility that inflation might rise suddenly given its non-linear behaviour, and more strongly than a linear model would dictate. [ABSTRACT FROM AUTHOR]

We compare inflation forecasts of a vector autoregressive fractionally integrated moving average (VARFIMA) model against standard forecasting models. U.S. inflation forecasts improve when controlling for persistence and economic policy uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and EPU, outperforms commonly used inflation forecast models. [ABSTRACT FROM AUTHOR]

This paper provides estimates of the effects of the fall in financial and housing wealth in 2008-09 on overall macroeconomic activity. When the wealth losses are run through a structural macroeconometric model, it is estimated that the fall in wealth contributed about 2.1 percentage points to the rise in the unemployment rate in 2009 and about 3.3 points in 2010. The contribution to the fall in real GDP was 4.5% and 5.4% in the 2 years. These estimates account for most-but not all-of the recessionary increase in unemployment. The remaining increase in unemployment may have resulted more directly from financial stresses, but little evidence is found for this in this study. [ABSTRACT FROM AUTHOR]

We use wavelet analysis to conclude that individual U.S. states' business cycles are very well synchronized. We also find evidence of a strong and significant correlation between business cycle dissimilitudes and the distance between each pair of states, consistent to gravity type mechanisms where distance affects trade. Trade, in turn, increases business cycle synchronization, while a higher degree of industry specialization is associated with a higher dissimilitude of the state cycle with the aggregate economy. Finally, there is evidence that business cycle dissimilitudes have been decreasing with time, consistent with the previous findings coupled with the idea that information and communications technology make distances smaller. [ABSTRACT FROM AUTHOR]

Latent factors estimated from panels of macroeconomic indicators are used to generate recession probabilities for the US economy. The focus is on current (rather than future) business conditions. Two macro factors are considered: (1) a dynamic factor estimated by maximum likelihood from a set of 4 monthly series; (2) the first of eight static factors estimated by principal components using a panel of 102 monthly series. Recession probabilities generated using standard probit, autoregressive probit, and Markov-switching models exhibit very different properties. Overall, a simple Markov-switching model based on the big data macro factor generates the sequence of out-of-sample class predictions that better approximates NBER recession months. Nevertheless, it is shown that the selection of the best performing model depends on the forecaster's relative tolerance for false positives and false negatives. [ABSTRACT FROM AUTHOR]

Bank risk is not directly observable, so empirical research relies on indirect measures. We evaluate how well Z-score, the widely used accounting-based measure of bank distance to default, can predict bank failure. Using the U.S. commercial banks' data from 2004 to 2012, we find that on average, Z-score can predict 76% of bank failure, and additional set of other bank- and macro-level variables do not increase this predictability level. We also find that the prediction power of Z-score to predict bank default remains stable within the three-year forward window. [ABSTRACT FROM AUTHOR]

ABSTRACT We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out-of-sample forecasts are generated from models identified from a multipath general-to-specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well-established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods. [ABSTRACT FROM AUTHOR]

We investigate whether there is information useful for identifying U.S. business cycle phases contained in subnational measures of economic activity. Using a probit model to forecast the National Bureau of Economic Research expansion and recession classification, we assess the incremental information content of state-level employment growth over a commonly used set of national-level predictors. As state-level data adds a large number of predictors to the model, we employ a Bayesian model averaging procedure to construct forecasts. Based on a variety of forecast evaluation metrics, we find that including state-level employment growth substantially improves nowcasts and very short-horizon forecasts of the business cycle phase. The gains in forecast accuracy are concentrated during months of national recession. [ABSTRACT FROM AUTHOR]

We examine both the degree and the structural stability of inflation persistence at different quantiles of the conditional inflation distribution. Previous research focused exclusively on persistence at the conditional mean of the inflation rate. As economic theory provides reasons for inflation persistence to differ across conditional quantiles, this is a potentially severe constraint. Conventional studies of inflation persistence cannot identify changes in persistence at selected quantiles that leave persistence at the mean of the distribution unchanged. Based on post-war US data we indeed find robust evidence for a structural break in persistence at all quantiles of the inflation process in the early 1980s. While prior to the 1980s inflation was not mean reverting, quantile autoregression based unit root tests suggest that since the end of the Volcker disinflation the unit root can be rejected at every quantile of the conditional inflation distribution. [ABSTRACT FROM AUTHOR]

The accuracy of inflation forecasts obtained from household and professional surveys has deteriorated noticeably of late, to the extent that a simple autoregressive specification outperforms survey forecasts. The decline in (absolute and relative) accuracy has taken place at about the same time as an apparent change in the inflation process. Projections of household forecasts on realized inflation suggests that households have not recognized this change. For the professionals, projections of expected inflation on headline inflation have changed, but on core inflation have not. By contrast, projections of realized headline inflation on core have changed sharply. [ABSTRACT FROM AUTHOR]

We explore empirically the theoretical prediction that public information acts as a focal point in the context of the U.S. monetary policy. We aim at establishing whether the publication of Federal Open Market Committee (FOMC) inflation forecasts affects the cross-sectional dispersion of private inflation expectations. Our main finding is that publishing FOMC inflation forecasts has a negative effect on the cross-sectional dispersion of private current-year inflation forecasts. This effect is found to be robust to another survey data set and to various macroeconomic controls. Moreover, we find that the dispersion of private inflation forecasts is not affected by the dispersion of views among FOMC members. [ABSTRACT FROM AUTHOR]

The Federal Open Market Committee (FOMC) of the U.S. Federal Reserve publishes the range of members' forecasts for key macroeconomic variables, but not the distribution of forecasts within this range. To evaluate these projections, previous papers compare the midpoint of the range with the realized outcome. This paper proposes an alternative approach to forecast evaluation that takes account of the interval nature of projections. It is shown that using the conventional Mincer-Zarnowitz approach to evaluate FOMC forecasts misses important information contained in the width of the forecast interval. This additional information plays a minor role at short forecast horizons but turns out to be of sometimes crucial importance for longer-horizon forecasts. For 18-month-ahead forecasts, the variation of members' projections contains information that is more relevant for explaining future inflation than information embodied in the midpoint. Likewise, when longer-range forecasts for real GDP growth and the unemployment rate are considered, the width of the forecast interval comprises information over and above the one given by the midpoint alone. [ABSTRACT FROM AUTHOR]

This paper implements the technique suggested by Den Haan (J Monet Econ 46:3-30, ) to investigate contemporaneous as well as lead and lag correlations among economic data for a range of forecast horizons. The lead/lag approach provides a richer picture of the economic dynamics generating the data and allows one to investigate which variables lead or lag others, and whether the lead or lag pattern is short term or long term in nature. This technique is applied to monthly sectoral level employment data for the USA and shows that among the ten industrial sectors followed by the US Bureau of Labor Statistics, six tend to lead the other four. These six have high correlations indicating that the structural shocks generating the data movements are mostly in common. Among the four lagging industries, some lag by longer intervals than others and some have low correlations with the leading industries. These low correlations may indicate that these industries are partially influenced by structural shocks beyond those generating the six leading industries, but they also may indicate that lagging sectors feature a different transmission mechanism of shocks. [ABSTRACT FROM AUTHOR]

Recently De Luca and Carfora (Statistica e Applicazioni 8:123-134, ) have proposed a novel model for binary time series, the Binomial Heterogenous Autoregressive (BHAR) model, successfully applied for the analysis of the quarterly binary time series of U.S. recessions. In this work we want to measure the efficacy of the out-of-sample forecasts of the BHAR model compared to the probit models by Kauppi and Saikkonen (Rev Econ Stat 90:777-791, ). Given the substantial indifference of the predictive accuracy between the BHAR and the probit models, a combination of forecasts using the method proposed by Bates and Granger (Oper Res Q 20:451-468, ) for probability forecasts is analyzed. We show how the forecasts obtained by the combination between the BHAR model and each of the probit models are superior compared to the forecasts obtained by each single model. [ABSTRACT FROM AUTHOR]

Models for the 12-month-ahead US rate of inflation, measured by the chain-weighted consumer expenditure deflator, are estimated for 1974-98 and subsequent pseudo out-of-sample forecasting performance is examined. Alternative forecasting approaches for different information sets are compared with benchmark univariate autoregressive models, and substantial out-performance is demonstrated including against Stock and Watson's unobserved components-stochastic volatility model. Three key ingredients to the out-performance are: including equilibrium correction component terms in relative prices; introducing nonlinearities to proxy state-dependence in the inflation process and replacing the information criterion, commonly used in VARs to select lag length, with a 'parsimonious longer lags' parameterization. Forecast pooling or averaging also improves forecast performance. [ABSTRACT FROM AUTHOR]

The cross-section distribution of U.S. import prices exhibits some of the fat-tailed characteristics that are well documented for the cross-section distribution of U.S. consumer prices. This suggests that limited-influence estimators of core import price inflation might outperform headline or traditional measures of core import price inflation. We examine whether limited influence estimators of core import price inflation help forecast overall import price inflation. They do not. However, limited influence estimators of core import price inflation do seem to have some predictive power for headline consumer price inflation in the medium term. [ABSTRACT FROM AUTHOR]

Recent price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using survey data for Canada, Japan, and the United States, we did not find evidence of forecaster herding. On the contrary, forecasters anti-herd and, thereby, tend to intentionally scatter their forecasts around the consensus forecast. The extent of anti-herding seems to vary over time. For Canada and the United States, we found that more pronounced anti-herding leads to lower forecast accuracy. [ABSTRACT FROM AUTHOR]

This article aims at assessing the role of the United States in the global economy and its evolution over time. Based on a Global VAR modeling approach, this article shows first that countries with a large trade exposure with the U.S. economy have a relatively larger sensitivity to U.S. developments. However, even for countries that do not trade so much with the U.S., they are largely influenced by its dominance through other partners' trade. Moreover, while no clear trend seems to emerge, it seems that the role of the U.S. in the global economy has changed over time. Overall, for most countries-the latest recession excluded-a change in U.S. GDP had weaker impacts-though more persistent-for most recent periods. The latest recession, however, led to some renewed increase in the sensitivity of the economies to U.S. developments. [ABSTRACT FROM AUTHOR]

This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated US recessions. We generate forecasts from six different models of the US economy and compare them to professional forecasts from the Federal Reserve's Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates. [ABSTRACT FROM AUTHOR]

The time-varying natural rate of interest and output and the implied medium-term inflation target for the US economy are estimated over the period 1983-2005. The estimation is conducted within the New Keynesian framework using Bayesian and Kalman-filter estimation techniques. With the model-consistent estimate of the output gap, we get a small weight on the backward-looking component of the New Keynesian Phillips curve-similar to what is obtained in studies which use labor share of income as a driver for inflation (e.g., Galí, Eur Econ Rev 45(7):1237-1270, 2001; Eur Econ Rev 47(4):759-760, 2003). The turning points of the business cycle are nevertheless broadly consistent with those of CBO/NBER. We find considerable variation in the natural rate of interest while the inflation target has been close to 2% over the last decade. [ABSTRACT FROM AUTHOR]

This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1-1994:Q4; and then forecasts one-to-four quarters-ahead real house price growth over the out-of-sample horizon of 1995:Q1-2006:Q4. The forecasts are evaluated by comparing them with those from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant of the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth over the recent period of 2007:Q1-2008:Q1. The results show that the BVARs, in whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in predicting the downturn in 18 of the 19 states. [ABSTRACT FROM AUTHOR]

Computable general equilibrium (CGE) models can be used to generate detailed forecasts of output growth for commodities/industries and thereby provide baselines from which to calculate the effects of policy changes. In this article, we assess a CGE forecasting method that has been applied in policy analyses in the USA and Australia. Using data available up to 1998, we apply the method with the USAGE model to generate ‘genuine forecasts’ for 500 US commodities/industries for the period 1998–2005. We then compare these forecasts with actual outcomes and with alternate forecasts derived as extrapolated trends from 1992 to 1998. [ABSTRACT FROM AUTHOR]

We introduce a general class of periodic unobserved component (UC) time series models with stochastic trend and seasonal components and with a novel periodic stochastic cycle component. The general state space formulation of the periodic model allows for exact maximum likelihood estimation, signal extraction and forecasting. The consequences for model-based seasonal adjustment are discussed. The new periodic model is applied to postwar monthly US unemployment series from which we identify a significant periodic stochastic cycle. A detailed periodic analysis is presented including a comparison between the performances of periodic and non-periodic UC models. [ABSTRACT FROM AUTHOR]

This paper examines the forecasting of growth cycle turning points by comparing the performances of US and Japanese professional forecasters with those of a simple time-series model, using real-time data. The empirical results, obtained after performing contingency table analyses and visual inspections, indicate that the US professional forecasters considerably outperform the simple time-series model; however, the Japanese professional forecaster is comparable to the simple time-series model in the visual inspections of major turning points. [ABSTRACT FROM AUTHOR]

We construct a measure of global liquidity using the growth rates of broad money for the G7 economies. Global liquidity produces forecasts of U.S. inflation that are significantly more accurate than the forecasts based on U.S. money growth, Phillips curve, and autoregressive and moving average models. The marginal predictive power of global liquidity is strong at 3-year horizons. Results are robust to alternative measures of inflation. [ABSTRACT FROM AUTHOR]

This study evaluates the Federal Reserve forecasts of manufacturing capacity utilization employing, as benchmarks, the forecasts from a univariate model which utilizes past information in capacity utilization, and from a bivariate model which utilizes past information in both capacity utilization and the federal funds rate. In addition to accurately predicting the directional change in capacity utilization, the Federal Reserve forecasts are “weakly” rational and generally superior to the bivariate forecasts. In light of another finding that monetary policy is non-neutral, we argue the Federal Reserve forecasts of capacity utilization have positively contributed to the Fed’s success in maintaining a low inflationary environment. [ABSTRACT FROM AUTHOR]

We propose formal and quantitative measures of the risk that future inflation will be excessively high or low relative to the range preferred by a private sector agent. Unlike alternative measures of risk, our measures are designed to make explicit the dependence of risk measures on the private sector agent's preferences with respect to inflation. We illustrate our methodology by estimating the risks of deflation for the United States, Germany, and Japan for horizons of up to 2 years. The question of how large these risks are has been subject to considerable public debate. We find that, as of September 2002 when this question first arose, there was no evidence of substantial deflation risks for the United States and for Germany, contrary to some conjectures at the time. In contrast, there was evidence of substantial deflation risks in Japan. [ABSTRACT FROM AUTHOR]

We examine whether the U.S. rate of price inflation has become harder to forecast and, to the extent that it has, what changes in the inflation process have made it so. The main finding is that the univariate inflation process is well described by an unobserved component trend-cycle model with stochastic volatility or, equivalently, an integrated moving average process with time-varying parameters. This model explains a variety of recent univariate inflation forecasting puzzles and begins to explain some multivariate inflation forecasting puzzles as well. [ABSTRACT FROM AUTHOR]

Aims: Medicare patients with metastatic or surgically unresectable urothelial carcinoma (mUC) often receive platinum-based chemotherapy as first line of therapy (LOT), but invariably progress, requiring additional LOTs and healthcare resource use (HCRU). To better understand the evolving mUC treatment landscape, the economic burden of chemotherapy-based mUC treatments among US Medicare patients was estimated. Methods: Newly diagnosed Medicare patients with mUC were identified from the Surveillance, Epidemiology, and End Results (SEER)-Medicare database. Patients were followed from diagnosis to death, disenrollment, or end of study to characterize LOTs (first [LOT1], second [LOT2], and third or greater [LOT3+]). Kaplan-Meier methods were used to estimate overall survival (OS) by LOT. HCRU and mean costs were reported over the follow-up period, LOT duration, and maximum LOT received. Results: Among 1,873 eligible patients with mUC (median age = 77 years; median follow-up = 7.5 months), 1,035 (55%) received no chemotherapy. Among chemotherapy-treated patients, 61% had LOT1 only, 25% had LOT1 and LOT2 only, and 14% had LOT3+. Median OS was 8.1 months, range was 4.3 (untreated) to 29.8 (LOT3+) months. HCRU frequency increased with additional LOTs. Mean cumulative per-patient cost was $82,912 for all patients, increasing with additional LOTs (untreated = $57,207; LOT1 = $99,213; LOT2 = $125,190; LOT3+ = $163,884). Mean per patient per month cost was $18,827 for all patients, decreasing with increasing number of LOTs received (untreated = $27,211; LOT1 = $9,601; LOT2 = $7,325; LOT3+ = $6,017). Limitations: Potential for treatment misclassification when using the algorithm defining LOTs and non-generalizability of results to younger patients. Conclusions: Over 50% of Medicare patients with mUC received no chemotherapy. Among chemotherapy-treated patients, most received only one LOT. Additional LOTs led to higher mean costs and HCRU, but as patients were followed longer, monthly costs decreased. As treatments evolve to include immuno-oncology agents, these findings provide a clinically relevant economic benchmark for mUC treatment across different traditional LOTs.