Durable Purchases over the Later Life Cycle

We investigate life‐cycle patterns of demand for services from household durables using UK panel data. We take careful account of prices, demographics, labour supply and health. Demand for consumer electronics rises with age, while the demand for household appliances is flat. These findings contrast with the well documented decline in non‐durable consumption at older ages, and suggest that studies that estimate the overall discount rate from nondurable consumption may underestimate consumer patience and the savings required to fund retirement. We also find important non‐separabilities between the demand for durables, labour supply and health status.     

Capital,credit constraints and the comovement between consumer durables and nondurables

Evidence indicates that consumer durables are more flexibly priced than nondurable goods and services. In otherwise standard two-sector neoclassical sticky-price models with flexible durable prices, following monetary tightening, nondurables decrease but consumer durables increase. Friction in lending between households can resolve the comovement problem if durable prices are sticky. However, if durable prices are flexible, friction in lending fails to generate joint decline. This paper resolves the co-movement problem by adding capital into a model with flexible durable prices and friction in lending. When capital is needed in production, monetary tightening reduces the relative price of durables which induces investment and decreases firms' real profits in the short run. Due to fewer profits remitted from firms, savers have a lower disposable income and cannot increase expenditures on consumer durables as much as otherwise. As a consequence, aggregate consumer durables decrease and there is a joint decline of nondurables and consumer durables.     

Real‐time forecast combinations for the oil price

Baumeister and Kilian (Journal of Business and Economic Statistics, 2015, 33(3), 338–351) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea‐based measure and update the evaluation sample to 2017:12. We model the oil price futures curve using a factor‐based Nelson–Siegel specification estimated in real time to fill in missing values for oil price futures in the raw data. We find that the combined forecasts for Brent are as effective as for other oil price measures. The extended sample using the oil price measures adopted by Baumeister and Kilian yields similar results to those reported in their paper. Also, the futures‐based model improves forecast accuracy at longer horizons.     

Selectivity correction in discrete-continuous choice analysis: With Empirical Evidence for Vehicle Choice and Use

In modelling the relationship between the household's demand for consumer durables and their utilisation rate one typically observes the latter only for the chosen durable. The absence of data on the level of ‘consumption’ of the non-chosen durables is a potential source of selectivity bias. There exist many procedures for correcting for selectivity so that predictions of use levels of non-chosen durables can be obtained if one wishes to include use as an influence on durable choice, or if one wants to apply the utilisation model to obtain predictions of use of each type of durable within the class. In this paper we identify the main sources of selectivity and introduce correction formulae that are easy to implement and generalise to polychotomous choice. We assess the empirical implications of the alternative formulae in the context of household vehicle choice and level of use. The data are drawn from 1,436 households in the Sydney Metropolitan Area.     

REAL‐TIME FORECASTING OF INFLATION AND OUTPUT GROWTH WITH AUTOREGRESSIVE MODELS IN THE PRESENCE OF DATA REVISIONS

We examine how the accuracy of real‐time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest‐available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple‐vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.     

Forecasting Tail Risks

This paper presents an early warning system as a set of multi‐period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data for the period 1972:1–2014:12. Pseudo‐real‐time forecasts are generated from: (a) sets of autoregressive and factor‐augmented vector autoregressions (VARs), and (b) sets of autoregressive and factor‐augmented quantile projections. Our key finding is that forecasts obtained with AR and factor‐augmented VAR forecasts significantly underestimate tail risks, while quantile projections deliver fairly accurate forecasts and reliable early warning signals for tail real and financial risks up to a 1‐year horizon. Copyright © 2016 John Wiley & Sons, Ltd.     

CONSTRUCTING OPTIMAL DENSITY FORECASTS FROM POINT FORECAST COMBINATIONS

Decision makers often observe point forecasts of the same variable computed, for instance, by commercial banks, IMF and the World Bank, but the econometric models used by such institutions are frequently unknown. This paper shows how to use the information available on point forecasts to compute optimal density forecasts. Our idea builds upon the combination of point forecasts under general loss functions and unknown forecast error distributions. We use real‐time data to forecast the density of US inflation. The results indicate that the proposed method materially improves the real‐time accuracy of density forecasts vis‐à‐vis those from the (unknown) individual econometric models. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting and explaining aggregate consumer credit delinquency behaviour

We model aggregate delinquency behaviour for consumer credit (including credit card loans and other consumer loans) and for residential real estate loans using data up until 2008. We test for cointegrating relationships and then estimate short run error correction models. We find evidence to support the portfolio explanations of declines in credit quality for consumer and for real estate loans, but support for the reduced stigma explanation was restricted to real estate loans. Evidence supportive of household-level explanations of irrational borrowing and unexpected net income shocks was found for consumer and real estate loans, but evidence of strategic default was restricted to the volume of consumer loans and real estate loans, and not for credit cards. We also found that the error correction model gave forecasts of the volume of delinquent consumer debt which were of an accuracy comparable to that of an ARIMA model.     

How accurate are government forecasts of economic fundamentals? The case of Taiwan

A government’s ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate.In this paper, we develop a methodology for evaluating non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that the undocumented knowledge of the Taiwanese government reduces forecast errors substantially.     

Demand for durable and nondurable goods,environmental policy and consumer welfare

If a tax on energy affects the demand for a nondurable good, the stock of a durable good, and the proportion of the energy consumption per unit of service of the durable good, an integrated approach for investigating consumer demand for durables and nondurables is required. The purpose of this paper is to employ the concept of a variable expenditure function with quasi-fixed durable goods as arguments in order to derive a demand system for nondurable goods in prices of the nondurables, in the stocks of durables, and in variable expenditure. From the envelope condition desired stocks of durables can be calculated and investment demand for durables can be determined. For an application we choose a variable expenditure function based on the almost ideal demand specification, and evaluate alternative environmental policy measures using the equivalent variation for comparing the welfare effects.     

Does Forecast Combination Improve Norges Bank Inflation Forecasts?*

We develop a system that provides model‐based forecasts for inflation in Norway. We recursively evaluate quasi out‐of‐sample forecasts from a large suite of models from 1999 to 2009. The performance of the models are then used to derive quasi real time weights that are used to combine the forecasts. Our results indicate that a combination forecast improves upon the point forecasts from individual models. Furthermore, a combination forecast outperforms Norges Bank's own point forecast for inflation. The beneficial results are obtained using a trimmed weighted average. Some degree of trimming is required for the combination forecasts to outperform the judgmental forecasts from the policymaker.     

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.     

Problems in Launching the Mobile Internet: Evidence From a Pricing Experiment

Commercialization of innovations frequently stumbles. A prominent recent example are the early (i.e., pre‐3G) mobile phone‐enabled Internet services, whose European take‐up was slower than expected. To determine why, we build a structural model of demand for such services and estimate it using consumer‐level panel data from a pricing experiment. The experiment allows a decomposition of the number of wireless connections into the number of needs—instances where a consumer would establish a connection if the price were zero—and the conditional probability of establishing a connection. We find that needs were plenty and potential consumer surplus several magnitudes higher than that attained. We find that pricing reduced usage substantially and explore potential reasons for the high prices.     

Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models

Over time, economic statistics are refined. This implies that data measuring recent economic events are typically less reliable than older data. Such time variation in measurement error affects optimal forecasts. Measurement error, and its time variation, are of course unobserved. Our contribution is to show how estimates of these can be recovered from the variance of revisions to data using a behavioural model of the statistics agency. We illustrate the gains in forecasting performance from exploiting these estimates using a real‐time dataset on UK aggregate expenditure data. Copyright © 2009 John Wiley & Sons, Ltd.     

Habits and heterogeneity in demands: a panel data analysis

We examine demand behaviour for intertemporal dependencies, using Spanish panel data. We present evidence that there is both state dependence and correlated heterogeneity in demand behaviour. Our specific findings are that food outside the home, alcohol and tobacco are habit forming, whereas clothing and small durables exhibit durability. We conclude that demand analyses using cross‐section data that ignore these effects may be seriously biased. On the other hand, the degree of intertemporal dependence is not sufficiently strong to make composite ‘consumption’ significantly habit forming, as has been suggested in some recent analyses. Copyright © 2007 John Wiley & Sons, Ltd.     

Forecasting Euro‐Area Macroeconomic Variables Using a Factor Model Approach for Backdating

We suggest to use a factor model based backdating procedure to construct historical Euro‐area macroeconomic time series data for the pre‐Euro period. We argue that this is a useful alternative to standard contemporaneous aggregation methods. The article investigates for a number of Euro‐area variables whether forecasts based on the factor‐backdated data are more precise than those obtained with standard area‐wide data. A recursive pseudo‐out‐of‐sample forecasting experiment using quarterly data is conducted. Our results suggest that some key variables (e.g. real GDP, inflation and long‐term interest rate) can indeed be forecasted more precisely with the factor‐backdated data.     

Introducing the Bank of Canada staff economic projections database

We present a new, publicly available database of real-time data and forecasts from the Bank of Canada's staff economic projections, which will be updated on an annual basis. We describe the data construct, its variables, coverage, and frequency. We then provide a forecast evaluation for gross domestic product (GDP) growth, consumer price index (CPI) inflation and the policy rate since 1982: We compare the staff's forecasts with those from commonly used time series models estimated with the real-time data, and with forecasts from other professional forecasters, and provide standard bias tests. Finally, we study changes in predictability of the Canadian economy following the announcement of the inflation-targeting regime in 1991. Our data set is unprecedented outside the USA, and our evidence is particularly interesting, as it covers over 30 years of staff forecasts, two severe recessions, and different monetary policy regimes.     

A dynamic almost ideal demand system incorporating consumer expectations

In this paper a theoretical framework is presented for the analysis of the effects of psychological, perceptual or expectational factors on household expenditure within a complete demand system. The model was estimated using the Consumer Sentiment Index, which represents an index of consumer perceptions of economic conditions. Evidence was found for significant expectational effects on five of nine expenditure categories—food at home, alcohol, housing, durables and other services. The direct expectation effects were found to be small in size. The results demonstrate the model's potential usefulness as a framework for modelling consumer behavioral responses to expectations and for evaluating the welfare implications of policy-induced changes in expectations.     

Macroeconomic forecasting and structural change

The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time‐varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.     

Semiparametric estimation of consumer demand systems in real expenditure

Microdata concerning consumer demand typically show considerable variation in real expenditures, but very little variation in prices. We propose a semiparametric strategy for the consumer demand problem in which expenditure share equations are estimated nonparametrically in the real expenditure direction and estimated parametrically (with fixed or varying coefficients) in price directions. In our model, Engel curves are unrestricted: demands may have any rank. Because the demand model is derived from a cost function, it may be restricted to satisfy integrability and used for consumer surplus calculations. Since real expenditure is unobserved, but rather estimated under the model, we face a semiparametric model with a nonparametrically generated regressor. We show efficient convergence rates for parametric and nonparametric components. We illustrate the feasibility of our proposed strategy using Canadian expenditure and price data: Engel curves display curvature which cannot be encompassed by standard parametric models. We also find that the rationality restriction of Slutsky symmetry is rejected in the fixed‐coefficients model, but not in the varying‐coefficients model. Copyright © 2009 John Wiley & Sons, Ltd.