Improved recession dating using stock market volatility

We offer an improved dating of U.S. business cycle turning points both retrospectively and in real time. This improvement is made possible by augmenting existing Markov-switching dynamic factor models with additional information on the stock return volatility. The model improves the prediction of the state of the economy using fully revised data significantly. Real-time identification can be made noticeably earlier than the NBER announcements, beating both the peak and trough announcements for recent recessions by several months.     

Dating Business-Cycle turning points

This paper presents a logit model for dating business-cycle turning points. The regressors are monthly series from the Business Cycle Indicators database of the Conference Board. Dividing the sample period into a subset for model initialization (1959∶9–1970∶12) and a subset for testing (1971∶1–2003∶12) yields a chronology that is nearly identical to that established by the National Bureau of Economic Research (NBER). However, the recognition lag is less than four months, in contrast to an average of more than eleven months for the official chronology. (JEL E320) The author is grateful for comments by an anonymous referee.     

Identification of Slowdowns and Accelerations for the Euro Area Economy*

Business cycle analyses have proved to be helpful to practitioners in assessing current economic conditions and anticipating upcoming fluctuations. In this article, we focus on the acceleration cycle in the euro area, namely the peaks and troughs of the growth rate which delimit the slowdown and acceleration phases of the economy. Our aim is twofold: first, we put forward a reference turning point chronology of this cycle on a monthly basis, based on gross domestic product and industrial production indices. We consider both euro area aggregate level and country‐specific cycles for the six main countries of the zone. Second, we come up with a new turning point indicator, based on business surveys carefully watched by central banks and short‐term analysts, to follow in real‐time the fluctuations of the acceleration cycle.     

Dating Business Cycles: A Methodological Contribution with an Application to the Euro Area

This paper proposes a dating algorithm based on an appropriately defined Markov chain that enforces alternation of peaks and troughs, and duration constraints concerning the phases and the full cycle. The algorithm, which implements Harding and Pagan's non‐parametric dating methodology, allows an assessment of the uncertainty of the estimated turning points caused by filtering and can be used to construct indices of business cycle diffusion, aiming at assessing how widespread are cyclical movements throughout the economy. Its adaptation to the notion of a deviation cycle and the imposition of depth constraints are also discussed. We illustrate the algorithm with reference to the issue of dating the euro‐area business cycle and analysing its characteristics, both from the classical and the growth cycle perspectives.     

Forecasting the resurgence of the U.S. economy in 2010: An expert judgment approach

This paper describes a forecast, performed in December 2008, of the time of the recovery of the U.S. economy from the contraction that began in December 2007. As in two earlier papers, the forecast uses an expert judgment approach, the Analytic Hierarchy Process (AHP), within the framework of decision theory, as well as its generalization to dependence and feedback in the form of the Analytic Network Process (ANP). The findings of this paper are that the economy would begin its recovery in July-August, 2010. While forecasting is always hazardous, our 2001 paper successfully forecast the date the recovery began. Since 1920 the validating authority for the turnaround dates has traditionally been the widely recognized National Bureau of Economic Research (NBER). The Bureau usually releases an official statement with their finding many months after the event (and of course after the forecasts had been done). Our results on the month in which the recovery began in 2001 were confirmed by the NBER in July 2003. We will again await the NBER determination of the time of recovery from the current recession.     

A new coincident index of business cycles based on monthly and quarterly series

Popular monthly coincident indices of business cycles, e.g. the composite index and the Stock–Watson coincident index, have two shortcomings. First, they ignore information contained in quarterly indicators such as real GDP. Second, they lack economic interpretation; hence the heights of peaks and the depths of troughs depend on the choice of an index. This paper extends the Stock–Watson coincident index by applying maximum likelihood factor analysis to a mixed‐frequency series of quarterly real GDP and monthly coincident business cycle indicators. The resulting index is related to latent monthly real GDP. Copyright © 2002 John Wiley & Sons, Ltd.     

Price-level uncertainty and instability in the United Kingdom

Was UK inflation more stable and/or less uncertain before 1914 or after 1945? We address these questions by estimating a statistical model with changing volatilities in transient and persistent components of inflation. Three conclusions emerge. First, since periods of high and low volatility occur in both eras, neither features uniformly greater stability or lower uncertainty. When comparing peaks with peaks and troughs with troughs, however, we find clear evidence that the price level was more stable before World War I. We also find some evidence for lower uncertainty at pre-1914 troughs, but its statistical significance is borderline.     

Defining efficient policies in a general equilibrium model: a multi-objective approach

Macroeconomic policy makers are typically concerned with several indicators of economic performance. We thus propose to tackle the design of macroeconomic policy using Multicriteria Decision Making (MCDM) techniques. More specifically, we employ Multi-objective Programming (MP) to seek so-called efficient policies. The MP approach is combined with a computable general equilibrium (CGE) model. We chose use of a CGE model since it has the dual advantage of being consistent with standard economic theory while allowing one to measure the effect(s) of a specific policy with real data. Applying the proposed methodology to Spain (via the 1995 Social Accounting Matrix) we first quantified the trade-offs between two specific policy objectives: growth and inflation, when designing fiscal policy. We then constructed a frontier of efficient policies involving real growth and inflation. In doing so, we found that policy in 1995 Spain displayed some degree of inefficiency with respect to these two policy objectives. We then offer two sets of policy recommendations that, ostensibly, could have helped Spain at the time. The first deals with efficiency independent of the importance given to both growth and inflation by policy makers (we label this set: general policy recommendations). A second set depends on which policy objective is seen as more important by policy makers: increasing growth or controlling inflation (we label this one: objective-specific recommendations).     

Productivity measurement for a distribution firm

The paper derives a consistent accounting framework for the treatment of inventories when measuring the productivity of a distribution firm. The average purchase price of an inventory item during an accounting period must be distinguished from its average selling price and these two average prices should be distinguished from the corresponding balance sheet prices. The accounting framework is implemented for a distribution firm which sold 76,000 separate items. The firm achieved a 9.6 percent per quarter total factor productivity growth rate over 6 quarters.The first author is a Professor of Economics at the University of British Columbia and a research associate of the NBER. He thanks the SSHRC of Canada for research support. The second author is a recent graduate of the University of British Columbia.     

The impact of traffic congestion when optimising delivery routes in real time. A case study in Spain

This paper studies the importance of considering congestion costs when optimising delivery routes. Through the analysis of two study areas (the region of Catalonia and the city of Barcelona, in Spain), four different scenarios have been implemented and compared in which different objective functions are minimised: Euclidean distance, real distance, real time with static congestion, and real time with dynamic congestion. The data have been collected from Google Maps, which allows us to obtain information on traffic conditions in real time. The results indicate that minimising real time considering congestion as a dynamic attribute which varies throughout the day is the most efficient method to optimise delivery routes, especially within urban areas. For the two study areas, and using this dynamic approach in which real-time congestion costs are reflected into the vehicle routing problem, savings in time up to 11% have been obtained.     

Reducing revisions in hedonic house price indices by the use of nowcasts

National Statistical Institutes (NSIs) must balance between timeliness and accuracy of the indicators they publish. Because some of the house sales transactions are reported several months after they occur, many countries that include Israel, publish provisional house price indices (HPIs) that are subject to large revisions as further transactions are reported. This happens because the late-reported transactions behave differently from the transactions reported on time. In this paper, we propose a novel methodology to minimize the size of the revisions, with illustrations from Israel, but the method can be applied to other countries with appropriate modifications. The proposed methodology consists of nowcasting three types of variables at a subdistrict level and adding them as input data to an extended hedonic model used for the computation of the HPI: (1) the average characteristics of the late-reported transactions such as the average number of rooms and the area size of the sold apartments; (2) the average price of the late-reported transactions; and (3) the number of late-reported transactions. The three variables are nowcasted based on models fitted to data from previous months. Evaluation of our methodology shows more than 50% reduction in the magnitude of the revisions.     

The Fed’s TRAP

The article examines if US monetary policy implicitly responds to asset price booms. Using real-time data and a GMM framework we estimate a Taylor-type rule with an asset variable that captures phases of booms and busts in the real estate market. We identify quasi real-time booms and busts using an asset cycle dating procedure. Our analysis yields two main findings. Firstly, the Fed does implicitly respond to asset price booms in the real estate market. Secondly, these responses are typically pro-cyclic and their intensity changes over time.     

Time-varying combinations of predictive densities using nonlinear filtering

We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be individually misspecified. A Sequential Monte Carlo method is proposed to approximate the filtering and predictive densities. The combination approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of simulated data, US macroeconomic time series and surveys of stock market prices. Simulation results indicate that, for a set of linear autoregressive models, the combination strategy is successful in selecting, with probability close to one, the true model when the model set is complete and it is able to detect parameter instability when the model set includes the true model that has generated subsamples of data. Also, substantial uncertainty appears in the weights when predictors are similar; residual uncertainty reduces when the model set is complete; and learning reduces this uncertainty. For the macro series we find that incompleteness of the models is relatively large in the 1970’s, the beginning of the 1980’s and during the recent financial crisis, and lower during the Great Moderation; the predicted probabilities of recession accurately compare with the NBER business cycle dating; model weights have substantial uncertainty attached. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 1990’s and switches to giving more weight to the professional forecasts over time. Information on the complete predictive distribution and not just on some moments turns out to be very important, above all during turbulent times such as the recent financial crisis. More generally, the proposed distributional state space representation offers great flexibility in combining densities.     

The European Market Abuse Directive: Has it Worked?

In this paper, we examine whether the Market Abuse Directive (MAD) has been effective in achieving its objectives of deterring the market manipulation activities, increasing the timeliness of information and decreasing the disclosure of inside information to select groups. Our sample consists of firms listed on Frankfurt Stock Exchange. We use stock prices and analysts' forecast‐based proxies to examine the impact and effectiveness of MAD. The analysis based on these two set of proxies provides evidence that the adoption of MAD has been effective. In particular, we find that after the implementation of MAD, on average, (1) the volatility of stock prices around earnings announcement declines, (2) stock prices remain closer to their post earnings announcement level during the period before earnings announcement, (3) the accuracy of analyst forecasts improves, (4) the dispersion of analyst forecasts decreases, and (5) the number of analysts following a company declines.     

Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ?

The study examines the predictability of 48 sovereign bond markets based on a strategy of 27,000 technical trading rules. These rules represent four popular trading rule classes, they are: moving average, filtering, support and resistance, and channel breakout rules, with numerous variants in each class. Empirical results show that (i) investing in sovereign bond markets is predictable, based on the buy-sell signals generated by trading rules, with the predictability of the emerging Asian markets being significantly higher than those of the advanced markets; (ii) the predictability is generally higher when the US tightens its monetary policies or undergoes recession or a financial crisis; (iii) two-thirds of sovereign bond markets have a higher predictability when we use a machine learning algorithm to determine the best trading rule strategy; and (iv) the predictability of a sovereign bond market is higher when the economy has a less effective government, lower regulatory quality, lower degree of financial openness, higher political risk, lower income and faster real money growth. Our results suggest that shocks originating from US monetary policy or economic conditions could have a considerable spillover effect on sovereign bond markets, particularly the emerging Asian markets.     

Sequential Decision-making in Interdependent Sectors with Multiobjective Inoperability Decision Trees: Application to Biofuel Subsidy Analysis

Decision-making involving large-scale systems often involves considerations for temporal changes, interdependencies in organizational structures, multiple competing objectives, and risk and uncertainty, among others. In this paper we develop a risk-based methodology, the Multiobjective Inoperability Decision Tree (MOIDT). It integrates several dimensions of decision-making processes associated with interconnected systems in terms of: (i) evaluation of sequential policies; (ii) analysis of interdependencies; (iii) treatment of multiple objectives and their tradeoffs; and (iv) characterization of uncertainties. To demonstrate the integration of these four components, we present a case study to analyze the impact of government policies towards mass-scale biofuel production. Using a multi-period decision framework, the analysis utilizes economic input–output data to model the probabilistic demand adjustments for sectors that will likely be affected by biofuel policies.     

How can big data enhance the timeliness of official statistics?: The case of the U.S. consumer price index

The daily consumer price index (CPI) produced by the Billion Prices Project (BPP CPI) offers a glimpse of the direction taken by consumer price inflation in real time. This is in contrast to the official U.S. CPI, which is compiled monthly and released with an average of a three-week delay following the end of the reference month. A recent body of research contended that the movements of online prices are representative of those of offline retail prices, making the BPP CPI a natural candidate for accurately improving the timeliness of the official CPI. We assess the predictive content of the BPP CPI using a variety of MIDAS models that accommodate data sampled at different frequencies. These models generate estimates that remain robust to the variety of time periods considered and, by the standard of the existing literature, contribute to a significant upgrade in the forecast accuracy of official consumer price inflation figures. The paper then sketches the broad implications of BPP CPI for the consumer price statistics maintained by national statistics offices and discusses how the proposed improvement in the timeliness of the official CPI fits in this perspective.     

Sequential city growth: Empirical evidence

Using two comprehensive datasets on populations of cities and metropolitan areas for a large set of countries, I present three new empirical facts about the evolution of city growth. First, the distribution of cities’ growth rates is skewed to the right in most countries and decades. Second, within a country, the average rank of each decade’s fastest-growing cities tends to rise over time. Finally, this rank increases faster in periods of rapid growth in urban population. These facts can be interpreted as evidence in favor of the hypothesis that historically, urban agglomerations have followed a sequential growth pattern: Within a country, the initially largest city is the first to grow rapidly for some years. At some point, the growth rate of this city slows down and the second-largest city then becomes the fastest-growing one. Eventually, the third-largest city starts growing fast as the two largest cities slow down, and so on.     

Predicting benchmarked US state employment data in real time

US payroll employment data come from a survey and are subject to revisions. While revisions are generally small at the national level, they can be large enough at the state level to alter assessments of current economic conditions. Users must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data 5–16 months after the reference period. This article develops a state-space model that predicts benchmarked state employment data in real time. The model has two distinct features: (1) an explicit model of the data revision process and (2) a dynamic factor model that incorporates real-time information from other state-level labor market indicators. We find that the model reduces the average size of benchmark revisions by about 11 percent. When we optimally average the model’s predictions with those of existing models, the model reduces the average size of the revisions by about 14 percent.     

G7 consumption: after the wealth rush

• ? In only one of 12 large advanced economies do we expect consumption to outstrip GDP growth in 2018. As key drivers rotate, the impact of a recovery in real incomes will be dampened by higher oil prices and waning wealth effects .
• ? Policy‐fuelled asset booms sustained the post‐crisis recovery in G7 consumption, though by historical standards the recovery was nothing special. Historically, the G7's average 5‐year recovery from troughs entailed consumption matching GDP growth, but in the five years from 2010 consumption was 0.2 ppt weaker. Its relative strength only picked up from 2015, when boosted by weak oil prices.
• ? Relatively weak G7 consumption growth is likely to continue as key drivers rotate. Strong employment growth and a modest pick‐up in wage inflation will offset waning equity and housing wealth effects.
• ? Near‐term risks are two‐way. An oil‐fuelled inflation surprise could hit consumers, wreck central bank gradualism and reveal balance sheet weaknesses. Currently, however, we see only limited pockets of credit risk and vulnerability to higher rates.
• ? Conversely, there is scope for a credit‐fuelled boost to consumption. G7 household borrowing relative to its trend is arguably close to 40‐year lows, so unless financial deepening has reached a limit, there is scope for increases in borrowing. Furthermore, G7 bank deleveraging could be over, boosting credit supply conditions.
• ? We see two positive longer‐term drivers of the global consumption share: (i) Asian economies will become more consumption‐driven; (ii) Household re‐leveraging offers scope for some debt‐fuelled consumption growth. Offsetting negatives are that demographics, interest rates and asset prices will provide little support