The role of inventories and capacity utilization as shock absorbers

We examine the role of inventories and capacity utilization (of both capital and labor) for the propagation of business cycle fluctuations. We document a new set of facts regarding the U.S. cyclical regularities of inventories and capacity utilization. First, we find that capital utilization and the flows of services from both capital and labor are procyclical, and comove with the holdings of inventories. Second, we find that labor utilization is procyclical as well, but is weakly negatively correlated with inventories. We build a model that accounts for these facts, and also accounts for the stylized inventory facts, i.e., inventory holdings are procyclical, while the inventory-to-sales ratio is countercyclical. The analysis is centered on the effects of two possible shocks: preference (demand) shocks and technology shocks. Our model shows that inventories and the rate of capital utilization are mostly complements, while inventories and the rate of labor utilization are mostly substitutes. It further shows that temporary demand shocks emphasize the role of inventories as being a “shock absorber,” whereas high-persistence demand shocks, as well as technology shocks of any persistence, emphasize the role of inventories as being a complement to consumption.     

Microdata imputations and macrodata implications: Evidence from the Ifo Business Survey

A widespread method for forecasting economic macro level parameters such as GDP growth rates is survey-based indicators that contain early information in contrast to official data. But surveys are commonly affected by nonresponding units, which can cause biased results. Many papers have examined the effect of nonresponse in individual or household surveys, but less is known in the case of business surveys. For this reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the German business cycle. To reflect the underlying latent data generating process, we compare different imputation approaches for longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their implications at the macro level. Finally, we show that the differences between the original and imputed indicators do not lead to substantial changes in the interpretation and the forecasting performance of the indicators.     

UK business cycle synchronization with Germany and the US: New evidence from time–frequency domain and structure of economic growth

This article uses a time–space approach to check the UK business cycle synchronization with Germany and the US. As a novelty, we consider the co-movements in terms of economic growth rate structure. In line with the existing studies, we discover that the UK business cycle is more synchronized with the US then with Germany, and that the co-movements have intensified lately. We also show that co-movements are reduced in terms of business cycle structure and are time–frequency-dependent. Finally, we point out that the UK business cycle became more synchronized with the US cycle given the contribution of investments and external balance to the real growth rate.     

Nowcasting the French index of industrial production: A comparison from bridge and factor models

Governments and central banks need to have an accurate and timely assessment of indicators for the current month, as this is essential for providing a reliable and early analysis of the current economic situation. The index of industrial production (IIP) is probably the most important and widely analyzed monthly indicator, given the relevance of the manufacturing activity as a driver of the whole business cycle. This paper presents a series of models conceived to forecast the current French monthly IIP, based on regression models and dynamic factor models. The combination of these two approaches allows selecting economically relevant explanatory variables among a large data set. In addition, a rolling forecast study is carried out to assess the forecasting performance of the estimated models, using predictive ability and model confidence set tests. This latter allows getting several models displaying equivalent forecasting performance and therefore gives robustness to the forecasting exercise rather than to base the forecasting analysis only on one model.     

Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data

In this paper, we develop a methodology for forecasting key macroeconomic indicators, based on business survey data. We estimate a large set of models, using an autoregressive specification, with regressors selected from business and household survey data. Our methodology is based on the Bayesian averaging of classical estimates method. Additionally, we examine the impact of deterministic and stochastic seasonality of the business survey time series on the outcome of the forecasting process. We propose an intuitive procedure for incorporating both types of seasonality into the forecasting process. After estimating the specified models, we check the accuracy of the forecasts.     

Think national,forecast local: a case study of 71 German urban housing markets

In this article, we examine whether the local indicators are able to predict the city-level housing prices and rents better than national indicators. For this purpose, we assess the forecasting ability of 126 indicators and 21 types of forecast combinations using a sample of 71 large German cities. There are several predictors that are especially useful, namely price-to-rent ratios, national-level business confidence, and consumer surveys. We also find that combinations of individual forecasts are among the top forecasting models. On average, the forecast improvements attain about 20%, measured by a reduction in root mean square error, compared to the naive models.     

A general to specific approach for constructing composite business cycle indicators

Combining economic time series with the aim to obtain an indicator for business cycle analyses is an important issue for policy makers. In this area, econometric techniques usually rely on systems with either a small number of series, N, or, at the other extreme, a very large N. In this paper we propose tools to select the relevant business cycle indicators in a “medium” N framework, a situation that is likely to be the most frequent in empirical works. An example is provided by our empirical application, in which we study jointly the short-run co-movements of 24 European countries. We show, under not too restrictive conditions, that parsimonious single-equation models can be used to split a set of N countries in three groups. The first group comprises countries that share a synchronous common cycle, a non-synchronous common cycle is present among the countries of the second group, and the third group collects countries that exhibit idiosyncratic cycles. Moreover, we offer a method for constructing a composite coincident indicator that explicitly takes into account the existence of these various forms of short-run co-movements among variables.     

Inventories and the Business Cycle*

This paper examines the relationship between the inventory cy and the business cycle using both macroeconomic and survey da It is argued that over the past decade and a half, the changes inventory management have reduced the amplitude of the inventc cycle. The paper also argues that the behaviour of inventories consistent with demand shocks being an important source of business cycle fluctuations.     

Does confidence data help forecast business cycles? New evidence from Canada

This paper assesses the contribution of confidence – or sentiment – data for predicting Canadian economic slowdowns. A probit framework is applied to an indicator of the status of the Canadian business cycle produced by the OECD. Explanatory variables include all available Canadian data on sentiment (from four distinct surveys) as well as macroeconomic and financial data. Sentiment data are introduced either as individual variables, as simple averages (such as confidence indices) and as confidence factors extracted from larger datasets containing all available sentiment data. Results indicate that the full potential of confidence data for forecasting Canadian business cycles obtains when factor models are used and all confidence data are utilized.     

Real-time forecasting US GDP from small-scale factor models

We show that the single-index dynamic factor model developed by Aruoba and Diebold (Am Econ Rev, 100:20–24, 2010) to construct an index of the US business cycle conditions is also very useful to forecast US GDP growth in real time. In addition, we adapt the model to include survey data and financial indicators. We find that our extension is unequivocally the preferred alternative to compute backcasts. In nowcasting and forecasting, our model is able to forecast growth as well as AD and better than several baseline alternatives. Finally, we show that our extension could also be used to infer the US business cycles very precisely.     

Leading indicators of non-performing loans in Greece: the information content of macro-, micro- and bank-specific variables

We examine the information content of a unique set of macroeconomic, bank-specific, market and credit registry variables as regards their ability to forecast non-performing loans using a panel data set of nine Greek banks. We distinguish between business, consumer and mortgage loans and investigate their differences with respect to their optimal predictors. The quasi-AIM approach (Carson et al. in Int J Forecast 27:923–941, 2010) is utilized in order to take into account heterogeneity across banks and minimize estimation uncertainty. In addition, we calculate a number of forecasting measures in order to take into account the policy makers’ preferences. We find that market variables, specifically the supermarket sales, confidence indices for the services and construction sector and the business sentiment index represent good forecasting variables for most categories of NPLs. In addition, industrial production is the optimal predictor for consumer NPLs and imports for business NPLs. Finally, bank-specific variables represent top-performing leading indicators for business NPLs. Our results have significant implications for stress-testing credit risk in a top-down manner and for supervisory and macro-prudential policy design.     

Synchronization and diversity in business cycles: a network analysis of the European Union

In this article, we use a correlation matrix and its internal networks to analyse business cycle synchronization across Europe since 2000. This methodology allows us to summarize individual country interactions and co-movements while also capturing the existing heterogeneity of connectivity within the European economic system. Our results indicate that synchronization of the euro zone countries remained stable from 1999 until the current financial crisis, after which co-movements increased sharply and synchronization rose to the highest in the time sample. By endogenously identifying clusters of countries with close connections in their business cycle, we also refute the commonly accepted notion of identifiable core and peripheral euro zone countries.     

Foreign direct investment and business cycle co-movements: The panel data evidence

The previous literature has largely overlooked the possible channels through which foreign direct investment (FDI) might influence business cycle synchronization. In this study we analyze the linkages that exist among FDI, trade and industrial dissimilarity in relation to business cycle co-movements using a panel data set taken from 77 pairs of developed countries. The error component three-stage least squares (EC3SLS) estimates from a simultaneous equations model with panel data are shown to be superior to the estimates obtained from single equation models or simultaneous equations models with cross-sectional data. Our results indicate that FDI serves as a channel of international business cycle transmission that is equally important as the channels of trade and monetary policy. On the contrary, industrial dissimilarity is identified as having an indirect impact on the business cycle correlation through trade and FDI. Furthermore, our findings suggest that in our sample FDI is of the horizontal type and tends to substitute for trade.     

The Effects of Inflation and the Business Cycle on Revisions of Macroeconomic Data

Although national accounts data provide the most comprehensive overview of economic activity, preliminary estimates are subject to much revision before they are regarded as reliable indicators. Oddly enough, the market acts on the preliminary estimates as though they were final and complete. Even though there exists a considerable international literature on the statistical properties of these revisions, little attention has been devoted to the effects of inflation and the business cycle on the size and direction of these early revisions. The aim of this article is to provide the first known examination of these effects. The findings in this article suggest that preliminary estimates are more reliable in periods of low inflationary economic growth.     

经济波动同步性、需求冲击与供给冲击——来自海湾国家的实证分析

本文运用结构向量自回归模型,将海湾六国产出的同步波动性和遭受冲击的对称性联系起来。研究发现,1977-2006年间海湾六国遭受需求冲击的对称程度要高于供给冲击,对称性的需求冲击对维持六国经济周期的同步性既显著又重要,而供给冲击的对称性对维持六国经济周期的同步性几乎没有作用;另外,本文没有发现供给和需求冲击的对称性对经济周期的同步波动有滞后影响。本文实证研究的结果对于海湾六国未来单一货币的汇率制度选择具有重要的参考意义。     

Box-Jenkins Methoden — Alternative Verfahren zur Prognose ökonomischer Zeitreihen

Summary In this paper we try to clarify whether the use ofBox-Jenkins methods would have improved the forecasting performance in Austria during the recession of 1975. For this purpose we estimate ARIMA models for gross national product, private consumption, investment in plant and equipment, and inventory investment. We then compare the forecasts derived from these models with the results of more convential forecasting techniques. It can not be expected that Box-Jenkins methods predict a business cycle turning point. But, as soon as the recession was under way Box-Jenkins methods were faster in adapting to the new situation than conventional forecasting techniques. We found that the accuracy of Box-Jenkins predictions depends to a large extent on the length of the forecasting horizon. Our results suggest that the forecasting horizon should not exceed one year. All in all, Box-Jenkins methods applied together with the forecasting techniques already in use could further improve the forecasting performance.     

Understanding China’s official statistics

In response to the three influential doubts about government statistics, this article expounds the differences between relevant common economic statistical indicators and the national account indicators that reflect demand structure and national income distribution structure. The comparisons include the following aspects: the differences between household consumption expenditure from household survey and from expenditure-based GDP; the differences between total retail sales of consumer goods and final consumption expenditure from expenditure-based GDP; the differences between government expenditure from government fiscal statistics and government consumption expenditure from expenditure-based GDP; the differences between total investment in fixed assets from investment statistics and gross fixed capital formation from expenditure-based GDP; the differences between inventories and change in inventories from expenditure-based GDP; the differences between foreign trade balance from customs statistics and net exports of goods and services from expenditure-based GDP; and the differences between household disposable income from the household survey and from the Flow of Funds Table, etc. In addition, this article answers the questions proposed by the three doubts correspondingly.     

Time–frequency relationship between US output with commodity and asset prices

Commodity and asset prices have a well-documented effect on economic growth as manifested through various channels. At the same time, the business cycle influences the commodity and asset prices. Whereas empirical evidence on the effect of commodity and asset prices on the long-run economic growth is ambiguous, most of the previous researches highlight a positive correlation in the short run. The aim of this article is to disentangle the short- and long-run co-movements between US historical business cycles and commodity and asset prices over the period 1859–2013. For this purpose, we use a time–frequency approach and we test the historical influence of oil, gold, housing and stock prices over the output growth. In contrast to other studies, we control for the effect of other prices and monetary conditions, using the wavelet partial coherency. In line with the previous works, we discover that co-movements between economic growth and commodity and assets prices manifest especially in the short run. We also find that stock returns and housing prices have a more powerful effect on the US economic growth rate than the oil and gold prices. The long-run co-movements are documented especially around the World War II. Finally, when controlling for the influence of the interest rate, inflation and other commodity and asset prices, co-movements become weaker in the short run. In general, the oil and housing prices lead the GDP growth, the US output leads the gold prices, while there is no clear causality direction between business cycle and stock prices.     

Moments of cross-sectional stock market returns and the German business cycle

Based on monthly data covering the period from 1987 to 2021, we analyse whether cross-sectional moments of stock market returns may provide information about the future position of the German business cycle. We apply in-sample forecasting regressions with and without leading indicators as control variables, pseudo-out-of-sample exercises, autoregressive distributed lag models, and impulse-response functions estimated by local projections. We find in-sample predictive power of the first and third cross-section moments for the future growth of industrial production, even if one controls for well-established leading indicators for the German business cycle. Out-of-sample tests show that these variables reduce the relative mean squared error compared with benchmark models. We do not find a long-run relation between the moment series and industrial production. The dynamic response of industrial production to a shock on the cross-section moments is in line with the other results.     

On testing for symmetry in business cycles

Business cycle forecasting has become an important part of short and medium term economic planning. Such forecasting, however, is often very intricate, as business cycles are not at all periodic, just recurrent. Furthermore, they often include irregular timing and varying amplitudes. When patterns and relationships are very irregular there are no simple reliable business cycle forecasting procedures. In practice there is, somewhere, a limit for business cycle predictability, and it is often worthwhile to examine empirically the various theoretical regularity assumptions. One important regularity issue concerns the business cycle symmetry assumption. The present paper empirically tests the hypothesis of symmetry around business cycle turning points in some economic time series. Two test procedures are applied. One is based on the analysis of transition probabilities between expansion and recession regimes. The second procedure tests symmetry versus asymmetry through skewness statistics. The analysis is based on detrending through the use of linear deterministic trends as well as by Beveridge-Nelson decompositions.