Lag Structure Research Articles

Profitability Ratios in the Early Stages of a Startup

This study develops a mathematical framework to analyze the time series of profitability ratios in the early stages of a startup. It is assumed that the expenditure of the startup grows at a steady rate and generates a proportionally identical flow of revenue in each period. The profitability in terms of the internal rate of return (IRR) and the lag structure of revenue flows are assumed constant over time in describing the adjustment process towards the steady state. The startup is assumed to expense in each period a constant part of periodic expenditure and beginning-of-the-period assets. The adjustment processes of three kinds of profitability ratios are investigated: return on investment ratio, profit margin (as percent of net sales), and (traditional) cash-flow margin (as percent of net sales). It is shown that IRR, growth, expense rate, and lag structure strongly affect the early time-series behavior of profitability ratios. Thus, in the early years, due to unstable adjustment processes, profitability ratios are unable to reflect profitability (IRR) properly and can give distorted signals of the performance of a startup. These findings are supported by numerical analyses with the parameters estimated for a sample of 2608 Finnish startups classified into five clusters.

Bootstrap lag selection in DSGE models with expectations correction

A well known feature of DSGE models is that their dynamic structure is generally not consistent with agents’ forecasts when the latter are computed from ‘unrestricted’ models. The expectations correction approach tries to combine the structural form of DSGE models with the best fitting statistical model for the data, taken the lag structure from dynamically more involved state space models. In doing so, the selection of the lag structure of the state space specification is of key importance in this framework. The problem of lag selection in state space models is quite an open issue and bootstrap techniques are shown to be very useful in small samples. To evaluate the empirical performances of our approach, a Monte Carlo simulation study and an empirical illustration based on U.S. quarterly data are provided.

Does a lag-structure of temperature confound air pollution-lag-response relation? Simulation and application in 7 major cities, Korea (1998–2013)

BackgroundTemperature must be controlled when estimating the associations of short-term exposure to air pollution and mortality. Given that multi-country studies have implied temperature has lagged effects, we aim to explore confounding by temperature-lag-response and investigate PM10-lag-mortality relation in 7 cities, Korea. MethodsIn a simulation study, we compared the performance of different methods to control for: the same day temperature, a lagged temperature and distributed lags of temperature. In a real data study, we explored PM10-lag-mortality relation in 7 cities using these different methods. ResultsWe confirmed that a model with insufficient control of temperature offers a biased estimate of PM10 risk. The degree of bias was from −82% to 95% in simulation settings. A real data study shows estimates among different models by temperature adjustments and PM10 lag variables ranging from −0.3% to 0.4% increase in the risk of all-cause mortality, with a 10μg/m3 increase in PM10. Controlling for temperature as distributed lags for 21 days provided 0.25% (95% CI: 0.1, 0.4) increase in the risk of all-cause mortality. ConclusionsA lag structure of temperature can confound the air pollution-lag-response relation. Temperature-lag-response relation should be evaluated when estimating air pollution-lag-response relation. As a corollary, air pollution and temperature risk in mortality can be estimated using the same regression model.

Hedge fund managers and deceit: is the accusation of performance manipulation valid?

PurposeThe purpose of this paper is to examine whether there is significant evidence that hedge fund managers engage in deceptive manipulation of their reported performance results.Design/methodology/approachA model of hedge fund performance has been developed using standard regression analysis incorporating dependent lagged variables and an autoregressive process. In addition, the extreme bounds analysis technique has been used to examine the robustness and sensitivity of the explanatory variables. Finally, the conditional influence of the global stock market’s returns on hedge fund performance and the conditional return behavior of the Hedge Fund Index’s performance have been explored.FindingsThis paper begins by identifying a model of hedge fund performance using passive index funds that is well specified and robust. Next, the lag structure associated with hedge fund returns has been examined and it has been determined that it seems to take the hedge fund managers two months to integrate the global stock market’s returns into their reported performance; however, the lagged variables were reduced from the final model. The paper continues to explore the smoothing behavior by conditioning the dependent lagged variables on positive and negative returns and find that managers are conservative in their estimates of positive performance events, but, when experiencing a negative result, they seem to attempt to rapidly integrate that effect into the return series. The strength of their integration increases as the magnitude of the negative performance increases. Finally, the performance of returns for both the Hedge Fund Index and the passive indices were examined and no significant differences between the conditional returns were found.Research limitations/implicationsThe results of this analysis illustrate that hedge fund performance is not all that different from the performance of passive indices included in this paper, although it does offer investors access to a unique return distribution. From a management perspective, we are reminded that we need to be cautious about hastily arriving at conclusions about something that looks different or feels different from everything else, because, at times, our preconceived notions will cause us to avoid participating in something that may add value to our organizations. From an investment perspective, sometimes having something that looks and behaves differently from everything else, improves our investment experience.Originality/valueThis paper provides a well-specified and robust model of hedge fund performance and uses extreme bounds analysis to test the robustness of this model. This paper also investigates the smoothing behavior of hedge fund performance by segmenting the returns into two cohorts, and it finds that the smoothing behavior is only significant after the hedge funds produce positive performance results, the strength of the relationship between the global stock market and hedge fund performance is more economically significant if the market has generated a negative performance result in the previous period, and that as the previous period’s performance becomes increasingly negative, the strength of the relationship between the Hedge Fund Index and the global stock market increases.

Revisiting the oil price and stock market nexus: A nonlinear Panel ARDL approach

In this paper, we re-examine the relationship between oil price and stock prices in oil exporting and oil importing countries in the following distinct ways. First, we account for possible nonlinearities in the relationship in order to quantify the asymmetric response of stock prices of these two categories to positive and negative oil price changes. Secondly, in order to capture within group differences, we allow for heterogeneity effect in the cross-sections by formulating a nonlinear Panel ARDL model which is the panel data representation of the Shin et al. (2014) model and also analogous to the non-stationary heterogenous panel data model. Thirdly, we evaluate the relative predictability of the linear (symmetric) and nonlinear (asymmetric) Panel ARDL models using the Campbell and Thompson (2008) test. Our results depict that stock prices of both oil exporting and oil importing groups respond asymmetrically to changes in oil price although the response is stronger in the latter than the former. This finding is further corroborated by the out-of-sample forecast results suggesting that the inclusion of positive and negative oil price changes in the predictive model for stock prices will produce better forecast results only for the oil importing countries. Our results are robust to different oil price proxies, lag structure and in-sample periods. Overall, the dichotomy between oil exporting and oil importing countries has implications on oil price-stock nexus.

Ambient Particulate Matter Concentrations and Hospitalization for Stroke in 26 Chinese Cities: A Case-Crossover Study.

Little is known about the short-term health impacts of particulate matter (PM) on stroke in China. We, therefore, conducted a time-stratified case-crossover study to examine the association between ambient PM and hospital admissions for stroke in 26 Chinese cities. We identified hospital admissions for stroke by using electronic hospitalization summary reports from January 1, 2014 through December, 31 2015. We applied conditional logistic regression to explore the association between PM and hospital admissions for stroke. We also assessed the effect modification of stroke risk by geographical region, sex, and age. Hospital admissions for ischemic (n=278 980) and hemorrhagic (69 399) strokes were examined separately. For ischemic stroke, both PM2.5 and PM10 had the strongest effect at lag 3 days, with an interquartile range increase in PM2.5 (47.5 μg/m3) and PM10 (76.9 μg/m3) significantly associated with a 1.0% (95% confidence interval, 0.7%-1.4%) and 0.8% (95% confidence interval, 0.3%-1.3%) increase in admissions for ischemic stroke, respectively. In northern China where PM pollution is more severe, the risk estimates for both PM2.5 and PM10 were larger than those in southern China in all lag structures. An interquartile range increase in PM2.5 and PM10 in northern China corresponded to a 1.0% (95% confidence interval, 0.7%-1.4%) and 0.7% (95% confidence interval, 0.3%-1.2%) increase in ischemic stroke admissions at lag 3 days, respectively. For hemorrhagic stroke, no significant association was observed with PM in the 26 cities. This study suggests that short-term elevations in PM may increase the risk of ischemic but not hemorrhagic stroke. The associations of PM with ischemic stroke are stronger in northern China than in the south.

Oil prices, socio-political destabilization risks, and future energy technologies

Our review of some modern trends in the development of energy technologies suggests that the scenario of a significant reduction of the global oil demand can be regarded as quite probable. Such a scenario implies a rather significant decline of oil prices. The aim of this article is to estimate the sociopolitical destabilization risks that such a decline could produce with respect to oil exporting economies. Our analysis of the relationship between changes in oil prices and political crises in these economies shows a large destabilizing effect for price declines in the respective countries. The effect is highly non-linear, showing a power-law type relationship: oil price changes in the range higher than $60 per barrel only exert very slight influence on sociopolitical instability, but if prices fall below this level, each further decrease by $10 leads to a greater increase in the risks of crises. These risks grow particularly sharply at a prolonged oil price collapse below $40 per barrel, and become especially high at a prolonged oil price collapse below $35 per barrel. The analysis also reveals a fairly short-term lag structure: a strong steady drop in oil prices immediately leads to a marked increase in the risks of sociopolitical destabilization in oil-exporting countries, and this risk reaches critical highs within three years. Thus, the possible substantial decline of the global oil demand as a result of the development of the energy technologies reviewed in the first section of the present article could lead to a very substantial increase in the sociopolitical destabilization risks within the oil exporting economies. This suggests that the governments, civil societies, and business communities of the respective countries should amplify their effort aimed at the diversification of their economies and the reduction of their dependence on the oil exports.

What led to the establishment of a rail-oriented city? Determinants of urban rail supply in Tokyo, Japan, 1950–2010

This study analyzed the determinants of urban rail supply in Tokyo using time series data for the postwar period. It modeled urban rail supply, measured by vehicle-kilometers of the urban rail service, incorporating demand for urban rail travel, urban rail travel speed, conditions of alternative transportation modes, land use patterns, and socioeconomic conditions as explanatory variables. The model adopted a lag structure for urban rail supply in line with the planning horizon. It was then estimated using the Bayesian model averaging approach, which provided robust estimation results based on our multivariate time series data. The results showed that investments in the urban rail network in Tokyo were primarily driven by the increase in demand for urban rail travel. In effect, demand growth in the Tokyo rail network has clearly translated to supply growth through the planning process; this induced supply has worked as one of the critical components in the establishment of a rail-oriented transportation system in Tokyo. Additionally, the negative effects of bus/tram travel speed on urban rail supply were estimated.

On the structure of dynamic principal component analysis used in statistical process monitoring

When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time-dependent data. In DPCA the input matrix is augmented by adding time-lagged values of the variables. In building a DPCA model the analyst needs to decide on (1) the number of lags to add, and (2) given a specific lag structure, how many principal components to retain. In this article we propose a new analyst driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using simulated vector autoregressive and moving average data, and tested on Tennessee Eastman process data.

A Resilient, Non-neuronal Source of the Spatiotemporal Lag Structure Detected by BOLD Signal-Based Blood Flow Tracking.

Recent evidence has suggested that blood oxygenation level-dependent (BOLD) signals convey information about brain circulation via low frequency oscillation of systemic origin (sLFO) that travels through the vascular structure (“lag mapping”). Prompted by its promising application in both physiology and pathology, we examined this signal component using multiple approaches. A total of 30 healthy volunteers were recruited to perform two reproducibility experiments at 3 Tesla using multiband echo planar imaging. The first experiment investigated the effect of denoising and the second was designed to study the effect of subject behavior on lag mapping. The lag map's intersession test-retest reproducibility and image contrast were both diminished by removal of either the neuronal or the non-neuronal (e.g., cardiac, respiratory) components by independent component analysis-based denoising, suggesting that the neurovascular coupling also comprises a part of the BOLD lag structure. The lag maps were, at the same time, robust against local perfusion increases due to visuomotor task and global changes in perfusion induced by breath-holding at the same level as the intrasession reliability. The lag structure was preserved after time-locked averaging to the visuomotor task and breath-holding events, while any preceding signal changes were canceled out for the visuomotor task, consistent with the passive effect of neurovascular coupling in the venous side of the vasculature. These findings support the current assumption that lag mapping primarily reflects vascular structure despite the presence of sLFO perturbation of neuronal or non-neuronal origin and, thus, emphasize the vascular origin of the lag map, encouraging application of BOLD-based blood flow tracking.

Dynamic lag structure of deposits and loans interest rates and business cycles formation

PurposeThis paper aims to study the structural dynamic behaviour of the depositors, banks and investors and the role of banks in the business cycles. The authors test the hypothesis: do banks’ behaviour make oscillations in the economy via interest rate?Design/methodology/approachThe authors dichotomized banking activities into two markets: deposit and loan. The first market forms deposit interest rate, and the second market forms credit interest rate. The authors show that these two types of interest rates have non-synchronized structures, and that is why money sector fluctuation starts. As a result, the fluctuation is transferred to the real economy through saving and investment functions.FindingsThe empirical results show that in the USA, the banking system creates fluctuations in money and real economy, as well as through interest rates. Short-term interest rates had complex roots in their characteristic, while medium and long-term interest rates, though they were second-order difference equations, had real characteristic roots. However, short-term interest rates are the source of oscillation and form the business cycles.Research limitations/implicationsThe authors tested the hypothesis for USA economy, while it needs to be tested for other economies as well.Practical implicationsThe results show that though the source of fluctuations in the real economy comes from short-term interest rates, medium- and long-term interest rates dampen real economy fluctuations and also work as economic stabilisers.Originality/valueRegarding the applied method, the topic is new.

The performance of one belt and one road exchange rate: Based on improved singular spectrum analysis

“One Belt and One Road” strategy in China is on push of foreign trade openness at northwest, southwest and northeast, absorption of the excess capacity and new support for economic increase. However, the fluctuation in RMB exchange rate with the countries along the road is unstable so related Chinese enterprises will face high risk of exchange rate. Precise explanation or prediction for exchange rate has been the challengeable hop point in the international finance. This paper decomposed the One Belt One Road Exchange Rate Index (OBORR) and the RMB Effective Exchange Rate Index (CNYX) into trend term, market fluctuation term and noise term using improved singular spectrum analysis (SSA). It turns out that the increasing velocity of OBORR is greater than that of CNYX in the long term, and there is dynamic lead–lag structure in the medium term. In the short term, the fluctuation range and frequency of OBORR are greater than those of CNYX, which means there will be more exchange rate risks in One Belt and One Road countries.

Short-term effects of air pollution on daily hospital admissions for cardiovascular diseases in western China

Controlling the confounding factors on cardiovascular diseases, such as long-time trend, calendar effect, and meteorological factors, a generalized additive model (GAM) was used to investigate the short-term effects of air pollutants (PM10, SO2, and NO2) on daily cardiovascular admissions from March 1st to May 31st during 2007 to 2011 in Lanzhou, a heavily polluted city in western China. The influences of air pollutants were examined with different lag structures, and the potential effect modification by dust storm in spring was also investigated. Significant associations were found between air pollutants and hospital admissions for cardiovascular diseases both on dust event days and non-dust event days in spring. Air pollutants had lag effects on different age and gender groups. Relative risks (RRs) and their 95% confidence intervals (CIs) associated with a 10μg/m3 increase were 1.14 (1.04~1.26) on lag1 for PM10, 1.31 (1.21~1.51) on lag01 for SO2, and 1.96 (1.49~2.57) on lag02 for NO2 on dust days. Stronger effects of air pollutants were observed for females and the elderly (≥60years). Our analysis concluded that the effects of air pollutants on cardiovascular admissions on dust days were significantly stronger than non-dust days. The current study strengthens the evidence of effects of air pollution on health and dust-exacerbated cardiovascular admissions in Lanzhou.

Measuring the output gap in Switzerland with linear opinion pools

We use the recently proposed linear opinion pool methodology of Garratt et al. (2014) to construct real-time output gap estimates for Switzerland over the out-of-sample period from 2003:Q1 to 2015:Q4. The model space consists of a large number of bivariate VAR specifications for the output gap and inflation, with each VAR specification using a different estimate of the output gap, lag order, and structural break information. We find that the linear opinion pool performs rather poorly. Real-time estimates of the output gap are no more accurate than those from some simple benchmark models, no more robust to ex post revisions than the real-time estimates of the individual univariate output gaps, and do not produce more accurate forecasts of inflation. The key driver of ‘good’ forecast performance is structural break information. Once the same structural break information is conditioned upon in all prediction models, the gain from averaging over many different pools of models that utilize various output gap estimates or lag structures in the VAR specification is of negligible magnitude.

Association of Asian Dust with daily medical consultations for pollinosis in Fukuoka City, Japan

BackgroundThe objective is to examine the association between AD and the daily number of medical consultations for pollinosis in Fukuoka City.MethodsWe analyzed 65,488 daily medical consultations for pollinosis from 4 clinics in Fukuoka City from February to April, 1989–2012. Time-series analyses were performed to estimate the clinic-specific relative risk (RR) of clinical pollinosis associated with AD, adjusting for airborne pollen, suspended particulate matter (SPM), meteorological and temporal factors. Delayed effects were considered. The association with SPM was also examined given its relationship with AD. The clinic-specific RRs were combined using meta-analytic technique.ResultsAD on the same day (lag 0) and the previous 3 to 5 days (lags 3, 4, and 5) was positively associated with the risk of medical consultations for pollinosis. Clinic visits were 21.5% (95% confidence interval 3.1% – 43.1%) higher when there was an AD event (across lags 0–5). The association with SPM showed comparable lag structure, but with smaller effect estimates. When stratified by the occurrence of AD, the estimated risk increases associated with SPM did not differ between the AD-affected and AD-free days.ConclusionAD is associated with an increased risk of medical consultations for pollinosis in spring. More research is needed to elucidate the roles of air particles with different sizes.

A Note on the “Economic Policy Uncertainty Index”

This paper builds upon the renowned media reports-based “Economic policy uncertainty” (EPU) index and analyzes its Granger-causal interrelationships with consumer confidence and aggregate economic activity. Employing a heterogeneous panel Granger causality test on data from 13 world countries, the authors find that: (1) the analyzed countries are quite heterogeneous in terms of the found causality patterns; (2) the EPU index does not Granger-cause the overall economic activity in the vast majority of the analyzed economies; (3) consumer confidence is confirmed as a significant predictor of aggregate output in all countries; (4) news media uncertainty and consumer confidence largely represent two distinct psychological transmission channels. The evidence presented in this paper suggests that consumer confidence (i.e. first moment, expectation) is considerably more important for the aggregate economic activity than economic uncertainty (i.e. second moment, variance). Two robustness checks are provided (an alternative lag structure of the Granger causality test, and the usage of monthly (industrial production) instead of quarterly (GDP) economic activity data). The obtained inferences are quite robust to both model alternations. Additionally, the leading characteristics of EPU index are found to be less pronounced than those of GARCH uncertainty and forecast disagreement.

Assessing the sources of competitiveness of the US states

PurposeThis study aims to test the emerald model on the regional basis for the identification of the most important sources of competitiveness in the states of the US.Design/methodology/approachUsing the emerald model and its assertions, data are collected over the period 1998-2013 from 47 states in the US. Multiple regression analysis is performed with a lag structure of four, six and eight years as alternative time intervals to explain the dependent variable.FindingsThe empirical results support the emerald model except for its R&D attractiveness dimension in its ability to explain competitiveness in the states of the US. In the longer term (eight-year lag), cluster attractiveness has the highest impact, followed by environmental attractiveness, ownership attractiveness, educational attractiveness and talent attractiveness. Comparison of regression models with different time lags indicates that once the very early phase is over, the impacts of most attractiveness dimensions become rather consistent across time and do not disappear.Originality/valueThe study contributes to the literature on the measurement of regional competitiveness by performing an overall assessment of the emerald model and by analyzing the impacts of the model’s dimensions on competitiveness over time. On the other hand, the identification of the sources of regional competitiveness paves the way for a more efficient allocation of resources regarding policies and improvement programs.

Particulate air pollution and mortality in 38 of China’s largest cities: time series analysis

Objectives To estimate the short term effect of particulate air pollution (particle diameter <10 μm, or PM10) on mortality and explore the heterogeneity of particulate air pollution effects in major...

Abstract P069: Heat, Heat Waves and Hospital Admissions in Indianapolis, Indiana

Background: The association between heat and hospital admissions is well studied, but in Indiana where the regulatory agencies cites lack of evidence for global climate change, local evidence of such an association is critical for Indiana to mitigate the impact of increasing heat. Methods: Using a distributed-lag non-linear model, we studied the effects of moderate (31.7 °C or 90 th percentile of daily mean apparent temperature (AT)), severe (33.5 °C or 95 th percentile of daily mean apparent temperature (AT)) and extreme (36.4 °C or 99 th percentile of AT) heat on hospital admissions (June-August 2007-2012) for cardiovascular (myocardial infarction, myocardial infarction, heart failure) and heat-related diseases in Indianapolis, Indiana located in Marion County. We also examined the added effects of moderate heat waves (AT above the 90 th percentile lasting 2-6 days), severe heat waves (AT above the 95 th percentile lasting 2-6 days) and extreme heat waves (AT above the 99 th percentile lasting 2-6 days). In sensitivity analysis, we tested robustness of our results to 1) different temperature and lag structures and 2) temperature metrics (daily min, max and diurnal temperature range). Results: The relative risks of moderate heat, relative to 29.2°C (75 th percentile of AT), on admissions for cardiovascular disease (CVD), myocardial infarction (MI), heart failure (HF), and heat-related diseases (HD) were 0.98 (0.67, 1.44), 6.28 (1.48, 26.6), 1.38 (0.81, 2.36) and 1.73 (0.58, 5.11). The relative risk of severe heat on admissions for CVD, MI, HF, and HD were 0.93 (0.60, 1.43), 4.46 (0.85, 23.4), 1.30 (0.72, 2.34) and 2.14 (0.43, 10.7). The relative risk of extreme heat were 0.79 (0.26, 2.39), 0.11 (0.087, 1.32), 0.68 (0.18, 2.61), and 0.32 (0.005, 19.5). We also observed statistically significant added effects of moderate heat waves lasting 4 or 6 days on hospital admission for MI and HD and extreme heat waves lasting 4 days on hospital admissions for HD. Results were strengthened for people older than 65. Conclusions: Moderate heat wave lasting 4-6 days were associated with increased hospital admissions for MI and HD diseases and extreme heat wave lasting 4 days were associated with increased admissions for HD.

FUELING JOB ENGINES: IMPACTS OF SMALL BUSINESS LOANS ON ESTABLISHMENT BIRTHS IN METROPOLITAN AND NONMETRO COUNTIES

This study examines the effect of small business loans on subsequent establishment births in U.S. counties. Using an economic growth framework and cross‐sectional empirical model, we test the hypothesis that the establishment birth rate is higher in counties where the level and annual increase in lending is greater, controlling for community‐level characteristics affecting business and economic dynamics. We also consider the long‐term effect of small business lending and focus on establishing the appropriate lag structure. The results indicate that small business lending does generally have a positive effect on the employer establishment birth rate that is strongest in nonmetropolitan (rural) counties. (JEL L26, R11, M13)