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This article is a study of the distributed lag relationship between completions and starts of private dwellings in the UK and extends some results previously published in this journal. The main finding confirms that a proper study of builders' behaviour, as manifested in the distributed lag relationship between completions and starts, must allow for both disequilibrium effects and time varying lag structures. However, in examining the economic variables that modified builders' behaviour (and hence the lag structure) it was found that both the rate of house price inflation and the level of building society mortgage advances had an important influence. An increase in both caused builders, in the expectation of easier sales, to quicken their rates of completion. Additional plausible influences — like the level of construction activity in the public sector were, however, not supported by the data.

The adoption of a model‐building approach to marketing is today inevitable, due to improvements in hardware and software and the increased professionalisation of marketing and its techniques. Aggregate response models are focused upon, particularly the issues of which responses are realistic and should be modelled, how the response can be expressed and how a choice can be made between options available. The traditional model‐building process is described, and the inclusion of correct variables found to be critical, the primary means of doing this being statistical analysis. Simple expressions perform as effectively as more complex ones, and should be used if able to give operationally meaningful results. Cross‐correlation analysis and biased estimation techniques provide good guides to usable variables and their effects.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

The 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.

A 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.

This 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.

The 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.

This 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.

The purpose of this paper is to analyze the association between banks’ advertising expenses and accounting measures of income and profitability for banking sector.

In this study the authors have used distributed lag models to investigate the association between advertising expenses and banks’ financial performance. To investigate the long-term effect of advertising expenses on financial performance of banking sector Koyck’s distributed lag models have been used.

The results confirm a significant and positive association between advertising expenses and financial performance. Besides its positive effect, the authors provide a basis for detecting the extent to which advertising has long-term benefits. The results show a positive association between advertising expenses and financial performance that extend that extends over time, thereby suggesting that advertising expenses should be capitalized and then amortized instead of being incurred as an expense immediately.

Although there are lots of studies about advertising and its effect on financial position of firms, research about advertising effects on financial performance of banking sector is very scarce. Therefore, this study has a potential to shed light on research about marketing aspect of financial sector. Besides, empirical results show a positive association between advertising expenses and financial performance that extend that extends over time (interest income, total operating income and return on assets), thereby suggesting that advertising expenses should be capitalized and then amortized instead of being incurred as an expense immediately. To sum it all, the paper finds an evidence for banking sector that advertising inholds “future economic benefits” which is the key criterion necessary for asset recognition.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

Delivery punctuality is essential in supply chain management, yet the cost of untimely delivery is usually assumed to be given or based on intuition and not quantified by facts.

The authors used a data set containing detailed transaction data for a nine-year period on orders and deliveries of sport goods. The methodology is based on applying a polynomial distributed lag model to longitudinal data on supply chain transactions.

The results indicate that small delivery delays up to two weeks decrease the sales by maximum 10% during a period of 3–4 weeks. Longer delays, up to 45 days, have a larger negative effect on sales, which can also last longer. For this case company, the estimated lost sales due to late deliveries (=5 days) were 5.1% of the delivery value. The longer delays got, the large the cost was: delays at least 45 days long were the most costly causing almost 40% of the estimated lost sales.

This study offers a methodology for quantifying lost sales due to delivery delays and estimating how long the poor delivery performance affects retailers' order behaviour.

The results give a quantitative decision-making tool for supply chain managers to estimate the profitability of investments in the supply chain performance, especially on improving punctuality.

This paper quantifies the effects of recent Federal Reserve interest rate changes, specifically recent hikes and cuts in the federal funds rate since 2015, on Midwest farmland values.

The authors apply three autoregressive distributed lag (ARDL) models to a panel data of state-level farmland values from 1963 to 2018 to estimate the dynamic effects of interest rate changes on the US farmland market. We focus on the I-states, Lakes states and Great Plains states. The models in the study capture both short-term and long-term impacts of policy changes on land values.

The authors find that changes in the federal funds rate have long-lasting impacts on farmland values, as it takes at least a decade for the full effects of an interest rate change to be capitalized in farmland values. The results show that the three recent federal funds rate cuts in 2019 were not sufficient to offset the downward pressures from the 2015–2018 interest rate hikes, but the 2020 cut is. The combined effect of the Federal Reserve's recent interest rate moves on farmland values will be positive for some time starting in 2022.

This paper provides the first empirical quantification of the immediate and long-run impacts of recent Federal Reserve interest rate moves on farmland values. The authors demonstrate the long-lasting repercussions of Federal Reserve's policy choices in the farmland market.

The distributed lag relationship between completions and starts of private dwellings in the U.K. has been studied by Whitehead (1971, 1974) and Hadjimatheou (1976), the typical relationship estimated by them being of the form