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The purpose of the paper is to estimate wheat productivity in Bangladesh and forecast the future expected population and food requirements in the country by 2010.

This paper reaches the objectives using total factors productivity approach, Box Jenkins approach, and sensitivity analysis for wheat farms in the country. The study used data on wheat during 1972‐2002.

In the existing situation, the national average level wheat yield was 1.9 MT/ha that was lower than any other stations. The reasons are late sowing, coupled with lack of seed quality, excess moisture at sowing, lack of fertilizer at reasonable price and timeliness at the farmers' level, and lack of capital. The total productivity grew at an average annual rate of 1.35 percent.

The results show that the Bangladeshi government could increase the domestic wheat supply by 56.84, 115.79, 247.37, and 321.58 percent depending, respectively, on the applied model I‐IV, that is much higher than the existing level of production.

This paper brings together diverse views and fusing them together providing a future path for research and taking suitable policy for wheat production to meet the demand for food.

The authors derive the probability distribution of the net present value of a project under the quite general assumption that the cash flows follow either an autoregressive moving average process or an integrated autoregressive process. Examples are presented which serve to both illustrate the application of the results as well as to underscore how to use utility functions for decision making, how to determine a project's Internal Rate of Return, and the dynamic resolution of uncertainty.

In this study, the relative accuracy of four well known methods for forecasting are compared The methods are applied to the time series of earnings per share for a random sample of United States corporations over a lengthy period of time. All the time series exhibit both period‐to‐period movements and seasonal fluctuation. The four models are, (1) Holt‐Winters multiplicative exponential smoothing model, (2) univariate Box‐Jenkins model, (3) linear autoregression of data seasonally adjusted by the Census II–XII method, and (4) linear autoregression of the data seasonally adjusted by the X11‐ARIMA method. The study of financial data of this type is important because (1) these data exhibit time series properties of trend, seasonality, and cycle, (2) earnings per share forecasts are important for purposes of financial planning and investment; and (3) previous studies of this nature were not as exhaustive in terms of the statistical analysis of the results

To sustain competitiveness, construction plant manufacturers must be able to accurately forecast market sales and moreover, their anticipated percentage of those sales. This paper addresses the former of these forecasts through the development of a multivariate time series model. Specifically, an autoregressive moving average (ARMA) time series model (otherwise known as the Box‐Jenkins approach) is constructed using economic data relating to a 15‐year period (1985–99). It is identified that population (millions); housing completions total (millions), total building repair and maintenance (billions Euro at 1998 prices, where 1 Euro = £0.6776) and gross domestic product (millions at market prices) are able to accurately predict plant sales. The performance statistics show the derived time series model to be very accurate in modelling the dependent variable [mean absolute deviation (MAD) = −87.04 and root mean square error (RMSE) = 1404.05]. By observing economic interactions and influences on sales, it is envisaged that the manufacturer can adjust their production output to match demand. Based upon the model produced, a forecast of machine sales for year 2000 is made and indicates that sales numbers look set to reduce to 15 482 units. The paper concludes with direction for future research work that will aim to model machine park, the influence of grey machines and individual plant items.

This study aims to attempt to examine whether the increase in hotel room capacity in the USA had a significant impact on nationwide aggregated weekly revenue per available room (RevPAR) during the recession of 2007-2009 and forecast average RevPAR, Occupancy and Average Daily Rate (ADR) for 2013 and 2014.

Using Autoregressive Integrated Moving Average with Intervention analysis technique, this study examined the significance of the fluctuations in weekly RevPAR, room capacity and market demand through the recent recession and forecasted hotel performance for 2013 and 2014.

The results of time series analysis suggest that the fast growth of room capacity during the recession was one of the main causes of the decrease in RevPAR. The 9,878 more than expected increase in average weekly number of rooms probably caused at least $0.10 more than expected decrease in average weekly RevPAR. The findings of this study also suggest that the US lodging industry has been facing more severe oversupply since the recession and fully rebound of RevPAR cannot be expected in the very near future.

The findings of this study will help stakeholders make more informed decisions to cope with possible future economic downturns. By quantifying the capacity increase and forecasting future market demand, this study provides hotel investors with empirical evidence on the overdevelopment and insights into expected overall hotel performance in next two years. This study has also discussed the cyclical patterns of hotel development during the past two recessions.

By identifying overdevelopment as one of the main causes of RevPAR decrease during the recession, this study contributes to the literature by adding an alternative explanation of RevPAR fluctuations and deepens the understanding of the adverse effects overdevelopment has on the lodging industry. The findings of this study will help hotel investors develop more informed future expansion plans.

The purpose of this paper is to generate three types of forecasts, namely, historical, ex‐post and ex‐ante, using the world famous Box‐Jenkins time series models for motor, mash and mung prices in Bangladesh.

The models on the basis of which these forecasts have been computed were selected by six important information criteria such as Akaike's Information Criterion (AIC), Schwarz's Bayesian Information Criterion (BIC), Theil's R², Theil's R², SE(σ) and Mean Absolute Percent Errors (MAPEs). In order to examine the forecasting performance of the selected models, three types of forecast errors were estimated, i.e. root mean square percent errors (RMSPEs), mean percent forecast errors (MPFEs) and Theil's inequality coefficients (TICs).

The estimates suggest that in most cases the forecasting performances of the models in question are quite satisfactory.

The models developed in this paper can be used for policy purposes as far as price forecasts of the commodities are concerned.

The term “economic growth” refers to the increase of real gross national product or gross domestic product or per capita income. National income or else national product is usually expressed as a measure of total added value of a domestic economy known as gross domestic product (GDP). Generally, GDP measures the value of economic activity within a country during a specific time period. The current study aims to find the most suitable model that adjusts on a time-series data set using Box-Jenkins methodology and to examine the forecasting ability of this model. The analysis used quarterly data for Greece from the first quarter of 1995 until the third quarter of 2019. Nonlinear maximum likelihood estimation (maximum likelihood-ML) was applied to estimate the model using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm while covariance matrix was estimated using the negative of the matrix of log-likelihood second derivatives (Hessian-observed). Forecasting of the time series was achieved both with dynamic as well as static procedures using all forecasting criteria.

The purpose of this paper is to examine the nature of Singapore's private housing market with respect to its price movement using time series models.

This paper analyses the price dynamics in the Singapore private housing market using the integrated autoregressive‐moving average modeling coupled with outlier detection and autoregressive conditional heteroskedasticity modeling techniques.

The paper finds that private house prices are better modeled as an ARIMA (1, 1, 0) model with corresponding dummy variables. This suggests that housing prices may be characterized as the combination of a stationary cyclical component and a non‐stationary stochastic growth component over the past almost three decades. This affirms that the Singapore's private housing market is characterised by the weak‐form inefficiency.

The results show that even though ARIMA with dummy variables performs better to ARIMA with ARCH in dynamic performance, there is only marginal improvement on the original model. This suggests that the method for selecting intervention variables in the ARIMA modeling is worth further research with the aim of improving its predictive ability.

This paper incorporates the detection of outliers and intervention procedure in the modeling in order to analyse the impacts of extraordinary events such the recent Asian financial crisis and excessive market speculation on property prices and take them into consideration in forecasting price changes.

This paper aims, first, to advance the current understanding about the impact of innovation in non-traditional exports, and, second, to provide insights about the structure of emergent economies often not regarded by traditional innovation and export theories.

A longitudinal analysis using panel data based on Box Jenkins’ theory was conducted, so to identify statistically significant variables on export performance, regarding expenditure on research, development and innovation (R&D + I) activities, ICT and specialized training and formation.

This study suggests the need to design public policies aimed at stimulating innovation in potential export sectors, as a mechanism for competitive development and growth in emergent economies such as Colombia.

The introduction of innovations in goods and services exports has become more important in economies, such as the Colombian ones, where globalization openness processes force to establish minimum competitiveness levels regarding the international standards.

The purpose of this study is to examine how the recent recession affected Iowa's gaming industry by analyzing gaming volumes before and through the recession.

This study used autoregressive integrated moving average (ARIMA) with intervention analysis to examine Iowa statewide aggregated monthly slot coin-in, table drop, and admission from December 2001 through June 2012.

The results of analyses show that: slot coin-in was not affected by the recession; table drop was slightly affected, but started to recover in late 2010; and monthly admission was not affected by the recession, and showed a significant increase after the recession. The results also indicate that the decrease in table drop in Iowa casinos represented only a very small amount of state gaming revenue in 2008. Therefore, the findings of this study suggest that Iowa's gaming volume was not significantly affected by the recent recession. In other words, Iowa's gaming industry is still recession-proof.

Current economic conditions suggest that the threat of a double-dip recession is quite real. The findings of this study are expected to help casino managers in Iowa understand how non-destination casinos behaved differently through the recession and strategically plan for a possible future economic downturn. In fact, the significant increase of monthly admission during the last recession implies that the Iowa gaming industry has actually benefited from the recession by accommodating more patrons. Therefore, to capitalize on the next recession, Iowa's casino operators should consider reducing the number of table games and increasing the number of slot machines to accommodate more slot players and reduce operating costs.

Most existing gaming-related research focuses on gaming destinations such as Las Vegas and Atlantic City. No known study on gaming volume in non-destination gaming markets has been identified. By examining Iowa's gaming volume through the recession, this study provides initial empirical evidence of the impact of recession on non-destination gaming markets.