Time series interpolation via global optimization of moments fitting

Most time series forecasting methods assume the series has no missing values. When missing values exist, interpolation methods, while filling in the blanks, may substantially modify the statistical pattern of the data, since critical features such as moments and autocorrelations are not necessarily preserved. In this paper we propose to interpolate missing data in time series by solving a smooth nonconvex optimization problem which aims to preserve moments and autocorrelations. Since the problem may be multimodal, Variable Neighborhood Search is used to trade off quality of the interpolation (in terms of preservation of the statistical pattern) and computing times. Our approach is compared with standard interpolation methods and illustrated on both simulated and real data

Time series interpolation via global optimization of moments fitting

Most time series forecasting methods assume the series has no missing values. When missing values exist, interpolation methods, while filling in the blanks, may substantially modify the statistical pattern of the data, since critical features such as moments and autocorrelations are not necessarily preserved. In this paper we propose to interpolate missing data in time series by solving a smooth nonconvex optimization problem which aims to preserve moments and autocorrelations. Since the problem may be multimodal, Variable Neighborhood Search is used to trade off quality of the interpolation (in terms of preservation of the statistical pattern) and computing times. Our approach is compared with standard interpolation methods and illustrated on both simulated and real data

The determinants and impact of agricultural credit on Vietnam agricultural performance : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

The Vietnamese agricultural sector has experienced remarkable progress since the 1986 Renovation reform that transformed Vietnam from a nation on the brink of famine into one of the biggest agricultural exporters worldwide. Despite undeniable achievements over the past 30 years, the Vietnamese agricultural sector currently faces a number of issues because of the conditions of a transition economy and climate change. The agricultural sector mainly consists of small-scale producers who cannot take advantage of economies of scale and have lagged behind regional and other developing countries in labour, agricultural land, and water productivity. Therefore, transforming the agricultural sector into a large-scale, modern, technology-based sector is an important priority of Vietnam’s farming community. One most critical element that fosters the development and modernization of the agricultural sector and rural areas in developing countries such as Vietnam is agricultural credit. The Vietnamese government has tried to meet rural households’ credit demand by issuing decrees on credit-related policies for agricultural and rural development. Vietnam’s farm households, however, suffer from credit rationing. Failure to correctly recognize the credit demand and credit rationing of farmers might be a major reason for the ineffectiveness of credit programmes in Vietnam and other developing countries. Despite the acknowledged importance of credit in agricultural development, no empirical investigation has explored the relationship between credit and Vietnam’s agricultural performance at the macro-level. In addition, no study simultaneously examines credit demand, credit rationing determinants, and credit rationing impact on farm performance in formal, semi-formal, and informal credit markets, leading to biased estimates. In this study, we conduct both micro-level and macro-level analyses. For micro-level analysis that uses farm household survey data, we apply the direct elicitation method to classify the farm households’ credit rationing conditions. Trivariate probit models are used to examine the relationships among formal, semi-formal, and informal credit markets, the determinants of credit demand and credit rationing of farm households. We use the multinomial endogenous treatment effects model to investigate the impact of credit rationing on farm performance. For macro-level analysis that uses secondary macro data, the credit-agricultural performance nexus is explored using the indicator saturation break tests, Kapetanios (2005) unit root test, Autoregressive distributed lag (ARDL) model, and Toda–Yamamoto (1995) approach to Granger causality. The trivariate probit model’s results reveal complementary relationships among two pairs of credit markets (formal versus informal and semi-formal versus informal). The joint significance of the correlations among credit demand and credit rationing in different credit markets is confirmed, which supports the use of the trivariate probit models when simultaneously investigating credit demand and credit rationing determinants in formal, semi-formal, and informal credit markets. In general, there are dissimilarities among the determinants of farm households' credit demands and credit rationing in different markets, reflecting Vietnam’s segmented credit markets. The multinomial endogenous treatment effects model results confirm that credit rationing in any credit market has a severe, negative impact on agricultural outcomes for farm households. The ARDL model’s results indicate that agricultural credit positively influences agricultural GDP in both the short and long run. The results also show the long-term positive, significant effects of labour and rainfall on agricultural GDP. The Toda–Yamamoto (1995) approach to the Granger causality test’s result reveals a unidirectional causal relationship running from credit to agricultural GDP