2. Data The monthly rice price data are taken from the food outlook of the FAO and the global information and early warning system (GIEWS) of FAO. The exchange rate data are collected from the ‘Economic Trends’ of Bangladesh Bank. The monthly FOB Thai 100% B prices are used as world price because Bangladesh imports this type of rice. Although, there are some changes of the exporting countries of rice to Bangladesh, the present study used `Thai price` as a world prices for two main reasons. First, Thailand has been the largest rice exporter over the last couple of decades and may be regarded as a price leader in the world rice market. Secondly, In addition, we assume that Thai and Indian rice prices are highly correlated because a recent study by Yavapolkul et al. (2006) found that major importing countries like Thailand and India among others are integrated, therefore, supporting our assumption. So, although Bangladesh imports rice from India, but using Thai price as a proxy for India so far a good choice. The data period covers September 1998 to February 2007. The data periods are chosen because of data availability and also to capture the period of the highest pace of agricultural trade liberalization in Bangladesh. The evolution of the Bangladesh domestic and the world market prices. The comovement of two price series someway roughly indicates that there might be an existence of long-run equilibrium relationship. The spread between these two prices has been squeezed during the later time period. The graph indicates that prices are more stable in the domestic market than in the world market. However, the number of observations after this period is not considered enough to model the relationships considering a break point hence we model the dynamic relationships for the whole period in a single model.
3.1 Time Series Properties of Data
Since data are time series, the world price and domestic price of rice are tested for their nonstationary. Therefore, we conduct unit root test by the standard augmented Dickey-Fuller (ADF) (1979) and the Philips Perron (PP) (1989) test. The ADF unit root test with an optimal lag length determined by the Akaike information criterion (AIC), Schwarz Bayesian information criterion (SBC) and Lagrangian multiplier (LM) criteria and is used in the following form.
3.2 Threshold cointegration model The concept of threshold cointegration was introduced first by Balke and Fomby (1997) as a way of combining cointegration and non-linearity. The authors present the possibility that movements towards the long-run equilibrium might not occur in every time period, due to the presence of TC. After that, the limitation of linear cointegration has been often discussed in recent literature because neglecting of TC may inhibit price integration across spatially separated markets (for example, see Barret and Li, 2002; Fackler and Goodwin, 2001; Goodwin and Piggot, 2001; Abdulai, 2000, 2002; Goodwin and Harper, 2000). Goodwin and Piggott (2001) have used a threshold error correction model to estimate spatial integration in US corn and soybean markets. Ben-Kaabia and Jose (2007) have estimated price transmission between vertical stages of the Spanish lamb market using a threshold model. Sanogo and Maliki (2010) have analyzed the rice market integration between Nepal and India applying a threshold autoregressive model. The conceptual basis of the analysis, along with the econometrics estimation procedures is explained below.
One implicit assumption of the linear model like Johansen and Jesulius (1992) and Engel and Granger (1987) is that adjustment of prices induced by deviations from the long-term equilibrium is a continuous and a linear function of the magnitude of deviations. Thus, every small deviation will always lead to an adjustment. This assumption might mislead the results because it ignores the affect of TC in price adjustment.
Considering the role of TC into account one could use a threshold cointegration model in which the price adjustment could differ based on the magnitude of the deviations from its long-run equilibrium. The speed of adjustment can be different if the deviations are above or below the specific threshold –which would proxy the size of TC.
The price adjustment (Pt) is considered to be a function of deviations from the long-run equilibrium (ECT) which can be represented by a two regime threshold vector error correction model (TVECM). We proceed by estimating the two regime TVECM proposed by Hansen and Seo (2002). Here, the regime is defined based on only one threshold (γ) and therefore if the absolute price deviation from the long-run equilibrium is bigger than the threshold (γ), the price transmission process is defined by regime 2, while in the case of smaller deviations and thus falling within a ‘band of no adjustment’ from the long-run equilibrium, the price transmission process is defined as regime 1. Therefore, to estimate a two-regime threshold vector error correction model, the threshold γ must also be estimated. For this, a variant of the Hansen and Seo (2002) model is presented below. Pede and McKenzie (2005) take this approach to estimate market integration in Benin maize markets.