Implications of conceptual and data complexities on time-series econometric applications in market integration analysis

Abstract

This article demonstrates and highlights the conceptual limits of current empirical market integration (MI) time series models (threshold models) and their implications on market effi ciency and competitive equilibrium conclusions. The complexities and diversities that characterise the analysis of the concept of market integration are evaluated within the framework of Enke-Samuelson-Takayama-Judge (ESTJ) spatial equilibrium theory. The effi ciency and competiveness implications drawn from MI models are limited by how the data generation process (DGP) is infl uenced by equilibrium conditions, by the tradability restrictions of the inter-markets relationships and by the presence of unobserved transactions costs. However, empirical applications scarcely address these limitations. Two sets of synthesized data with varying levels of non-linear complexity implied by alternating equilibrium conditions are generated to demonstrate conceptual limits of current threshold models in market integration analysis. Inconsistent conclusions that linear representations imply for threshold propagated DGP will also apply for conclusions derived from threshold models if markets are characterised by switching equilibria conditions.

Modelling non-linear Spatial Market Integration and Equilibrium Processes in Hidden Markov Framework

“…That is, they address non-linear constraint imposed on MI processes by transaction costs component without using observable transaction costs data. However, as noted above and recently demonstrated by Abunyuwah (2013) in synthesized experiment, the general b-TAR models do underestimate adjustment parameters when the DGP is characterised by switching equilibria dynamics. In their attempt to address switching inter-market adjustment processes in the time-series domain, Stephens et al (2008) and Amikuzuno, (2011) utilised trade flow data in price transmission analysis, similar to tradability implications drawn from trade flow data in PBM applications.…”

“…In line with the main proposition that motivates the application of PBM and limits imposed by data and conceptual complexities on b-TAR models (see Abunyuwah, 2013), we also applied the MSIA and b-TAR models to analyse series B. This data set is characterised by a mixture of TC based threshold effects and switching equilibrium conditions implied by alternating segmented and integrated market conditions.…”

“…When b-TAR model was applied, the three adjustment parameters produced (see columns 3 to 5 of row five of the table) indicate a form of persistence across all the regimes. Unlike the simple nonlinear data set considered in Table 1, the impact of the complex data set has significantly blurred and understated the strong rent correction implied by periods of relatively perfect market integration phases (see Abunyuwah, 2013 for recent demonstration). In the second half of the table, results from the Markov variant are also presented.…”

“…Studies such as Abunyuwah (2008), van Campenhout (2009), Barrett and Li (2002) and Baulch (1997) have conceptually demonstrated how each of these and or their combined effects create non-linear constraints on the dynamic system. For instance, Abunyuwah (2008Abunyuwah ( , 2013 demonstrates that if markets are characterised by switching equilibria conditions over time, then a two-layer non-linear structure ensues if transaction costs motivated threshold effect is present. The presence of the later characteristic leads to regime switching that is based on the level or magnitude of price differences in relation to prevailing TC.…”

Modeling Market Integration and Asymmetric Price Transmission Dynamics of Yam Markets in Ghana

“…In market integration and price transmission mechanisms (Baulch, 1997;von Cramon, 1998; Barrett and Li, 2002;Zapata and Gauthier, 2003;Abunyuwah, 2008Abunyuwah, , 2013. Presence of strongly integrated markets across space ensures the prevalence of the law of one price and in effect reduces price variability and its adverse consequences on consumer and producers' resource utilization behaviors.…”