Jamie Emerson

A Cointegration Analysis of the Correlates of Performance in Franchised Channels

Not much is known about the primary drivers of performance in franchising systems. With some notable exceptions, much of the franchising literature on performance related issues has focused on either contrasting failure rates of independent small businesses and entrepreneurs with those of franchises and/or system survival issues. The existing literature on franchising performance displays at least three other characteristic patterns. First, most studies have restricted themselves to a single sector, usually, the fast food restaurant industry, since it is often perceived and portrayed as the archetypical franchise sector. Second, existing investigations have tended to focus on a single measure of performance. Finally, with the exception of survival articles, empirical studies have typically confined themselves to cross-sectional examination of the evidence. In other words, we know very little about what fosters long term performance.

Testing for Structural Change of a Time Trend Regression in Panel Data

In this paper we propose two classes of test statistics for detecting a break at an unknown date in panel data models with time trend. The first one is the fluctuation test of Ploberger-Kramer-Kontrus (1989). The second one is based on the mean and exponential Wald statistics of Andrew and Ploberger (1994) and maximum Wald statistic of Andrew (1993). We derive the limiting distributions of the proposed test and tabulate the critical values. Asymptotic results were derived I(0), I(1) and nearly I(1) error terms. We also show that these tests have non-trivial local power only when the error terms are I(0).

Cointegration analysis and the choice of lag length

This article investigates the effects of the choice of lag length on the estimation of long run cointegration relationships using the Johansen estimation procedure. This issue is of particular interest to applied researchers using time series data, see for example Awokuse (2005), Bacchiocchi et al . (2005), Gallegati (2005), Gomes and Paz (2005), Hasan (2005) and Pieroni and Ricciarelli (2005), among many others. An empirical example is used to demonstrate some of the issues that applied researchers face when they wish to use cointegration analysis. First, the number of lags to include in the model must be determined. However, different lag length selection criteria often lead to a different conclusion regarding the optimal lag order that should be used. Second, as demonstrated in this article, the choice of lag length can drastically affect the results of the cointegration analysis.

How Firm Strategies Impact Size of Partner-Based Retail Networks: Evidence from Franchising

How do firms’ partnering strategies impact the size of their partner-based retail networks? We draw on agency theory to address this question in the context of franchising. Our econometric analyses (based on 9 years of longitudinal balanced panel data) include assessment of data nonstationarity and estimation of a dynamic panel data model that accounts for unobserved heterogeneity and endogeneity. Our findings indicate that franchisee network size is driven more by franchisor strategies that mitigate agency costs than by strategies that simply lower entry and ongoing costs and barriers for franchisees.

Long-run monetary neutrality

In this article, we provide a test of long-run monetary neutrality employing cointegration and vector error-correction modelling methodology. Using quarterly data for the United States, we estimate the long-run relationships among money supply and output and other key macroeconomic variables. Our findings, in general, raise doubts about the long-run monetary neutrality proposition.

Fiscal policy, profits and investment: some additional evidence

This article provides time series evidence on the effects of fiscal policy on profits and investment in the US. In addition to neoclassical models of investment and profits we also consider Keynesian models. Our findings provide some support for the neoclassical views. However, Keynesian explanations, which allow for the effects of the real interest rate, receive strong support from the data.

On the Estimation of a Linear Time Trend Regression with a One- Way Error Component Model in the Presence of Serially Correlated Errors

In this paper, we study the limiting distributions for the ordinary least squares (OLS), the fixed effects (FE), first difference (FD), and the generalized least squares (GLS) estimators in a linear time trend regression with a one-way error component model in the presence of serially correlated errors. We show that when the error term is I(0), the FE is asymptotically equivalent to GLS. However, when the error term is I(1), the GLS could be less efficient than FD or FE estimators and FD is the most efficient estimator. However, when the intercept is included in the model and the error term is I(0), the OLS, FE, and GLS are asymptotically equivalent. The limiting distribution of the GLS depends on the initial condition significantly when the error term is I(1) and an intercept is included in the regression. Monte Carlo experiments are employed to compare the performance of these estimators in finite samples. The main findings are: (1) the two-steps GLS estimators perform well if the variance component is small and close to zero when autocorrelation coefficient is less than one, (2) the FD estimator dominates the other estimators when autocorrelation coefficient equals to one for all values of variance component and (3) the FE estimator is recommended in practice since it performs pretty well for all values of the autocorrelation coefficient and variance component.

Bootstrapping and hypothesis testing in non-stationary panel data

This paper uses the wild bootstrap to compute empirically relevant critical values for the test statistics proposed by Emerson and Kao (2001). Monte Carlo simulations were then performed to evaluate the size and power properties of the bootstrapped tests.