A Spurious Regression Approach to Estimating Structural Parameters

Economic models often imply that certain variables are cointegrated. However, tests often fail to reject the null hypothesis of no cointegration for these variables. One possible explanation of these test results is that the error is unit root nonstationary due to a nonstationary measurement error in one variable. For example, currency held by the domestic economic agents for legitimate transactions is very hard to measure due to currency held by foreign residents and black market transactions. Therefore, money may be measured with a nonstationary error. If the money demand function is stable in the long-run, we have a cointegrating regression when money is measured with a stationary measurement error, but have a spurious regression when money is measured with a nonstationary measurement error. We can still recover structural parameters under certain conditions for the nonstationary measurement error. This paper proposes econometric methods based on asymptotic theory to estimate structural parameters with spurious regressions involving unit root nonstaionary variables.Spurious regression, GLS correction method

Structural Spurious Regressions and A Hausman-type Cointegration Test

This paper proposes two estimators based on asymptotic theory to estimate structural parameters with spurious regressions involving unit-root nonstationary variables. This approach motivates a Hausman-type test for the null hypothesis of cointegration for dynamic Ordinary Least Squares estimation using one of our estimators for spurious regressions. We apply our estimation and testing methods to four applications: (i) long-run money demand in the U.S.; (ii) long-run implications of the consumption-leisure choice; (iii) output convergence among industrial and developing countries; (iv) Purchasing Power Parity for traded and non-traded goods.Spurious regression, GLS correction method, Dynamic regression, Test for cointegration.

Unbiased Estimation of the Half-Life to PPP Convergence in Panel Data

Three potential sources of bias present complications for estimating the half-life of purchasing power parity deviations from panel data. They are the bias associated with inapproiate aggregation across heterogeneous coefficients, time aggregation of commodity prices, and downward bias in estimation of dynamic lag coefficients. Each of these biases have been addressed individually in the literature. In this paper, we address all three biases in arriving at our estimates. Analyzing an annual panel data set of real exchange rates for 21 OECD countries from 1948 to 2002, our point estimate of the half-life is 5.5 years.

Bias Reduction by Recursive Mean Adjustment in Dynamic Panel Data Models

Accurate estimation of the dominant root of a stationary but persistent time series are required to determine the speed at which economic time series, such as real exchange rates or interest rates, adjust towards their mean values. In practice, accuracy is hampered by downward small- sample bias. Recursive mean adjustment has been found to be a useful bias reduction strategy in the regression context. In this paper, we study recursive mean adjustment in dynamic panel data models. When there exists cross-sectional heterogeneity in the dominant root, the recursive mean adjusted SUR estimator is appropriate. When homogeneity restrictions can be imposed, a pooled recursive mean adjusted GLS estimator with fixed e¤ects is the desired estimator. Application of these techniques to a small panel of five eurocurrency rates finds that these interest rates are unit root nonstationary as the bias-corrected autoregressive coefficient exceeds 1.Small sample bias, Recursive mean adjustment, Panel Data, Cross-sectional dependence, Interest rate dynamics

Prewhitening Bias in HAC Estimation

HAC estimation commonly involves the use of prewhitening filters based on simple autoregressive models. In such applications, small sample bias in the estimation of autoregressive coefficients is transmitted to the recoloring filter, leading to HAC variance estimates that can be badly biased. The present paper provides an analysis of these issues using asymptotic expansions and simulations. The approach we recommend involves the use of recursive demeaning procedures that mitigate the effects of small sample autoregressive bias. Moreover, a commonly-used restriction rule on the prewhitening estimates (that first order autoregressive coefficient estimates, or largest eigenvalues, greater than 0.97 be replaced by 0.97) adversely interfers with the power of unit root and KPSS tests. We provide a new boundary condition rule that improves the size and power properties of these tests. Some illustrations are given of the effects of these adjustments on the size and power of KPSS testing. Using prewhitened HAC estimates and the new boundary condition rule, the KPSS test is consistent, in contrast to KPSS testing that uses conventional prewhitened HAC estimates (Lee, 1996).Autoregression, Bias, HAC estimator, KPSS testing, Long run variance, Prewhitening, Recursive demeaning

Heterogeneous Response of Disaggregate Inflation to Monetary Policy Regime Change: The Role of Price Stickiness

This paper explores the impact of monetary policy regime change on sectoral and regional inflation by analyzing the case of Canada and its adoption of inflation targeting (IT). Using disaggregated CPI data for Canada from 1978, we find that responses to the change in the monetary policy framework are quite heterogeneous, particularly across sectors. While inflation series in the traditionally volatile commodity sectors exhibit weak responses to the regime change, those in the so-called core sectors are highly responsive. This pattern is evident in both national and provincial level data, indicating that it is the core sectors that are crucial for the transmission of a monetary policy regime change. Further analysis based on a common factor model reveals that common shocks, such as those associated with the monetary policy framework, account for only a small portion of the variation in sectoral inflation, and that their relative importance has decreased after IT adoption in many core sectors. Interestingly, considerable variation exists even across the core sectors in the strength of the regime change effect. We document that this heterogeneity is meaningfully correlated with some measurable sector-specific characteristics; sectors with a lower degree of prices stickiness and a lower degree of tradability appear more sensitive to the change in monetary policy regime

Inflation Targeting and Relative Price Variability: What Difference Does Inflation Targeting Make?

This article studies the effects of inflation targeting (IT) on relative price variability (RPV) using a data set of twenty countries comprising both targeters and nontargeters. We find that a decline in mean inflation after IT adoption is not necessarily associated with a similar fall in RPV and that what matters most for the structural changes in RPV is the initial inflation regime prior to the adoption of IT rather than IT adoption itself. IT adoption impacts the shape of the underlying relationship between inflation and RPV in countries with initially high inflation rates, moving it from monotonie to the U-shaped profile observed consistently for countries with low-inflation regimes. The minimum point of this U-shaped curve is indicative of the public\u27s expectations of inflation and is very close to the announced target for inflation in most of the countries we study