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It is widely documented that while contemporaneous spot and forward financial prices trace each other extremely closely, their difference is often highly persistent and the conventional cointegration tests may suggest lack of cointegration. This chapter studies the possibility of having cointegrated errors that are characterized simultaneously by high persistence (near-unit root behavior) and very small (near zero) variance. The proposed dual parameterization induces the cointegration error process to be stochastically bounded which prevents the variables in the cointegrating system from drifting apart over a reasonably long horizon. More specifically, this chapter develops the appropriate asymptotic theory (rate of convergence and asymptotic distribution) for the estimators in unconditional and conditional vector error correction models (VECM) when the error correction term is parameterized as a dampened near-unit root process (local-to-unity process with local-to-zero variance). The important differences in the limiting behavior of the estimators and their implications for empirical analysis are discussed. Simulation results and an empirical analysis of the forward premium regressions are also provided.

This chapter studies the asymptotic properties of within-groups k-class estimators in a panel data model with weak instruments. Weak instruments are characterized by the coefficients of the instruments in the reduced form equation shrinking to zero at a rate proportional to nTδ, where n is the dimension of the cross-section and T is the dimension of the time series. Joint limits as (n,T)→∞ show that this within-group k-class estimator is consistent if 0≤δ<12 and inconsistent if 12≤δ<∞.

This chapter examines the asymptotic properties of the Stein-type shrinkage combined (averaging) estimation of panel data models. We introduce a combined estimation when the fixed effects (FE) estimator is inconsistent due to endogeneity arising from the correlated common effects in the regression error and regressors. In this case, the FE estimator and the CCEP estimator of Pesaran (2006) are combined. This can be viewed as the panel data model version of the shrinkage to combine the OLS and 2SLS estimators as the CCEP estimator is a 2SLS or control function estimator that controls for the endogeneity arising from the correlated common effects. The asymptotic theory, Monte Carlo simulation, and empirical applications are presented. According to our calculation of the asymptotic risk, the Stein-like shrinkage estimator is more efficient estimation than the CCEP estimator.

In the past two decades, there has been a remarkable decline in inflation in both developed and developing countries, in sharp contrast to the period immediately preceding it. Interestingly, the behaviour of inflation in India broadly exhibits such a pattern. For much of the 1970s and 1980s, India experienced recurrent bouts of high inflation together with sub‐par economic performance. Since the 1990s the inflation record has been far better. The purpose of this paper is to answer an important question about what ultimately brought on this improved economic outcome.

A time‐varying parameter model for inflation is proposed which nests all the plausible explanations. The time variation in parameters is modelled as driftless random walks, and is estimated using the median unbiased estimator. The median unbiased estimate helps in addressing the pile‐up problem, which arise if variances of the state specification are small. In such cases the maximum likelihood estimates are biased towards zero. Kalman Filter algorithm is used to obtain the time path of the parameters of the reduced form equation.

The estimated time paths of the reaction function coefficients suggest gradual changes in the rule coefficients. It has been found that while better monetary policy and structural change have played a non‐trivial role, good luck and exchange rate regime have played a major role in the moderation of inflation in the 1990s. This interpretation suggests that to prevent a resurgence of 1970s‐style inflation, the central bank should reinforce as much as possible its commitment to low inflation by institutional, operational, and rhetorical means. Otherwise, sooner or later, luck will dry out and high inflation could return.

A time‐varying parameter model for inflation in India is proposed which nests the various plausible explanations for moderate inflation in the recent decade. Most empirical and theoretical studies on inflation dynamics have concentrated on developed economies. This paper pays attention to the international dimension of the issue. The reduced form model is estimated using time‐varying parameter estimation technique.

Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

This paper considers the finite-sample distribution of the 2SLS estimator and derives bounds on its exact bias in the presence of weak and/or many instruments. We then contrast the behavior of the exact bias expressions and the asymptotic expansions currently popular in the literature, including a consideration of the no-moment problem exhibited by many Nagar-type estimators. After deriving a finite-sample unbiased k-class estimator, we introduce a double-k-class estimator based on Nagar (1962) that dominates k-class estimators (including 2SLS), especially in the cases of weak and/or many instruments. We demonstrate these properties in Monte Carlo simulations showing that our preferred estimators outperform Fuller (1977) estimators in terms of mean bias and MSE.