The Estimation of Technical Efficiency Effects Models with an Example Applied to the Thai Manufacturing Sector

This paper does two things. First, it presents alternative approaches to the standard methods of estimating productive efficiency using a production function. It favours a parametric approach (viz. the stochastic production frontier approach) over a nonparametric approach (e.g. data envelopment analysis); and, further, one that provides a statistical explanation of efficiency, as well as an estimate of its magnitude. Second, it illustrates the favoured approach (i.e. the ‘single stage procedure’) with estimates of two models of explained inefficiency, using data from the Thai manufacturing sector, after the crisis of 1997. Technical efficiency is modelled as being dependent on capital investment in three major areas (viz. land, machinery and office appliances) where land is intended to proxy the effects of unproductive, speculative capital investment; and both machinery and office appliances are intended to proxy the effects of productive, non-speculative capital investment. The estimates from these models cast new light on the five-year long, post-1997 crisis period in Thailand, suggesting a structural shift from relatively labour intensive to relatively capital intensive production in manufactures from 1998 to 2002.

Technology shocks and aggregate fluctuations in an estimated hybrid RBC model

This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of technology shocks in explaining aggregate fluctuations. To this end we estimate the model’s posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework we extend Ireland’s (2001, 2004) hybrid estimation approach to allow for a vector autoregressive moving average (VARMA) process to describe the movements and co-movements of the model’s errors not explained by the basic RBC model. The results of marginal likelihood ratio tests reveal that the more general model of the errors significantly improves the model’s fit relative to the VAR and AR alternatives. Moreover, despite setting the RBC model a more difficult task under the VARMA specification, our analysis, based on forecast error and spectral decompositions, suggests that the RBC model is still capable of explaining a significant fraction of the observed variation in macroeconomic aggregates in the post-war U.S. economy.

A Salience Theory of Choice Errors

We study a psychologically based foundation for choice errors. The decision maker applies a preference ranking after forming a 'consideration set' prior to choosing an alternative. Membership of the consideration set is determined both by the alternative specific salience and by the rationality of the agent (his general propensity to consider all alternatives). The model turns out to include a logit formulation as a special case. In general, it has a rich set of implications both for exogenous parameters and for a situation in which alternatives can a¤ect their own salience (salience games). Such implications are relevant to assess the link between 'revealed' preferences and 'true' preferences: for example, less rational agents may paradoxically express their preference through choice more truthfully than more rational agents.

Growth Rate Estimation in the presence of Unit Roots

This study addresses the issue of the presence of a unit root on the growth rate estimation by the least-squares approach. We argue that when the log of a variable contains a unit root, i.e., it is not stationary then the growth rate estimate from the log-linear trend model is not a valid representation of the actual growth of the series. In fact, under such a situation, we show that the growth of the series is the cumulative impact of a stochastic process. As such the growth estimate from such a model is just a spurious representation of the actual growth of the series, which we refer to as a “pseudo growth rate”. Hence such an estimate should be interpreted with caution. On the other hand, we highlight that the statistical representation of a series as containing a unit root is not easy to separate from an alternative description which represents the series as fundamentally deterministic (no unit root) but containing a structural break. In search of a way around this, our study presents a survey of both the theoretical and empirical literature on unit root tests that takes into account possible structural breaks. We show that when a series is trendstationary with breaks, it is possible to use the log-linear trend model to obtain well defined estimates of growth rates for sub-periods which are valid representations of the actual growth of the series. Finally, to highlight the above issues, we carry out an empirical application whereby we estimate meaningful growth rates of real wages per worker for 51 industries from the organised manufacturing sector in India for the period 1973-2003, which are not only unbiased but also asymptotically efficient. We use these growth rate estimates to highlight the evolving inter-industry wage structure in India.

Where is the economics in spatial econometrics?

Spatial econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.

Estimation of the Spatial Weights Matrix under Structural Constraints

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand.

Productivity shocks and aggregate fluctuations in an estimated endogenous growth model with human capital

Employing an endogenous growth model with human capital, this paper explores how productivity shocks in the goods and human capital producing sectors contribute to explaining aggregate fluctuations in output, consumption, investment and hours. Given the importance of accounting for both the dynamics and the trends in the data not captured by the theoretical growth model, we introduce a vector error correction model (VECM) of the measurement errors and estimate the model’s posterior density function using Bayesian methods. To contextualize our findings with those in the literature, we also assess whether the endogenous growth model or the standard real business cycle model better explains the observed variation in these aggregates. In addressing these issues we contribute to both the methods of analysis and the ongoing debate regarding the effects of innovations to productivity on macroeconomic activity.

Currency Forecast Errors at Times of Low Interest Rates: Evidence from Survey Data on the Yen/Dollar Exchange Rate

Using survey expectations data and Markov-switching models, this paper evaluates the characteristics and evolution of investors' forecast errors about the yen/dollar exchange rate. Since our model is derived from the uncovered interest rate parity (UIRP) condition and our data cover a period of low interest rates, this study is also related to the forward premium puzzle and the currency carry trade strategy. We obtain the following results. First, with the same forecast horizon, exchange rate forecasts are homogeneous among different industry types, but within the same industry, exchange rate forecasts differ if the forecast time horizon is different. In particular, investors tend to undervalue the future exchange rate for long term forecast horizons; however, in the short run they tend to overvalue the future exchange rate. Second, while forecast errors are found to be partly driven by interest rate spreads, evidence against the UIRP is provided regardless of the forecasting time horizon; the forward premium puzzle becomes more significant in shorter term forecasting errors. Consistent with this finding, our coefficients on interest rate spreads provide indirect evidence of the yen carry trade over only a short term forecast horizon. Furthermore, the carry trade seems to be active when there is a clear indication that the interest rate will be low in the future.

Decision Errors, Legal Uncertainty and Welfare: a General Treatment

This paper provides a general treatment of the implications for welfare of legal uncertainty. We distinguish legal uncertainty from decision errors: though the former can be influenced by the latter, the latter are neither necessary nor sufficient for the existence of legal uncertainty. We show that an increase in decision errors will always reduce welfare. However, for any given level of decision errors, information structures involving more legal uncertainty can improve welfare. This holds always, even when there is complete legal uncertainty, when sanctions on socially harmful actions are set at their optimal level. This transforms radically one’s perception about the “costs” of legal uncertainty. We also provide general proofs for two results, previously established under restrictive assumptions. The first is that Effects-Based enforcement procedures may welfare dominate Per Se (or object-based) procedures and will always do so when sanctions are optimally set. The second is that optimal sanctions may well be higher under enforcement procedures involving more legal uncertainty.

Optimal Universal and Categorical Benefits with Classification Errors and Imperfect Enforcement

We determine he optimal combination of a universal benefit, B, and categorical benefit, C, for an economy in which individuals differ in both their ability to work - modelled as an exogenous zero quantity constraint on labour supply - and, conditional on being able to work, their productivity at work. C is targeted at those unable to work, and is conditioned in two dimensions: ex-ante an individual must be unable to work and be awarded the benefit, whilst ex-post a recipient must not subsequently work. However, the ex-ante conditionality may be imperfectly enforced due to Type I (false rejection) and Type II (false award) classification errors, whilst, in addition, the ex-post conditionality may be imperfectly enforced. If there are no classification errors - and thus no enforcement issues - it is always optimal to set C>0, whilst B=0 only if the benefit budget is sufficiently small. However, when classification errors occur, B=0 only if there are no Type I errors and the benefit budget is sufficiently small, while the conditions under which C>0 depend on the enforcement of the ex-post conditionality. We consider two discrete alternatives. Under No Enforcement C>0 only if the test administering C has some discriminatory power. In addition, social welfare is decreasing in the propensity to make each type error. However, under Full Enforcement C>0 for all levels of discriminatory power. Furthermore, whilst social welfare is decreasing in the propensity to make Type I errors, there are certain conditions under which it is increasing in the propensity to make Type II errors. This implies that there may be conditions under which it would be welfare enhancing to lower the chosen eligibility threshold - support the suggestion by Goodin (1985) to "err on the side of kindness".

Type II Errors in IO Multipliers

This paper compares methods for calculating Input-Output (IO) Type II multipliers. These are formulations of the standard Leontief IO model which endogenise elements of household consumption. An analytical comparison of the two basic IO Type II multiplier methods with the Social Accounting Matrix (SAM) multiplier approach identifies the treatment of non-wage income generated in production as a central problem. The multiplier values for each of the IO and SAM methods are calculated using Scottish data for 2009. These results can be used to choose which Type II IO multiplier to adopt where SAM multiplier values are unavailable.