Studying Growth in the Modern Classical Approach: Theoretical and Empirical Implications for the Analysis of Potential Output

Abstract

The main characteristics of the modern classical approach to growth are studied with particular reference to the notion of ‘potential output’. In contrast to mainstream approaches, which consider potential output to be exogenous and supply-determined, it is here regarded as endogenous and path-dependent. A tentative analysis is carried out of the implications of such a conception in empirical research, with special reference to the effects of the crisis on potential growth. Mainstream estimation methods (especially those used by international institutions) are shown to be deeply influenced by theory, but also to provide dubious and puzzling results. Very different empirical results and policy implications may be obtained from the standpoint of the alternative theoretical framework provided by the modern classical approach. On this basis, the paper proposes that the long-term policy target should be set in terms of the rate of unemployment rather than potential output or potential growth.     

Modelling nonlinearity in U.S. Gross national product 1889–1987

This paper considers modelling the annual logarithmed per capita gross national product of the United States in 1889–1987. Some authors have suggested that the parameters of the process generating the data have changed over time but formal parameter constancy tests do not support this argument. The series turns out to be nonlinear and can be adequately characterized by an exponential smooth transition autoregressive model. For comparison, a detrended series is also considered, found nonlinear and modelled using a logistic smooth transition autoregressive model. The behaviour of the estimated models is discussed, and it is seen that nonlinearity is needed to describe the response of the process to exceptionally large exogenous shocks. The properties of the models are further investigated by forecasting several years ahead, and the forecasts are compared with those from other linear and nonlinear models.     

A modified logit model for time series with an application to the pricing behaviour of manufacturing firms in Australia

The modified logit model (Amemiya and Nold, 1975) is generalised to the case where the error term is autocorrelated. The asymptotic distribution (as n →∞ and T →∞) of a feasible GLS estimator of β is derived. Tests of linear restrictions on β and the significance of ρ are presented. The results of the applied work suggest that the factors which explain the pricing behaviour of manufacturing firms, as reported in the tendency survey conducted by the Australian Chamber of Commerce and Industry and the Westpac Banking Corporation, include historical inflation rates of up to 7 quarters and capacity utilisation. First version received: March 2001/Final version received: July 2002 RID="*" ID="*"  The first draft of this paper was written while the author was on study leave at the Department of Econometrics, University of Sydney, Australia.     

基于ARMA-GARCH模型的股指波动压力测试情景设计研究

针对股指收益率时间序列某期间的异方差、尖峰厚尾以及序列自相关等特性,将ARMA模型与GARCH模型相结合,回归建模测算相关股指年度收益率VaR值,可以有效预测类似市场条件下股指的波动以及相伴概率。因此,在证券公司压力测试实践中,基于相伴概率合理设计股指下跌的压力测试情景,可以进一步提高压力测试情景设计的科学性,增强压力测试结果的现实指导意义。同时,可以将本文研究思路推广应用于利率、汇率、市场交易量等历史数据较充分的金融时间序列的实证分析,借以指导债市波动、汇市波动以及市场交易量波动等压力测试情景的设计工作。     

An augmented P-star model of Indian inflation

ABSTRACT

An augmented P-Star model is estimated and tested to identify the drivers of inflation in India. The model includes monetary and non-monetary factors, demand-pull and cost-push factors, and domestic as well as foreign factors. The results show that inflation in India is driven by a combination of monetary factors and non-monetary factors, some of which affect inflation on the supply side while others operate on the demand side. It turns out, however, that inflation in India is determined more by domestic rather than foreign factors.     

The effect of quantitative easing on stock prices: a structural time series approach

A structural time series model is estimated and tested to examine the effect of quantitative easing (QE) on US stock prices. The model is estimated by maximum likelihood in a Time-varying parametric (TVP) framework, using the S&P 500 index as the dependent variable and the Fed’s balance as an explanatory variable in addition to the unobserved components accounting for the behaviour of variables that do not appear explicitly in the equation. The results show that QE had a sizeable, but not exclusive, effect on stock prices and that stock prices were also affected by other missing variables and cyclical movements. Several explanations are presented for the rise of the US stock market in the post-QE period, particularly since the election of Donald Trump.     

A linear demand system within a seemingly unrelated time series equations framework

A seemingly unrelated time series equations framework for the linear almost ideal (AID) demand system is considered. The framework is applied to a consumer demand system covering nine non-durable commodities. Within a specification where the static linear AID system is augmented by latent variables representing stochastic trends and seasonality, demand homogeneity is tested; both in each equation and in the system as a whole. Income and own-price elasticities are calculated under homogeneity restrictions. Although the homogeneous model is formally rejected by statistical tests, it performs well with respect to interpretability, parameter stability and forecasting.     

Forecasting volatility in the Indian equity market using return and range-based models

In this article, we assess the time-varying volatility of the National Stock Exchange in the Indian equity market using unconditional estimators and asymmetric conditional econometric models. The volatility estimate and forecast is computed from the interday return and intraday range-based data of the exchange’s flagship index, CNX NIFTY, for the time period spanning 1 January 2009 through 31 December 2013. These are our findings: First, we determine that the time-varying volatility of the index is asymmetric with qualities of stationarity and leptokurtic distribution. Second, the one-step-ahead volatility forecast derived from the univariate time series parameters through the GJR-GARCH ?????process indicates that the model evaluation criteria of the autoregressive process tends towards range-based models vis-à-vis a return-based model. The validity of this methodology is further analysed with the superior predictive ability test, the outcome of which supports the use of range-based conditional models. Finally, among the evaluated range-based model variants, the model confidence set procedure favours the Yang–Zhang estimator as being better suited to forecast the exchange’s volatility than the ones by Parkinson, Garman–Klass and Rogers–Satchell.     

From innovation to market entry: a strategic management model for new technologies

In this paper, we introduce a new framework that generates a list of strategic actions to support successful management of the innovation process as new technologies are taken to market. The framework links different stakeholders inside and outside the organisation to obtain an holistic view of the requirements necessary to develop the new technology. It integrates and synthesises existing frameworks into an inclusive set of guidelines. The framework is then tested in a case study located in a mid-size semiconductor organisation currently seeking new business opportunities in the emerging photovoltaic market. We conclude by reflecting on the usefulness of the model. Theoretically, the paper contributes to the literature on the management of the introduction of new technologies; practically, the framework provides a normative tool for practising managers.     

Was the recent downturn in US real GDP predictable?

This article uses a small set of variables – real GDP, the inflation rate and the short-term interest rate – and a rich set of models – atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models – to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-of-sample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.     

Comovement among returns of the private Chilean pension system

Abstract

The measurement of comovement among economic variables is key in several areas of economics and finance. This article examines the comovement among Pension Fund Administrators (AFPs) in the Chilean private pension system from 2005 to 2016. We use several statistical methods to assess the comovement among the returns during this period. We found evidence of strong comovement among the returns of all AFPs; the higher the percentage of risk rate of the investment instrument, the higher the comovement was. The introduction of a new AFP in 2010 did not change the comovement patterns. The comovement analysis study shows that the quality attributes of the products offered by the AFPs are homogeneous, supporting the idea that concentrated markets with homogeneous products could incentivize tacit collusive behavior, which could distort the functioning of the pension market, thereby affecting the aggregate well-being of workers.     

汇改后人民币汇率波动的非线性特征研究——基于门限自回归TAR模型

文章对自2005年7月人民币汇率制度改革至2008年1月31日的人民币对美元名义汇率波动进行了计量研究,通过建立基于不同时间段汇率数据的门限自回归模型(TAR)可以看到,两年多来的人民币汇率波动存在门限的非线性特征,当升值幅度较大,即大于一定的门限值时,升值的冲击显示出更持久的延续性,体现出了升值预期的作用和升值不断加速的趋势.     

我国棉花短期价格波动研究——基于时间序列

采用ARCH模型对我国国内短期棉花价格波动的影响因素进行了研究。结果显示:棉花流通体制改革和市场宏观调控政策对棉花价格波动分别表现为正向影响和负向影响;棉花当期价格受一期和八期滞后价格影响,这显示出市场主体预期对市场变动趋势具有一定影响;国内持续上涨的需求对棉花市场价格波动的影响相对不显著,而供需缺口的变动是影响国内棉花价格波动的重要因素;棉花进口量的增加有利于减弱国内棉花价格波动;国际市场棉花价格波动对国内价格波动存在显著的正向影响。短期内棉花价格呈现出明显的季节特征,这种季节特征与市场预期、供需变化有较大关联。     

Modelling trigonometric seasonal components for monthly economic time series

The basic structural time series model has been designed for the modelling and forecasting of seasonal economic time series. In this article, we explore a generalization of the basic structural time series model in which the time-varying trigonometric terms associated with different seasonal frequencies have different variances for their disturbances. The contribution of the article is two-fold. The first aim is to investigate the dynamic properties of this frequency-specific Basic Structural Model (BSM). The second aim is to relate the model to a comparable generalized version of the Airline model developed at the US Census Bureau. By adopting a quadratic distance metric based on the restricted reduced form moving-average representation of the models, we conclude that the generalized models have properties that are close to each other compared to their default counterparts. In some settings, the distance between the models is almost zero so that the models can be regarded as observationally equivalent. An extensive empirical study on disaggregated monthly shipment and foreign trade series illustrates the improvements of the frequency-specific extension and investigates the relations between the two classes of models.     

Quantile estimation of frontier production function

The purpose of the paper is to provide new information on the performance of frontier estimation methods, using data from Italian hotel industry. Quantile regression is also suggested as solution to frontier production function estimation. It is shown that, while the choice of estimation methods among conventional techniques significantly affects the economic analysis, quantile regression provides valuable new information by estimating the whole spectrum of production functions corresponding to different efficiency levels. In addition, the method makes available a coherent framework to analyze the performance of the conventional techiniques. Jel classification: C14, C16, D24We would like to thank the Co-Editor, the Associate Editor and an anonymous referee for comments and suggestions. The research was supported by the University Research Council and the National Research Council. The usual disclaimer applies.The estimates were computed using the Roger Koenker and StatLibS-Plus routine of quantile regression and the Tim Coelli and CEPA Web site FRONTIER 4.1 Program. The data set is provided by the Ho.Re.Ca. survey conducted by ISTAT in 1992.First version received: June 2001/Final version received: December 2002     

Gaussian estimation of a second order continuous time macroeconometric model of the UK

This paper describes the first application to an economy-wide macroeconometric model of recently developed methods for the exact Gaussian estimation of higher order continuous time dynamic models. The new model is formulated as a system of second order differential equations, thus providing a much richer dynamic specification than the predominantly first order continuous time macroeconometric models developed during the last 15 years. It also makes intensive use of economic theory to obtain a parsimonious parametrization, is designed in such a way as to permit a rigorous mathematical investigation of its steady state and asymptotic stability properties, and makes systematic use of the assumption of long-run rational expectations. In addition to the exact Gaussian estimates of the structural parameters, the paper includes the first set of continuous lag distributions derived from estimates that take account of the exact restrictions on the distribution of the discrete data implied by a continuous time model. It also includes the first estimates of the coefficient matrices of the exact discrete model, in its VARMAX form, satisfied by the discrete stock and flow data generated by a higher order continuous time dynamic model.     

New tests for stationarity and parity reversion: Evidence on New Zealand real exchange rates

The present paper discusses the stochastic stationarity of New Zealand exchange rates in light of new time series methods and new tests. The question of whether the real exchange rates have a unit root or are mean reverting is set in the general framework of fractionally integrated models. The estimates sustain the claim that New Zealand real exchange series are not stationary. However, it is shown that nonstationarity is compatible with parity reversion in the framework of fractional unit-root models.     

Empirical analysis of corn and soybean basis in Canada

The article examines the factors affecting the basis for corn and soybeans using several time-series techniques to account for potential structural breaks, seasonality, residual serial correlation and structural breaks, as well as potential endogeneity and nonstationarity. The spatio-temporal empirical framework is based on storage and trade theories which assume the relationship between nondelivery location’s spot price and futures price of a storable commodity depends on opportunity cost of capital, warehousing costs, a convenience yield and shipping costs. The interest rate effect is strong for both crops with shipping costs also affecting soybean basis and own inventory levels positively correlated with corn basis. The effect of the wedge between the price of carrying physical grain and the maximum storage rate on basis is positive for both crops. The empirical results, which are robust to multiple estimators, provide stronger evidence of a structural break for the soybean basis than for the corn basis.     

Do large capital inflows hinder competitiveness? The Dutch disease in Ethiopia

This article investigates whether large inflows of foreign aid and remittances have had a damaging impact on the Ethiopian Real Exchange Rate (RER). We improve the current empirical literature by: (i) compiling a unique quarterly dataset to provide a larger sample size and enable the modelling of important intra-year dynamics – which should lead to better model specifications; (ii) providing a new empirical approach (Unobserved Components (UC)) to test the ‘Dutch disease’ hypothesis; and (iii) using several cointegration approaches to further test the robustness of our conclusions. Our results suggest that there are two main long-run determinants of the RER in Ethiopia: trade openness is found to be correlated with RER depreciations, while a positive shock to the terms of trade tends to appreciate the RER. Foreign aid is not found to have a statistically significant impact, while there is only weak evidence that remittances are associated with RER appreciations. The lack of empirical support for the ‘Dutch disease’ hypothesis suggests that Ethiopia has been able to effectively manage large capital inflows, thus avoiding major episodes of macroeconomic instability. We believe that most African countries will therefore be able to absorb large inflows of foreign capital without damaging their external competitiveness.     

Identification problems in Granger causality tests based on the net oil price increase

The net oil price increase is one of the most popular models used to study the relationship between changes in the price of oil and macroeconomic activity. The model postulates that an increase in the price of oil has negative consequences for the economy if the new price exceeds the maximum price observed over a reference period of arbitrary length. The relationship between the net oil price increase and other economic variables is often evaluated with Granger causality tests, the results of which are sensitive to the choice of the reference period. If the reference price is chosen to best fit the data, it becomes an unidentified nuisance parameter under the null hypothesis, causing standard tests to over-reject the null. This article proposes a simple method to obtain correct critical values. Using US data for the period 1954 to 2012, it is found that these corrected critical values reduce, but do not eliminate support for the proposition that the net oil price increase Granger causes real USGDP growth.