Loss aversion in aggregate macroeconomic time series

Prospect theory has been the focus of increasing attention in many fields of economics. However, it has scarcely been addressed in macroeconomic growth models—neither on theoretical nor on empirical grounds. In this paper we use prospect theory in a stochastic optimal growth model. Thereafter, the focus lies on linking the Euler equation obtained from a prospect theory growth model of this kind to real macroeconomic data. We will use generalized method of moments (GMM) estimation to test the implications of such a non-linear prospect utility Euler equation. Our results indicate that loss aversion can be traced in aggregate macroeconomic time series.     

Seasonal fractional components in macroeconomic time series

Seasonal fractional models are shown in this article to be alternative credible ways of modelling the seasonal component in macroeconomic time series. A testing procedure that allows one to test different orders of integration at zero and at each of the seasonal frequencies is described. This procedure is then applied to the Italian consumption and income series, the results being very sensitive to the way of modelling the I(0) disturbances.     

On business cycle fluctuations in USA macroeconomic time series

This study employs eighteen USA macroeconomic time series variables to investigate possible existence of asymmetries in business cycle fluctuations in the series. Detection of asymmetric fluctuations in economic activity is important for policymakers since effective monetary policy relies on asymmetric business cycle fluctuations in all the series. The asymmetric deviations from the long-term growth trend in each of the series are modeled using regime switching models and artificial neural networks. The results based on nonlinear switching time series models reveal strong evidence of business cycle asymmetries in most of the series. The results based on in-sample approximations from artificial neural networks show statistically significant evidence of asymmetries in all the series. Similar results are obtained when jackknife out-of-sample approximations from artificial neural networks are used. Thus, the study results show statistically significant evidence of asymmetries in all the series which indicates that business cycle fluctuations in the series are asymmetric, thus alike. Therefore, the impact of monetary policy shocks on the output and the other macroeconomic variables can be anticipated using nonlinear models only. The results on asymmetric business cycle fluctuations in real GDP are in line with recent studies but in sharp contrast with Balke and Fomby (1994).     

Testing for deterministic and stochastic cycles in macroeconomic time series

In this paper we use a statistical procedure which is appropriate to test for deterministic and stochastic (stationary and nonstationary) cycles in macroeconomic time series. These tests have standard null and local limit distributions and are easy to apply to raw time series. Monte Carlo evidence shows that they perform relatively well in the case of functional misspecification in the cyclical structure of the series. As an example, we use this approach to test for the presence of cycles in US real GDP.      

Evidence for common features in G7 macroeconomic time series

Systems of economic data potentially exhibit a number of common features, which aid both econometric modelling and economic interpretation. This paper surveys a variety of common features and applies the corresponding testing and estimation techniques to systems of macroeconomic time series in the G7 countries. Strong evidence is found of common trends and common or co-dependent cycles in the data, and, for two countries, empirical support for common non-linearities.     

A multivariate time series approach to modelling macroeconomic sequences

In this paper we discuss a multivariate generalization of autoregressive integrated moving average models. A methodology for constructing multivariate time series models is developed and the derivation of forecasts from such models is considered. A bivariate model for Austrian macroeconomic sequences is constructed. Furthermore it is discussed whether multivariate time series methods can be expected to lead to a significant increase in prediction accuracy when forecasting macroeconomic series.     

Seasonality and Markov switching in an unobserved component time series model

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. First Version Received: January 2001/Final Version Received: February 2002 Send offprint requests to: Rob Luginbuhl?Correspondence to: Rob Luginbuhl     

The nature of Austrian macroeconomic time series unit root tests

It is still an open question in economic and econometric modelling whether the non-stationarity in a time series is captured by detrending or by differencing. We test thirrteen Austrian macroenconomic time series for difference versus trend stationarity using informal methods and formal procedures developed by Dickey-Fuller and Phillips-Perron. To eliminate the effects of seasonal adjustment on the tests we apply a third procedure to the unadjusted data, recently developed by Hylleberg-Engle-Granger-Yoo. Independent of the seasonal adjustment the empirical results indicate that these series are integrated of order 1.     

On smoothing macroeconomic time series using the modified HP filter

In business-cycle research, smoothing data is an essential first step to evaluate the extent to which model-generated moments stand up to their empirical counterparts. We put to test McDermott’s (1997) modified version of Hodrick and Prescott’s (1997) smoothing filter. On the one hand, our simulations suggest that relative to other filters, the modified HP-filter replicates better artificially generated series with known properties. On the other hand, using true data we find that autoregressive properties of smoothed series are not affected by the choice of smoothing HP filters, but the same does not hold when it comes to multivariate analysis. The later result is especially strong for annual data. We report results for a large set of countries.     

Forecast content and content horizons for some important macroeconomic time series

Abstract.  The information content of statistical forecasts of approximately stationary quantities tends to decline as the forecast horizon increases, and there exists a maximum horizon beyond which forecasts cannot provide discernibly more information about the variable than is present in the unconditional mean (the content horizon ). The pattern of decay of forecast content (or skill) with increasing horizon is well known for many types of meteorological forecasts; by contrast, little generally accepted information about these patterns or content horizons is available for economic variables. In this paper we estimate content horizons for a variety of macroeconomic quantities; more generally, we characterize the pattern of decay of forecast content as we project farther into the future. We find a wide variety of results for the different macroeconomic quantities, with models for some quantities providing useful content several years into the future, for other quantities providing negligible content beyond one or two months or quarters.     

Business cycle asymmetries and non-linearities in U.K. macroeconomic time series

This paper empirically tests for and models non-linearities in a selection of U.K. macroeconomic time series. Attention is focused first on business cycle asymmetry, using Markov chain models to investigate whether cycles in macroeconomic time series display symmetric behaviour on both sides of a peak or trough. Next, a selection of statistical tests of non-linearity are employed to investigate formally the presence of departures from the linearity assumption. A variety of specific non-linear models of the business cycle that have been proposed recently are then fitted to ascertain how useful they are in explaining any non-linearities that have been observed in the series. Finally, the results are brought together in an extended discussion of their implications for business cycle research and policy analysis.     

Relationships between macroeconomic time series in a fixed-exchange-rate economy: The case of Italy

This paper presents an empirical analysis that is useful for the task of characterizing the dynamic structure and causal orderings of the underlying macroeconomic model of a fixed-exchange-rate economy. Particular emphasis is given to the formulation and testing of versions of the ‘natural-rate’ hypothesis, the ‘small open economy’ hypothesis, and the ‘non-sterilization’ hypothesis. These issues are examined on the basis of quarterly time series for Italy (1956 1970)     

Unit roots and structural breakpoints in China’s macroeconomic and financial time series

This paper applies unit-root tests to 10 Chinese macroeconomic and financial time series that allow for the possibility of up to two endogenous structural breaks. We found that 6 of the series, i.e., GDP, GDP per capita, employment, bank credit, deposit liabilities and investment, can be more accurately characterized as a segmented trend stationarity process around one or two structural breakpoints as opposed to a stochastic unit root process. Our findings have important implications for policy-makers to formulate long-term growth strategy and short-run stabilization policies, as well as causality analysis among the series. __________ Translated from Economic Research Journal (经济研究), 2006, (1) (in Chinese)     

Long run and cyclical strong dependence in macroeconomic time series: Nelson and Plosser revisited

This paper deals with the presence of long range dependence at the long run and the cyclical frequencies in macroeconomic time series. We use a procedure that allows us to test unit roots with fractional orders of integration in raw time series. The tests are applied to an extended version of Nelson and Plosser’s (Nelson CR, Plosser CI (1982) J Monetary Econ 10:139–162) dataset, and the results show that, though the classic unit root hypothesis cannot be rejected in most of the series, fractional degrees of integration at both the zero and the cyclical frequencies are plausible alternatives in some cases. Additionally, the root at the zero frequency seems to be more important than the cyclical one for all series, implying that shocks affecting the long run are more persistent than those affecting the cyclical part. The results are consistent with the empirical fact observed in many macroeconomic series that the long-term evolution is non-stationary, while the cyclical component is stationary.

The order of integration for quarterly macroeconomic time series: A simple testing strategy

An empirical example and a simulation study show that much more attention should be devoted to the practical issue of selecting the maximum admissible order of integration for quarterly macroeconomic time series. In fact, it is shown that when that order is too high, one may get (spurious) evidence for an excessive number of unit roots, resulting in an overdifferenced series. Besides introducing a simple and intuitive definition for the order of integration of quarterly time series, this paper also presents a simple testing strategy to determine that order for the case of macroeconomic data.Helpful comments and suggestions from João Santos Silva and Paulo Rodrigues are gratefully acknowledged. I am also grateful to two anonymous referees, whose comments and suggestions helped improving this paper. Obviously, the usual disclaimer applies. This work has also benefited from financial support from Fundação para a Ciência e Tecnologia (FCT), through Programa POCTI (ECO/33778/2000). A previous version of this paper was presented at the Royal Economic Society Conference, March 2002, Warwick.     

Financial development shocks and contemporaneous feedback effect on key macroeconomic indicators: A post Keynesian time series analysis

In this paper we take into account the role of the banking system, credit and stock market in stimulating aggregate demand in post Keynesian tradition. According to the results of impulse response analysis; it appears all three financial development indicators contributed as expected in improving macroeconomic performance of South Korean economy. Stock market capitalisation and domestic credit availability are strongly responsible for stimulation of investment, saving and productivity Growth in Hong Kong. The UK financial system seems vulnerable to future shocks, whether by shocks in the credit markets or stock markets.     

Normalization in cointegrated time series systems

Abstract .  A method for normalizing cointegrating vectors is proposed for cointegrated time series systems containing multiple cointegrating vectors, a method requiring that an identity matrix appear in the normalized cointegrating matrix with unit coefficients attached to the endogenous or choice variables. The preferred method causes the normalized cointegrating matrix and the adjustment matrix to be consistent with the implications of static and dynamic economic theory. Alternative normalizations generate cointegrating and adjustment matrices that do not match up well with economic theory and do not reveal the testable restrictions implied by static economic theory.     

Analysis on structural changes in the macroeconomic data series with the empirical evidence from China

Analysis on structural changes in macroeconomic data series has been the key issue for studying data quality. This paper studies the structural changes in China’s 36 macroeconomic time series using joint estimation model, and we find out the characteristics and movement pattern for the outliers. Our results show that most outliers show up more or less in groups, indicating that there is a significant correlation between them. The isolated outliers are not the main characteristic of China’s macroeconomic time series. Nearly all the original series contain the obvious skewness and kurtosis; hence, the hypothesis of normality is significantly rejected. Most original and outlier correction series show the non-autoregressive conditional heteroskedasticity (ARCH) characteristic, but the p value for ARCH2, ARCH4, and ARCH8 is very different. __________ Translated from Economic Research Journal (经济研究), 2005, (1) (in Chinese)     

Multiple cyclical fractional structures in financial time series

This article analyses multiple cyclical structures in financial time series. In particular, we focus on the monthly structure of the Nasdaq, the Dow–Jones and the S&P stock market indices. The three series are modelled as long-memory processes with poles in the spectrum at multiple frequencies, including the long-run or zero frequency.     

Model selection for nonlinear time series

We investigate the finite-sample performance of model selection criteria for local linear regression by simulation. Similarly to linear regression, the penalization term depends on the number of parameters of the model. In the context of nonparametric regression, we use a suitable quantity to account for the Equivalent Number of Parameters as previously suggested in the literature. We consider the following criteria: Rice T, FPE, AIC, Corrected AIC and GCV. To make results comparable with other data-driven selection criteria we consider also Leave-Out CV. We show that the properties of the penalization schemes are very different for some linear and nonlinear models. Finally, we set up a goodness-of-fit test for linearity based on bootstrap methods. The test has correct size and very high power against the alternatives investigated. Application of the methods proposed to macroeconomic and financial time series shows that there is evidence of nonlinearity.First version received: September 2002/Final version received : October 2003I would like to thank Cees Diks, Cars Hommes and an anonymous referee for useful comments that significantly improved the paper.