Inflation in Czechoslovakia, 1985-91

The authors assess inflation in Czechoslovakia between 1985 and 1991 and identify the main causes of inflation through a literature survey and empirical studies. The official prices in centrally planned economies were never perceived by central planners to be fully market clearing. Only by coincidence would the overall price level correspond to the level associated with general equilibrium. What is missing in official price indices in centrally planned economies - including the consumer price index - is suppressed inflation, manifast in queuing for products, forced substitution of demand, and forced savings. Also missing is hidden inflation, associated with practices that disguise price increases behind cosmetic or other change in product quality. The authors argue that inflationary pressures in Czechoslovakia in 1985-89 originated mainly in the investment sector. Even though the investment sector was strictly controlled, making it difficult for open inflation to emerge, the scope for inflationary pressures was great in Czechoslovakia. Such pressures arose from a mixture of factors, including poor investment planning, accommodating government finance, and the high priority given to investments and social consumption. For Czechoslovakia, the official price indices show virtually no inflation between 1985 and 1989, when there were long waiting lists for such products as cars and state and cooperative flats. Trends in these price indices do not seem to depend on the method used for constructing them, according to the sensitivity tests conducted by Czechoslovakia's Federal Statistical Office. Obviously, the official price indices failed to capture the full extent of economic disequilibrium in that period. But the extent to which official price indices understated inflationary pressures was not serious in Czechoslovakia, compared with other centrally planned economies. Estimates of hidden inflation for 1985-89 range from 0.5 percent to 2 percent a year in consumer markets and about 3 percent in the industrial sector. Estimates for suppressed inflation were less than 5 percent. The relatively small inflationary gap is indirectly confirmed by the sharp inflation associated with the recent price liberalization that subsided in a relatively short period, and both suppressed and hidden inflations have virtually disappeared. Estimates of hidden inflation were based on benchmark price comparisons between Czechoslovakia and such market economies as Austria. Those for suppressed inflation were based on disequilibrium econometric models of asset holdings and on conjecture tests.Economic Theory&Research,Environmental Economics&Policies,Markets and Market Access,Access to Markets,Financial Intermediation

Clustering Time Series from Mixture Polynomial Models with Discretised Data

Clustering time series is an active research area with applications in many fields. One common feature of time series is the likely presence of outliers. These uncharacteristic data can significantly effect the quality of clusters formed. This paper evaluates a method of over-coming the detrimental effects of outliers. We describe some of the alternative approaches to clustering time series, then specify a particular class of model for experimentation with k-means clustering and a correlation based distance metric. For data derived from this class of model we demonstrate that discretising the data into a binary series of above and below the median improves the clustering when the data has outliers. More specifically, we show that firstly discretisation does not significantly effect the accuracy of the clusters when there are no outliers and secondly it significantly increases the accuracy in the presence of outliers, even when the probability of outlier is very low

Editorial

Brain size is widely used as a measure of behavioural complexity and sensory-locomotive capacity in avians but has largely relied upon laborious dissections, endoneurocranial tissue displacement, and physical measurement to derive comparative volumes. As an alternative, we present a new precise calculation method based upon coupled magnetic resonance (MR) imaging and computed tomography (CT). Our approach utilizes a novel interactive Fakir probe cross-referenced with an automated CT protocol to efficiently generate total volumes and surface areas of the brain tissue and endoneurocranial space, as well as the discrete cephalic compartments. We also complemented our procedures by using sodium polytungstate (SPT) as a contrast agent. This greatly enhanced CT applications but did not degrade MR quality and is therefore practical for virtual brain tissue reconstructions employing multiple imaging modalities. To demonstrate our technique, we visualized sex-based brain size differentiation in a sample set of Ring-necked pheasants (Phasianus colchicus). This revealed no significant variance in relative volume or surface areas of the primary brain regions. Rather, a trend towards isometric enlargement of the total brain and endoneurocranial space was evidenced in males versus females, thus advocating a non-differential sexually dimorphic pattern of brain size increase amongst these facultatively flying birds

Real-time extraction of the Madden-Julian oscillation using empirical mode decomposition and statistical forecasting with a VARMA model

A simple guide to the new technique of empirical mode decomposition (EMD) in a meteorological-climate forecasting context is presented. A single application of EMD to a time series essentially acts as a local high-pass filter. Hence, successive applications can be used to produce a bandpass filter that is highly efficient at extracting a broadband signal such as the Madden-Julian Oscillation (MJO). The basic EMD method is adapted to minimize end effects, such that it is suitable for use in real time. The EMD process is then used to efficiently extract the MJO signal from gridded time series of outgoing longwave radiation (OLR) data. A range of statistical models from the general class of vector autoregressive moving average (VARMA) models was then tested for their suitability in forecasting the MJO signal, as isolated by the EMD. A VARMA (5, 1) model was selected and its parameters determined by a maximum likelihood method using 17 yr of OLR data from 1980 to 1996. Forecasts were then made on the remaining independent data from 1998 to 2004. These were made in real time, as only data up to the date the forecast was made were used. The median skill of forecasts was accurate (defined as an anomaly correlation above 0.6) at lead times up to 25 days

What Really Matters: The Effect of Covid-19 on the Factors of Life Satisfaction

This article investigates the effect of the Covid-19 pandemic on the structure of factors of life satisfaction in the city of Usti nad Labem, Czech Republic. The dataset is based on a questionnaire survey conducted in Usti nad Labem, Czech Republic. Subsequent data analysis is conducted using ordinal logistic regression models. The results show that the emergence of the pandemic had a significant impact on life satisfaction factors. Firstly, the importance of family came to the fore: being in a relationship or being married proved to be a significant factor of life satisfaction during the pandemic but not before the pandemic. Secondly, a negative association between drinking alcohol and life satisfaction emerged during the pandemic. Alcohol probably started to be the tool for stress relief. Thirdly, sport became a significant positive factor of women’s life satisfaction. Sport most likely became an effective way of keeping oneself in balance. Results indicate that during a difficult time period there are different ways of how to deal with it. In this way doing sports and drinking alcohol seem to be substitute activities

Goodness of fit of prediction models and two step prediction

Given a second order stationary time series it can be shown that there exists an optimum linear predictor of Xk, say X*k which is constructed from {Xt ,t=O,-l,-2 …} the mean square error of prediction being given by ek = E [|Xk- X*k|2]. In some cases however a series can be considered to have started at a point in the past and an attempt is made to see how well the optimum linear form of the predictor behaves in this case. Using the fundamental result due to Kolmogorov relating the prediction error e1 to the power spectrum f(w) e1 = exp. {1/2 pi Log from – pi to p log 2 pi f(w) dw} estimates of e1 are constructed using the estimated periodogram and power spectrum estimates. As is argued in some detail the quantity e1 is a natural one to look at when considering prediction and estimation problems and the estimates obtained are non-parametric. The characteristic functions of these estimates are obtained and it is shown that asymptotically they have distributions which are approximately normal. The rate of convergence to normality is also investigated. A previous author has used a similar estimate as the basis of a test of white noise and the published results are extended and in the light of the simulation results obtained some modifications are suggested. To increase the value of the estimates e1 their small sample distribution is approximated and extensive tables of percentage points are provided. Using these approximations one can construct a more powerful and versatile test for white noise and simulation results confirm that the theoretical results work well. The same approximation technique is used to derive the small sample distribution of some new estimates of the coefficients in the model generating {Xt}. These estimates are also based on the power spectrum. While it is shown small sample theory is limited in this situation the asymptotic results are very interesting and useful. Several suggestions are made as to further fields of investigation in both the univariate and multivariate cases

Goodness of fit of prediction models and two step prediction

Given a second order stationary time series it can be shown that there exists an optimum linear predictor of Xk, say X*k which is constructed from {Xt ,t=O,-l,-2 …} the mean square error of prediction being given by ek = E [|Xk- X*k|2]. In some cases however a series can be considered to have started at a point in the past and an attempt is made to see how well the optimum linear form of the predictor behaves in this case. Using the fundamental result due to Kolmogorov relating the prediction error e1 to the power spectrum f(w) e1 = exp. {1/2 pi Log from – pi to p log 2 pi f(w) dw} estimates of e1 are constructed using the estimated periodogram and power spectrum estimates. As is argued in some detail the quantity e1 is a natural one to look at when considering prediction and estimation problems and the estimates obtained are non-parametric. The characteristic functions of these estimates are obtained and it is shown that asymptotically they have distributions which are approximately normal. The rate of convergence to normality is also investigated. A previous author has used a similar estimate as the basis of a test of white noise and the published results are extended and in the light of the simulation results obtained some modifications are suggested. To increase the value of the estimates e1 their small sample distribution is approximated and extensive tables of percentage points are provided. Using these approximations one can construct a more powerful and versatile test for white noise and simulation results confirm that the theoretical results work well. The same approximation technique is used to derive the small sample distribution of some new estimates of the coefficients in the model generating {Xt}. These estimates are also based on the power spectrum. While it is shown small sample theory is limited in this situation the asymptotic results are very interesting and useful. Several suggestions are made as to further fields of investigation in both the univariate and multivariate cases