Consumer Price Index-July 2021
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Inflation and the Business Cycle
Inflation and the business cycle Michael McMahon Money and Banking (5): Inflation & Bus. Cycle 1 / 68 To Cover • Discuss the costs of inflation; • Investigate the relationship between money and inflation; • Introduce the Romer framework; • Discuss hyperinflations. • Shocks and the business cycle; • Monetary policy responses to business cycles. • Explain what the monetary transmission mechanism is; • Examine the link between inflation and GDP. Money and Banking (5): Inflation & Bus. Cycle 2 / 68 The Next Few Lectures Term structure, asset prices Exchange and capital rate market conditions Import prices Bank rate Net external demand CPI inflation Bank lending Monetary rates and credit Policy Asset purchase/ Corporate DGI conditions Framework sales demand loans Macro prudential Household policy demand deposits Inflation expectations Money and Banking (5): Inflation & Bus. Cycle 3 / 68 Inflation Definition Inflation is a sustained general rise in the price level in the economy. In reality we measure it using concepts such as: • Consumer Price Indices (CPI); • Producer Price Indices (PPI); • Deflators (GDP deflator, Consumption Expenditure Deflator) Money and Banking (5): Inflation & Bus. Cycle 4 / 68 Inflation: The Costs If all prices are rising at same rate, including wages and asset prices, what is the problem? • Information: Makes it harder to detect relative price changes and so hinders efficient operation of market; • Uncertainty: High inflation countries have very volatile inflation; • High inflation undermines role of money and encourages barter; • Growth - if inflation increases by 10%, reduce long term growth by 0.2% but only for countries with inflation higher than 15% (Barro); • Shoe leather costs/menu costs; • Interaction with tax system; • Because of fixed nominal contracts arbitrarily redistributes wealth; • Nominal contracts break down and long-term contracts avoided. -
An Introduction to Psychometric Theory with Applications in R
What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD An introduction to Psychometric Theory with applications in R William Revelle Department of Psychology Northwestern University Evanston, Illinois USA February, 2013 1 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Overview 1 Overview Psychometrics and R What is Psychometrics What is R 2 Part I: an introduction to R What is R A brief example Basic steps and graphics 3 Day 1: Theory of Data, Issues in Scaling 4 Day 2: More than you ever wanted to know about correlation 5 Day 3: Dimension reduction through factor analysis, principal components analyze and cluster analysis 6 Day 4: Classical Test Theory and Item Response Theory 7 Day 5: Structural Equation Modeling and applied scale construction 2 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Outline of Day 1/part 1 1 What is psychometrics? Conceptual overview Theory: the organization of Observed and Latent variables A latent variable approach to measurement Data and scaling Structural Equation Models 2 What is R? Where did it come from, why use it? Installing R on your computer and adding packages Installing and using packages Implementations of R Basic R capabilities: Calculation, Statistical tables, Graphics Data sets 3 Basic statistics and graphics 4 steps: read, explore, test, graph Basic descriptive and inferential statistics 4 TOD 3 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD What is psychometrics? In physical science a first essential step in the direction of learning any subject is to find principles of numerical reckoning and methods for practicably measuring some quality connected with it. -
Methodological Notes for OECD CPI News Release
OECD Consumer Price Index Last update: 2 June 2021 Methodological Notes for OECD CPI News Release Consumer price indices (CPIs) measure inflation as price changes of a representative basket of goods and services typically purchased by households. In most instances, national CPIs are compiled in accordance with international statistical guidelines and recommendations. However, national practices may differ in the coverage and treatment of certain items and in the use of index number formulas. In particular, country methodologies for the treatment of owner-occupied housing vary significantly and, where included, carry large weights in the index. The European Harmonised Indices of Consumer Prices (HICP) is based on a harmonised approach and a single set of definitions in order to arrive at comparable measures of inflation in the European Union (EU); owner-occupied housing is excluded from the scope of the HICP as do national CPIs for Belgium, Estonia, France, Greece, Italy, Luxembourg, Poland, Portugal, Spain and the United Kingdom. HICPs are therefore shown for comparison under the all items column in the first table. For the United Kingdom the national CPI is the same as the HICP. The three CPI aggregates for zones (OECD Total, G7 and G-20), calculated by the OECD, are annual chain-linked Laspeyres-type indices. The weights for each country in each link are based on the previous year’s relative share of individual final consumption expenditure of households and non-profit institutions serving households expressed in Purchasing Power Parities (PPPs). The CPI aggregates for OECD total and G7 area are calculated for four variables that are available for all OECD countries: CPI-All items; CPI-Food excluding restaurant meals (COICOP 01); CPI-Energy [Electricity, gas and other fuel (COICOP 04.5) plus Fuel and lubricants for personal transport equipment (COICOP 07.2.2)]; CPI-All items less Food less Energy (food and energy as defined above). -
What's Driving Food Prices in 2011?
What’s Driving Food Prices in 2011? .YP] Preface ree years ago Farm Foundation, NFP asked Purdue University economists Wallace Tyner, Philip Abbot and Christopher Hurt to review the literature and provide a comprehensive assessment of the major factors driving commodity and food prices. Our objective was to cool the heated rhetoric in the food-versus-fuel debate, which had been rising right along with the prices of corn and oil. Today, although the rhetoric seems to be lower, prices of oil and commodities, including corn, soybeans and wheat, have again approached the levels of 2008. In addition, policy makers today are focused on a national debate over fiscal policy—a debate that encompasses the future direction of food, agricultural and energy policies. Farm Foundation commissioned the current paper to provide a comprehensive, objective assessment of the forces driving commodity and food prices. A major question was whether the forces at work today are the same as or different from those of 2008. Our goal is to provide public and private decision makers with an objective assessment of the forces driving commodity prices, as well as offer some insights into the impacts on policy options. Now, as in 2008, the full story behind rapid increases in commodity prices is not a simple one. Many of the factors driving prices remain the same but play out in different ways—including biofuel demands, exchange rates and weather. As concluded in 2008, one simple fact stands out: economic growth and rising human aspirations are putting ever greater pressure on the global resource base. -
Consumerism and Environmental Policy: Moving Past Consumer Culture
Consumerism and Environmental Policy: Moving Past Consumer Culture Bradley A. Harsch* CONTENTS Introduction ......................................................................... 544 I. Environmental Problems and Ways of Dealing With Th em ............................................................................ 548 A. Industrial Economy and the Environment ............... 548 B. Conventional Approaches to Addressing Environmental Problems ......................................... 550 C. Proposed Approaches to Addressing Environmental Problem s ................................................................ 552 1. Market-Based Approaches: Internalizing Externalities ...................................................... 553 2. Reducing Energy and Raw Material Input .......... 554 3. Proposed Approaches that Address Consum ption ..................................................... 554 II. Consum er Culture ........................................................ 555 A. The Historical Development and Critique of the Consum er Culture .................................................. 557 B. Definitive Aspects of Consumer Culture .................. 559 1. The Reification of Images .................................. 559 2. The Market as the Primary Means of Satisfying D esires .............................................................. 562 C. Advertising and Consumer Culture ......................... 566 1. Advertising and its Place in Society .................... 566 2. Our Incredulity ................................................. -
Cluster Analysis for Gene Expression Data: a Survey
Cluster Analysis for Gene Expression Data: A Survey Daxin Jiang Chun Tang Aidong Zhang Department of Computer Science and Engineering State University of New York at Buffalo Email: djiang3, chuntang, azhang @cse.buffalo.edu Abstract DNA microarray technology has now made it possible to simultaneously monitor the expres- sion levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremen- dous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expres- sion data, and also new algorithms have recently been proposed specifically aiming at gene ex- pression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. -
HEAT WAVES Albany, New York Since 1874 Periods of 3 Or More Consecutive Days with Maximum Temperatures of 90 Degrees Or More
HEAT WAVES Albany, New York Since 1874 Periods of 3 or more consecutive days with maximum temperatures of 90 degrees or more. 10 days August 9-11, 1893 June 26-28, 1953 Aug 27-Sep 5, 1953 4 days June 16-18, 1894 June 20-22, 1954 July 25-28, 1882 9 days July 30-August 2, 1887 August 4-12, 1896 May 30-June 2, 1895 July 18-20, 1894 July 8-10, 1955 August 18-21, 1899 July 27-29, 1894 July 21-23, 1955 8 days July 9-12, 1911 September 21-23, 1895 August 20-22, 1955 June 26-July 3, 1901 July 30-August 2, 1917 July 28-30, 1896 June 13-15, 1956 July 3-10, 1912 July 26-29, 1918 July 5-7, 1897 July 19-21, 1957 July 31-August 7, 1955 July 25-28, 1921 June 5-7, 1899 July 22-24, 1959 July 12-15, 1927 August 9-11, 1900 July 28-30, 1959 7 days July 29-August 1, 1933 August 25-27, 1900 May 18-20, 1962 July 14-16, 1901 June 30-July 2, 1963 August 17-23, 1916 June 28-July 1, 1947 July 17-19, 1905 July 17-19, 1963 July 5-11, 1988 August 8-11, 1949 July 5-7, 1908 July 17-19, 1964 July 17-23, 1991 July 12-15, 1952 August 16-18, 1913 July 21-23, 1964 July 16-19, 1953 August 8-10, 1914 July 27-29, 1964 6 days June 15-18, 1957 August 14-17, 1959 September 21-23, 1914 August 14-16, 1970 June 29-July 4, 1887 September 1-4, 1961 July 21-23, 1918 July 7-9, 1973 July 24-29, 1892 June 25-28, 1963 August 12-14, 1918 July 13-15, 1979 June 3-8, 1925 July 10-13, 1966 June 3-5, 1919 July 2-4, 1983 July 7-12, 1936 July 27-30, 1970 June 21-23, 1921 June 13-15, 1988 July 7-12, 1944 August 28-31, 1973 June 19-21, 1923 July 25-27, 1989 July 24-29, 1963 September 2-5, 1973 July -
AVAILABLE from a Price Index for Deflation of Academic R&D
DOCUMENT RESUME ED 067 986 HE 003 406 TITLE A Price Index for Deflation of Academic R&D Expenditures. INSTITUTION National Science Foundation, Washington, D.C. REPORT NO NSF-72-310 PUB DATE 72 NOTE 38p. AVAILABLE FROMSuperintendent of Documents, U.S. Government Printing- Office, Washington, D.C. 20402 ($.25, 3800-00122) EDRS PRICE MF-$0.65 HC-$3.29 DESCRIPTORS Costs; Educational Finance; *Educational Research; Financial Problems; *Financial Support; *Higher Education; Research; Research and Development Centers; *Scientific Research; *Statistical Data ABSTRACT This study relates to price trends affecting research and development (R&D) activities at academic institutions. Part I of this report provides the overall results of the study with limited discussion of measurement concepts, methodology and limitations. Part II deals with price indexes and deflation-general concepts and methodology. Part III discusses methodology and data base and Part IV describes alternative computations and approaches. Statistical tables and charts are included.(Author/CS) cO Cr` CD :w U S DEPARTMENT OF HEALTH EDUCATION & WELFARE OFFICE OF EDUCATION THIS DOCUMENT HASBEEN REPRO DUCED EXACTLY ASRECEIVED FROM THE PERSON OR ORGANIZATION ORIG INATING IT POINTS OFVIEW OR OPIN IONS STATED DONOT NECESSARILY REPRESENT OFFICIALOFFICE OF EDU CATION POSITION OR POLICY RELATED PUBLICATIONS Title Number Price National Patterns of R&D Resources: Funds and Manpower in the United States, 1953-72 72-300 $0.50 Resources for Scientific Activities at Universi- ties and Colleges, 1971 72-315 In press Availability of Publications Those publications marked with a price should be obtained directly from the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. -
February 2021 Historical Winter Storm Event South-Central Texas
Austin/San Antonio Weather Forecast Office WEATHER EVENT SUMMARY February 2021 Historical Winter Storm Event South-Central Texas 10-18 February 2021 A Snow-Covered Texas. GeoColor satellite image from the morning of 15 February, 2021. February 2021 South Central Texas Historical Winter Storm Event South-Central Texas Winter Storm Event February 10-18, 2021 Event Summary Overview An unprecedented and historical eight-day period of winter weather occurred between 10 February and 18 February across South-Central Texas. The first push of arctic air arrived in the area on 10 February, with the cold air dropping temperatures into the 20s and 30s across most of the area. The first of several frozen precipitation events occurred on the morning of 11 February where up to 0.75 inches of freezing rain accumulated on surfaces in Llano and Burnet Counties and 0.25-0.50 inches of freezing rain accumulated across the Austin metropolitan area with lesser amounts in portions of the Hill Country and New Braunfels area. For several days, the cold air mass remained in place across South-Central Texas, but a much colder air mass remained stationary across the Northern Plains. This record-breaking arctic air was able to finally move south into the region late on 14 February and into 15 February as a strong upper level low-pressure system moved through the Southern Plains. As this system moved through the region, snow began to fall and temperatures quickly fell into the single digits and teens. Most areas of South-Central Texas picked up at least an inch of snow with the highest amounts seen from Del Rio and Eagle Pass extending to the northeast into the Austin and San Antonio areas. -
Food Price Variation
2019 Food Price Variation The USDA estimates that 40 million people, including more than 12 million children, in the United States are food insecure as of 2017. This means that 1 in 8 individuals (13%) and 1 in 6 children (17%) live in households without 40 million consistent access to adequate food. The experience of food insecurity -or- depends on individual circumstances, local economies, and broader social and economic forces. Food prices in particular represent an important component of cost-of-living that affects households’ ability to afford food. 1/8 In fact, the high relative price of food contributed to the persistently high people are food insecure. rate of national food insecurity in the years following the Great Recession.1 Food prices began edging upward again in 2017 and 2018, and are expected to continue rising in 2019.2 For the ninth consecutive year, Feeding America has conducted the Map the 1,352 Meal Gap study to improve our understanding of how food insecurity and counties (43%) have food costs vary at the local level. This brief focuses on the variation in local meal costs higher than food prices and the average cost of a meal, one of four related topics that the national average. make up the Map the Meal Gap 2019 report briefs. Through the lens of local meal costs, it is possible to see how people already struggling with hunger in communities across the country can find it difficult to afford enough food to live active, healthy lives. $3.02 is the national average meal cost. -
Does Google Search Index Help Track and Predict Inflation Rate? an Exploratory Analysis for India
Does Google Search Index Help Track and Predict Inflation Rate? An Exploratory Analysis for India By G. P. Samanta1 Abstract: The forward looking outlook or market expectations on inflation constitute valuable input to monetary policy, particularly in the ‘inflation targeting' regime. However, prediction or quantification of market expectations is a challenging task. The time lag in the publication of official statistics further aggravates the complexity of the issue. One way of dealing with non-availability of relevant data in real- time basis involves assessing the current or nowcasting the inflation based on a suitable model using past or present data on related variables. The forecast may be generated by extrapolating the model. Any error in the assessment of the current inflationary pressure thus may lead to erroneous forecasts if the latter is conditional upon the former. Market expectations may also be quantified by conducting suitable surveys. However, surveys are associated with substantial cost and resource implications, in addition to facing certain conceptual and operational challenges in terms of representativeness of the sample, estimation techniques, and so on. As a potential alternative to address this issue, recent literature is examining if the information content of the vast Google trend data generated through the volume of searches people make on the keyword ‘inflation' or a suitable combination of keywords. The empirical literature on the issue is mostly exploratory in nature and has reported a few promising results. Inspired by this line of works, we have examined if the search volume on the keywords ‘inflation’ or ‘price’in the Google search engine is useful to track and predict inflation rate in India. -
The Evolution of Inflation and Unemployment: Explaining the Roaring Nineties
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Karanassou, Marika; Sala, Héctor; Snower, Dennis J. Working Paper The evolution of inflation and unemployment: Explaining the roaring nineties IZA Discussion Papers, No. 2900 Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Karanassou, Marika; Sala, Héctor; Snower, Dennis J. (2007) : The evolution of inflation and unemployment: Explaining the roaring nineties, IZA Discussion Papers, No. 2900, Institute for the Study of Labor (IZA), Bonn This Version is available at: http://hdl.handle.net/10419/4105 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu IZA DP No. 2900 The Evolution of Inflation and Unemployment: Explaining the Roaring Nineties Marika Karanassou Hector Sala Dennis J.