The Federal Reserve Bank of Cleveland provides daily “nowcasts” of inflation for two popular price indexes, the price index for personal consumption expenditures (PCE) and the Consumer Price Index (CPI). These nowcasts give a sense of where inflation is today. Released each business day.

There has been a resurgence of interest in dynamic factor models for use by policy advisors. Dynamic factor methods can be used to incorporate a wide range of economic information when forecasting or measuring economic shocks. This article introduces dynamic factor models that underlie the data-rich methods and also tests whether the data-rich models can help a benchmark autoregressive model forecast alternative measures of inflation and real economic activity at horizons of 3, 12, and 24 months ahead. The authors find that, over the past decade, the data-rich models significantly improve the forecasts for a variety of real output and inflation indicators. For all the series that they examine, the authors find that the data-rich models become more useful when forecasting over longer horizons. The exception is the unemployment rate, where the principal components provide significant forecasting information at all horizons.

The Consumer Price Index gauges the price changes in a basket of goods and services representative of Mexican households' consumption. As of August 2024, the CPI had increased **** percentage points compared to the same month of the previous year. Despite some fluctuations, the monthly inflation rate in the country has been experiencing an overall downward trend since August 2022. Different forms of measuring inflation The National Institute of Statistics, Geography, and Informatics (INEGI) measures price variations considering a total of 299 goods and services that encompass the most representative goods in rural and urban areas of the country. From the second half of June 2018 to May 2024, the accumulated CPI was around ****** points, representing price increases of over ** percent in almost six years. Nonetheless, not all categories of goods and services increased at the same rate, as of June 2024, food and non-alcoholic beverages recorded the highest CPI with *** points, followed by restaurants and hotels. Consumer’s perception Consumers in Mexico had experienced rising prices differently, for example, people older than 55 years old had a higher perceived level of inflation in groceries than any other age group. Groceries were the second category with the highest perceived inflation, only behind restaurants, with almost ** percent of Mexicans reporting high increases. As well as different perceptions, consumers decide to take varying alternatives to cope with the increases, the most common were paying more attention to prices, changing brands of certain products, or reducing consumption.

We explain how measures of consumer prices are computed and what the differences are between the consumer price index (CPI) and the personal consumption expenditures (PCE) price index. We also explain various measures used to gauge underlying inflation, or the long-term trend in prices, such as median and trimmed-mean inflation rates and core inflation.

Many problems plague empirical Phillips curves (PCs). Among them is the hurdle that the two key components, inflation expectations and the output gap, are both unobserved. Traditional remedies include proxying for the absentees or extracting them via assumptions-heavy filtering procedures. I propose an alternative route: a Hemisphere Neural Network (HNN) whose architecture yields a final layer where components can be interpreted as latent states within a Neural PC. First, HNN conducts the supervised estimation of nonlinearities that arise when translating a high-dimensional set of observed regressors into latent states. Second, forecasts are economically interpretable. Among other findings, the contribution of real activity to inflation appears understated in traditional PCs. In contrast, HNN captures the 2021 upswing in inflation and attributes it to a large positive output gap starting from late 2020. The unique path of HNN’s gap comes from dispensing with unemployment and GDP in favor of an amalgam of nonlinearly processed alternative tightness indicators.

Norway NO: GDP: USD: Gross National Income: Atlas Method data was reported at 401.390 USD bn in 2017. This records a decrease from the previous number of 429.276 USD bn for 2016. Norway NO: GDP: USD: Gross National Income: Atlas Method data is updated yearly, averaging 109.233 USD bn from Dec 1962 (Median) to 2017, with 56 observations. The data reached an all-time high of 537.021 USD bn in 2014 and a record low of 5.841 USD bn in 1962. Norway NO: GDP: USD: Gross National Income: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Gross Domestic Product: Nominal. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Gap-filled total;

The model estimated in this document uses a set of variables that are available for a wide range of countries with different levels of development, resulting in a sample of 91 countries for the period 1970-2010. The file titled “Database PLS-PM” contains the data with which is possible to estimate the human capital index (ich) calculated in the paper. The variables used and their notation is as follows: FR= Fertility Rates VAAS = value-added contributed by the agricultural sector to GDP GNI = Gross National Incomes per capita LE = Life Expectancy MR = Mortality rate for children under five years AYE = Average Years of Education SPR = Student-Professor Ratio EC = Energy Consumption per capita PP = patent applications by residents per capita Given the database is not complete for all countries or for all years, this missing data was complete through interpolation method. All variables were transformed by mean of logarithms, except GNI. In the case of EC and PP, block of returns on human capital, the manifest variables are transformed such that they may be retrieved in levels at a later stage. 2. Data to estimate the economic growth regressions Cross-section: The file titled “Database – Cross-Section” contains the data with which it is possible to estimate the results shown in tables 1-5 of the manuscript. The variables used and their notation is the following: grow = GDP per capita, rate of change log(gdp75) = lag of GDP in 1975, logarithm demo = a binary variable measuring the level of democracy in the countries contes = indicators by principal component analysis to approximate the degree of contestation inclu = indicators by principal component analysis to approximate the degree of inclusiveness lnihc = human capital index estimated through PLS-PM, logarithm lnaye = average years of education developed by Barro and Lee (2013), logarithm lninves = investment in physical capital, measured as the average share of investment real to GDP, logarithm lngov = average government consumption as a percentage of GDP, logarithm lninfla = inflation measured by consumer prices, logarithm lnpop = population growth rate, logarithm lnich70, lnich75, lnape70, lnape75 lninves70 lninves75 lnpop70 lnpop75 = lags of lnich, lnaye, lninves and lnpop dafri = dummy for African countries Panel data: The file titled “Database – Panel data” contains the data with which it is possible to estimate the results shown in tables 6-9 of the manuscript. All variables are averages for the underlying period. The variables used and their notation is the following: grow = GDP per capita, rate of change lngdp75 = initial GDP in 1975, logarithm demo = a binary variable measuring the level of democracy in the countries contes = indicators by principal component analysis to approximate the degree of contestation inclu = indicators by principal component analysis to approximate the degree of inclusiveness lnihc = human capital index estimated through PLS-PM, logarithm lnaye = average years of education developed by Barro and Lee (2013), logarithm lninves = investment in physical capital, measured as the average share of investment real to GDP, logarithm lngov = average government consumption as a percentage of GDP, logarithm lninfla = inflation measured by consumer prices, logarithm lnpop = population growth rate, logarithm dafri = dummy for African countries

Key macro indicators such as output, productivity, and inflation are based on a complex system across multiple statistical agencies using different samples and levels of aggregation. The Census Bureau collects nominal sales, the Bureau of Labor Statistics collects prices, and the Bureau of Economic Analysis constructs nominal and real GDP using these data and other sources. The price and quantity data are integrated at a high level of aggregation. This paper explores alternative methods for reengineering key national output and price indices using item-level data. Such reengineering offers the promise of greatly improved key economic indicators along many dimensions.

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